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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Suggested Citation:"Appendix A: Contributed Manuscripts." Institute of Medicine. 2014. The Influence of Global Environmental Change on Infectious Disease Dynamics: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/18800.
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Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Appendix A Contributed Manuscripts A1 ANIMAL MIGRATION AND INFECTIOUS DISEASE RISK1 Sonia Altizer,2 Rebecca Bartel,2 and Barbara A. Han2 Abstract Animal migrations are often spectacular, and migratory species har- bor zoonotic pathogens of importance to humans. Animal migrations are expected to enhance the global spread of pathogens and facilitate cross- species transmission. This does happen, but new research has also shown that migration allows hosts to escape from infected habitats, reduces disease levels when infected animals do not migrate successfully, and may lead to the evolution of less-virulent pathogens. Migratory demands can also reduce immune function, with consequences for host susceptibility and mortality. Studies of pathogen dynamics in migratory species and how these will re- spond to global change are urgently needed to predict future disease risks for wildlife and humans alike. 1  Originally printed as Altizer et al. 2011. Animal migration and infectious disease risk. Science 331(6015):296-302. Reprinted with permission from AAAS. 2  Odum School of Ecology, University of Georgia, Athens, GA 30602, USA. 111

112 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS Billions of animals from groups as diverse as mammals, birds, fish, and insects undertake regular long-distance movements each year to track seasonal changes in resources and habitats (Dingle, 1996). The most dramatic migrations, such as those by monarch butterflies (Figure A1-1), gray whales, and some shore- birds and dragonflies (Figure A1-2), span entire continents or hemispheres, can take several months to complete, and are accompanied by high energetic demands and extreme physiological changes. The ultimate cause of these seasonal migra- tions remains debated; most hypotheses focus on avoidance of food scarcity, seeking physiologically optimal climates, and avoiding predation during periods FIGURE A1-1  Monarch butterflies (Danaus plexippus), shown here at a wintering site in central Mexico, undertake one of the longest distance two-way migrations of any insect species worldwide. Monarchs are commonly infected by a debilitating protozoan parasite that can lower the flight ability of migrating butterflies.

APPENDIX A 113 FIGURE A1-2 Representative migratory species, including migration distances and routes, known parasites and pathogens, and major threats to species persistence. Infec- tious diseases have been examined in the context of migration for some, but not all, of these species. Supporting references and photo credits are provided in the supporting online material (SOM) text.

114 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS of reproduction [e.g., (McKinnon et al., 2010)]. Contemporary studies of migra- tion have uncovered mechanisms of animal navigation, energy budgets, resource use, and phenological responses to environmental change; migratory species have also been recognized for their potential to connect geographically distant habitats and transfer large amounts of biomass and nutrients between ecosystems [reviewed in (Bowlin et al., 2010)]. These studies illustrate the profound ecologi- cal and evolutionary consequences of migratory journeys for animal populations on a global scale. Owing to their long-distance movements and exposure to diverse habitats, migratory animals have far-reaching implications for the emergence and spread of infectious diseases. Importantly, most previous work on the role of host move- ment in infectious disease dynamics has focused on spatially localized or random dispersal. For example, dispersal events give rise to traveling waves of infection in pathogens such as raccoon rabies (Russell et al., 2005), influenza in humans (Viboud et al., 2006), and nuclear polyhedrosis viruses in insects (White et al., 2000). In the context of metapopulations, limited amounts of host movement could actually prevent host extinction in the face of a debilitating pathogen and allow host resistance genes to spread (Carlsson-Granner and Thrall, 2002; Gog et al., 2002). From a different perspective, case studies of species invasions dem- onstrate that one-time transfers of even a few individuals can transport pathogens long distances and introduce them to novel habitats (Daszak et al., 2000). Yet relatively few studies have examined how regular, directed mass movements that characterize seasonal migration affect the transmission and evolution of patho- gens within host populations and the response of migratory species to infection risks. In this article, we review the consequences of long-distance migrations for the ecological dynamics of host–pathogen interactions and outline key challenges for future work. Ecological processes linked with migration can increase or de- crease the between-host transmission of pathogens, depending on host migratory behavior and pathogen traits (Figure A1-3). Moreover, new work shows that for some species, the energetic demands of migration compromise host immunity, possibly increasing susceptibility to infection and intensifying the impacts of dis- ease. Importantly, many migratory species are at risk of future declines because of habitat loss and exploitation, and animal migrations are shifting with ongoing anthropogenic change (Wilcove, 2008). Thus, understanding how human activi- ties that alter migratory patterns influence wildlife–pathogen dynamics is urgently needed to help guide conservation and management of migratory species and mitigate future risks from infectious disease. What Goes Around Comes Around: Pathogen Exposure and Spatial Spread An oft-cited but poorly supported assumption is that long-distance move- ments of migrating animals can enhance the geographic spread of pathogens,

APPENDIX A 115 FIGURE A1-3  Points along a general annual migratory cycle where key processes can increase (red text) or decrease (blue text) pathogen exposure or transmission. Behavioral mechanisms such as migratory escape and migratory culling could reduce overall pathogen prevalence. As animals travel to distant geographic locations, the use of multiple habitat types including stopover sites, breeding areas, and wintering grounds can increase trans- mission as a result of host aggregations and exposure to multihost pathogens. This might be especially true for migratory staging areas where animals stop to rest and refuel. High energetic demands of spring and fall migration can also result in immunomodulation, pos- sibly leading to immune suppression and secondary infections. [Photo credits (clockwise): J. Goldstein, B. McCord, A. Friedlaender, and R. Hall]

116 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS including zoonotic pathogens important for human health such as Ebola virus in bats, avian influenza viruses in waterfowl and shorebirds, and Lyme disease and West Nile virus (WNV) in songbirds. For example, WNV initially spread in North America along a major corridor for migrating birds and rapidly expanded from its point of origin in New York City along the Atlantic seaboard from 1999 to 2000 (Rappole et al., 2000). Although experimental work concluded that pas- serine birds in migratory condition were competent hosts for WNV and could serve as effective dispersal agents (Owen et al., 2006), evidence to show that this expansion resulted from movements of migratory birds remains equivocal. For the zoonotic pathogen Ebola virus, a recent study points to the coincident timing of an annual influx of migratory fruit bats in the Democratic Republic of Congo and the start of human Ebola outbreaks in local villages during 2007 (Leroy et al., 2009). In central Kazakhstan, saiga antelopes (Saiga tatarica) become infected with gastrointestinal nematodes (Marshallagia) during the course of seasonal migration by grazing on pastures used by domesticated sheep earlier in the sea- son. As migration continues, saiga carry and transmit Marshallagia to northern sheep populations, leading to pulses of infection that coincide with annual saiga migrations (Morgan et al., 2007). The potential for serious disease risks for human and livestock health has raised alarm about the role of migratory species in moving infectious agents to distant locations. Yet surprisingly few examples of long-distance pathogen dispersal by migrating animals have been clearly documented in the published literature, and some studies indicate that migrants might be unfairly blamed for transporting pathogens. As a case in point, wild waterfowl (Anseriformes) and shorebirds (Charadriiformes) represent the major natural reservoirs for diverse strains of avian influenza virus (AIV) worldwide, including the highly pathogenic (HP) H5N1 subtype that can lethally infect humans (Olsen et al., 2006). Although many of these migratory birds can become infected by HP H5N1, recent work incorporating what is known about viral shedding period, host migration phe- nology, and the geographical distribution of viral subtypes suggests that most wild birds are unlikely to spread HP H5N1 long distances (e.g., between Asia and the Americas) as previously suspected [e.g., (Krauss et al., 2007; Takekawa et al., 2010)]. Central to the question of how far any host species can transport a pathogen are the concepts of pathogen virulence and host tolerance to infection. Specifically, virulence refers to the damage that parasites inflict on their hosts, and tolerance refers to the host’s ability to withstand infection without suffering major fitness costs. Thus, host–parasite species or genotype combinations associ- ated with very low virulence or high tolerance should be the most promising can- didates for long-distance movement of pathogen strains, a simple prediction that could be explored within migratory species or using cross-species comparisons. Beyond their potential role in pathogen spatial spread, a handful of studies suggest that migratory species themselves encounter a broader range of pathogens from diverse environments throughout their annual cycle compared with species

APPENDIX A 117 residing in the same area year-round (Figure A1-3). One field study showed that songbird species migrating from Europe became infected by strains of vector- borne blood parasites originating from tropical bird species at overwintering sites in Africa (Waldenström et al., 2002), in addition to the suite of parasite strains transmitted at their summer breeding grounds. The authors posited that winter exposure to parasites in tropical locations is a significant cost of migration, be- cause resident species wintering in northern latitudes encounter fewer parasite strains and do not experience year-round transmission. Similarly, the number of parasite species per host was positively related to distances flown by migratory waterfowl (Figuerola and Green, 2000), indicating that migrating animals could become exposed to parasites through encounters with different host species and habitat types. Although some animals undertake nonstop migrations, most migratory spe- cies use stopover points along the migration route to rest and feed. These stop- over points usually occur frequently along a journey, although some species like shorebirds fly thousands of kilometers between only a handful of staging areas (Dingle, 1996). Refueling locations are often shared by multiple species, and the high local densities and high species diversity can increase both within- and between-species transmission of pathogens. In one of the most striking examples of this phenomenon, shorebirds such as sanderlings (Calidris alba), ruddy turn- stones (Arenaria interpres; Figure A1-2), and red knots (Calidris canutus), which migrate annually between Arctic breeding grounds and South American wintering sites, congregate to feed in massive numbers in the Delaware Bay and the Bay of Fundy to rebuild fat reserves, leading to upwards of 1.5 million birds intermin- gling, at densities of over 200 birds per square meter (Krauss et al., 2010). This phenomenon creates an ecological hotspot at Delaware Bay, where the prevalence of AIV is 17 times greater than at any other surveillance site worldwide (Krauss et al., 2010). Leaving Parasites Behind: Migration as a Way of Lowering Infection Risk Although greater exposure to parasites and pathogens can pose a significant cost of long-distance migration, for some animal species, long-distance migration will reduce infection risk by at least two nonexclusive processes (Figure A1-3). First, if prolonged use of habitats allows parasites with environmental transmis- sion modes to accumulate (i.e., those parasites with infectious stages that can persist outside of hosts, such as many helminths, ectoparasites, and microbial pathogens with fecal-oral transmission), migration will allow animals to escape from contaminated habitats [i.e., “migratory escape” (Loehle, 1995)]. Between intervals of habitat use, unfavorable conditions (such as harsh winters and a lack of hosts) could eliminate most parasites, resulting in hosts returning to these habitats after a long absence to encounter largely disease-free conditions (Loehle, 1995). Empirical support for migratory escape comes from a few well-studied

118 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS host–parasite interactions, including research on reindeer (Rangifer tarandus), which showed that the abundance of warble flies (Hypoderma tarandi) was nega- tively correlated with the distance migrated to summer pastures from reindeer calving grounds (the main larval shedding area in early spring) (Folstad et al., 1991). This observation prompted researchers to suggest that the reindeers’ an- nual postcalving migration reduces warble fly transmission by allowing animals to leave behind areas where large numbers of larvae have been shed (and where adult flies will later emerge). It is worth noting that escape will be less successful from pathogens with long-lived infectious stages that persist between periods of host absence or pathogens that cause chronic or life-long infections. Long-distance migration can also lower pathogen prevalence by removing infected animals from the population [i.e., “migratory culling” (Bradley and Altizer, 2005)]. In this scenario, diseased animals suffering from the negative consequences of infection are less likely to migrate long distances owing to the combined physiological demands of migration and infection. Work on the migra- tory fall armyworm moth (Spodoptera frugiperda) suggested that insects infected by an ectoparasitic nematode (Noctuidonema guyanense) had reduced migratory ability because few to no parasites were detected in moths recolonizing sites as they returned north (Simmons and Rogers, 1991). More recent work on Bewick’s swans (Cygnus columbianus bewickii) showed that infection by low-pathogenic avian influenza (LPAI) viruses delayed migration over a month and reduced the travel distances of infected birds compared with those of healthy individuals (van Gils et al., 2007). However, a study of AIV in white fronted geese did not find any difference in distances migrated between infected and uninfected birds (Kleijn et al., 2010), suggesting that, not surprisingly, some species are better able to tolerate infections during long journeys and raising the possibility that migra- tion could select for greater tolerance to infections in some hosts due to the high fitness costs of attempting migration with a debilitating pathogen. Whether the net effects of migration will increase or decrease prevalence depends in large part on the mode of parasite transmission and the level of host specificity, both of which will affect opportunities for cross-species transmission at staging and stopover sites. Parasites that decline in response to host migra- tion may include specialist pathogens, as well as those with transmission stages that can build up in the environment, pathogens transmitted by biting vectors or intermediate hosts, or for which transmission occurs mainly from adults to juveniles during the breeding season (e.g., Box A1-1). Conversely, migrating hosts could experience higher pressure from generalist parasites if opportunities for cross-species transmission are high at stopover areas or wintering grounds or from specialist pathogens if transmission increases with dense host aggre- gations that accompany mass migrations. Importantly, effects of migration on pathogen dynamics within host populations should translate to large differences in prevalence across host populations with different migratory strategies. Over the past few years, we have focused on monarch butterflies (Danaus plexippus)

APPENDIX A 119 as a model system to study the effects of migration on host–pathogen interac- tions (Box A1-1) and found that both migratory culling and migratory escape can cause spatiotemporal variation in prevalence within populations and extreme differences in prevalence among populations with different migratory strategies. However, we are not aware of intraspecific comparisons of prevalence between migratory and nonmigratory populations for other animal species. Immune Defense Balanced Against the Demands of Migration In addition to ecological mechanisms affecting between-host transmission, the physiological stress and energetic demands of migration can alter the out- come of infection within individuals through interactions with the host’s im- mune system (Figure A1-2). More generally, because several immune pathways in both vertebrates and invertebrates are known to be costly (Eraud et al., 2005; Schmid-Hempel, 2005), seasonal demands such as premigratory fattening or strenuous activity will likely lower the resource pool available for mounting an immune response (Weber and Stilianakis, 2007). In anticipation of migration, for example, some animals accrue up to 50% of their lean body mass in fat, increase muscle mass, and atrophy organs that are not essential during migration (Dingle, 1996). Thus, before migration, animals might adjust components of their immune response to a desired level (i.e., immunomodulation), or the energetic demands of migration could reduce the efficacy of some immune pathways (i.e., immunosuppression). To date, the effects of long-distance migration on immune defenses have been best studied in birds. In a rare study of immune changes in wild individu- als during migration, field observations of three species of thrushes showed that migrating birds had lower baseline measures for several components of innate immunity (including leukocyte and lymphocyte counts), and exhibited lower fat reserves and higher energetic stress, relative to individuals measured outside of the migratory season (Owen and Moore, 2006). Captive experiments with Swainson’s thrushes (Catharus ustulatus; Figure A1-2) later demonstrated that cell-mediated immunity was suppressed with the onset of migratory restlessness (the agitated behavior of birds that would normally precede their migratory depar- ture) (Owen and Moore, 2008a), suggesting that predictable changes in immunity occur in preparation for long-distance flight. In this species, the energetic costs of migration can intensify seasonal immune changes: Migrating thrushes that arrived at stopover sites in poorest condition had the lowest counts of immune cells (Owen and Moore, 2008b). The extent of altered immunity before and during migration is likely to be both species and resource dependent and will further depend on the specific im- mune pathway measured. Red knots, for example, exhibited no change in either antibody production or cell-mediated immunity after long flights in a wind tunnel, a result that argues against migration-mediated immunosuppression (Hasselquist

120 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS BOX A1-1 Lessons from a Model System: Monarch Migration Drives Large-Scale Variation in Parasite Prevalence During the past 10 years, we studied monarch butterflies (Danaus plexippus) and a protozoan parasite (Ophryocystis elektroscirrha) (top-right images) for the effects of seasonal migration on host–pathogen dynamics. Monarchs in eastern North America (A) migrate up to 2,500 km each fall from as far north as Canada to wintering sites in Central Mexico (Brower and Malcolm, 1991). Monarchs in western North America (B) migrate shorter distances to winter along the coast of California (Nagano et al., 1993). Monarchs also form nonmigratory populations et al., 2007). Another study of captive red knots revealed no declines in costly immune defenses during the annual periods of mass gain (Buehler et al., 2008); however, animals in this study had constant access to high-quality food, which might have negated energetic trade-offs between immune investment and weight gain. Interestingly, barn swallows (Hirundo rustica) in better physical condition showed greater measures of cellular immunity during migration, cleared ecto- parasites and blood parasites more effectively, and arrived earlier at breeding grounds than birds with poor energy reserves (Møller et al., 2004). These studies suggest that animals in robust condition or with access to resources might toler- ate long journeys without significant immunocompromise. Studies of migratory species to date also emphasize the need for a more detailed understanding of the

APPENDIX A 121 that breed year-round in southern Florida (C), Hawai’i, the Caribbean Islands, and Central and South America (Ackery and Vane-Wright, 1984). Because monarchs are abundant and widespread and can be studied easily both in the wild and in captivity, field and experimental studies can explore effects of annual migra- tions on host–pathogen ecology and evolution. A recent continent-scale analysis showed that parasite prevalence increased throughout the monarchs’ breeding season, with highest prevalence among adults associated with more intense habitat use and longer residency in eastern North America, consistent with the idea of migratory escape (bottom-right graph) (Bartel et al., 2010). Experiments showed that monarchs infected with O. elektroscirrha flew shorter distances and with reduced flight speeds, and field studies showed parasite prevalence de- creased as monarchs moved southward during their fall migrations (Bartel et al., 2010; Folstad et al., 1991), consistent with the idea of migratory culling. Parasite prevalence was also highest among butterflies sampled at the end of the breed- ing season than for those that reached their overwintering sites in Mexico (bot- tom right graph). Collectively, these processes have likely generated the striking differences in parasite prevalence reported among wild monarch populations with different migratory behaviors (bottom-left graph) (Altizer et al., 2000). Laboratory studies also showed that parasite isolates from the longest-distance migratory population in eastern North America (A) were less virulent than isolates from short-distance (B) and nonmigratory (C) populations (de Roode and Altizer, 2010; Altizer, 2001), suggesting that longer migration distances cull monarchs carrying virulent parasite genotypes. Work on this model system illustrates how multiple mechanisms can operate at different points along a migratory cycle and highlights the role that migration plays in keeping populations healthy. Monarch migrations are now considered an endangered phenomenon (Brower and Malcolm, 1991) due to deforestation of overwintering grounds, loss of critical breeding habitats, and climate-related shifts in migration phenology. If climate warming extends monarch breeding seasons into fall and winter months, migrations may eventually cease altogether. Evidence to date indicates that the loss of migration in response to mild winters and year-round resources could prolong exposure to parasites, elevate infection prevalence, and favor more virulent parasite genotypes. Images reproduced from (Bartel et al., 2010; Altizer et al., 2000). [Photos by S. Altizer] mechanisms linking nutrient intake and metabolic activity to innate and adaptive immune measures, a step that is essential to predicting how different immune pathways will respond to physiological changes that occur before and during long-distance migrations. Perhaps most importantly, immune changes that accompany long-distance migration could lead to a relapse of prior infections and more severe disease fol- lowing exposure to new pathogens, increasing the likelihood of migratory culling and lowering the probability of spatial spread. This possibility was investigated for Lyme disease in redwings (Turdus iliacus) (Gylfe et al., 2000). Consistent with results showing negative effects of migratory status on immunity, migratory restlessness alone was sufficient to reactivate latent Borrelia infections in captive

122 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS birds. Thus, the demands of migration could ultimately lead to more severe in- fections and greater removal of infected hosts. Together, these results point to a role for migration-mediated immune changes in the dynamics of other wildlife pathogens, including zoonotic agents such as WNV (Owen et al., 2006) and bat- transmitted corona and rabies viruses (Li et al., 2005; Messenger et al., 2002). Effects of Anthropogenic Change and Climate Changes to the ecology of migratory species in the past century (Figure A1-2) could have enormous impacts on pathogen spread in wildlife and livestock, as well as altering human exposure to zoonotic infections. As one example, habitat loss caused by urbanization or agricultural expansion can eliminate stopover sites and result in higher densities of animals that use fewer remaining sites along the migration route (Wilcove, 2008). Although the resulting impacts on infectious diseases remain speculative, dense aggregations of animals at dwindling stopover sites might create ecological hot spots for pathogen transmission among wildlife species, as illustrated in the case of AIV in migrating shorebirds at Delaware Bay (Krauss et al., 2007). Moreover, continuing human encroachment on stopover habitats increases the likelihood of contact and spillover infection from wildlife reservoir hosts to humans and domesticated species. For some animal species, physical barriers such as fences (terrestrial species) or hydroelectric dams (aquatic species) impede migration (Berger et al., 2008), leaving animals to choose between navigating a narrow migratory corridor or forming nonmigratory populations. Consequently, pathogen prevalence could increase when animals stop migrating and become confined to smaller habitats, if parasite infectious stages build up with more intense use of a given habitat. Attempts to control cattle exposure to brucellosis from bison (Bison bison) and elk (Cervus elaphus) in the Greater Yellowstone Ecosystem illustrate these risks. Due to the potential threat of Brucella transmission from bison to cattle, bison are routinely culled if they leave the confines of Yellowstone National Park (Bienen and Tabor, 2006). Elk migration is less restricted, but there is evidence that supplemental feeding areas encourage the formation of dense nonmigratory populations that support higher prevalence of brucellosis, with 10 to 30% sero- prevalence in animals at the feeding grounds compared with 2 to 3% seropreva- lence in unfed elk ranging the park (Cross et al., 2010). High population densities in elk also correlate with higher gastrointestinal parasite loads at feeding grounds (Hines et al., 2007), suggesting that high densities of nonmigrating hosts lead to increasing intraspecific transmission of multiple parasites. More generally, human activities that discourage long-distance animal move- ments and encourage the formation of local year-round populations can cause the emergence of zoonotic pathogens in humans. For example, human-meditated environmental changes facilitated outbreaks of two zoonotic paramyxoviruses

APPENDIX A 123 carried by flying foxes (Pteropus fruit bats; Figure A1-2): These animals are highly mobile and seasonally nomadic in response to local food availability (Daszak et al., 2006). Anthropogenic changes such as deforestation and agricul- tural production likely influenced the emergence of lethal Nipah and Hendra virus outbreaks in humans in Australia and Malaysia in two key ways: by resource supplementation and habitat alteration limiting migratory behaviors of fruit bats and by facilitating close contact with domesticated virus-amplifying hosts (pigs and horses). In Malaysia, resident flying foxes foraging on fruit trees on or near pig farms transmitted Nipah virus to pigs, probably via urine or partially con- sumed fruit with subsequent spread from pigs to humans [(Daszak et al., 2006) and references therein]. Human activities are also thought to increase the risk of Hendra virus outbreaks in Australia by driving flying foxes from formerly forested areas into urban and suburban areas (Plowright et al., 2008), where they form dense nonmigratory colonies that aggregate in public gardens containing abundant food sources. In marine systems, aquaculture increases exposure to parasites in wild fish species, particularly in salmonids. Migration normally protects wild juvenile salmon from marine parasites in coastal waters by spatially separating them from infected wild adults offshore (Krkošek et al., 2007), but densely populated salmon farms place farmed fish enclosures adjacent to wild salmon migratory corridors, increasing the transmission of parasitic sea lice (Lepeophtheirus salmonis) to wild juveniles returning to sea (Krkošek et al., 2007). Finally, climate change will alter infectious disease dynamics in some migra- tory species (Harvell et al., 2009). To survive, many migratory species must re- spond to climate changes by shifting migratory routes and phenology in response to temperature and the availability of key resources (i.e., flowering plants, insects) [e.g., (Saino et al., 2010)]. It is possible that changes in the timing of migration could disrupt the synchronicity of host and parasite life cycles, much in the way that ecological mismatch in migration timing or altered migratory routes could impact whether suitable food and habitat are available when migrants arrive. For example, the spawning periodicity of whale barnacles in calving lagoons of gray whales is a classic example of a parasite synchronizing its reproduction to overlap with a host’s migratory cycle (Rice and Wolman, 1971). If the tim- ing of whale migrations and barnacle reproduction shift in response to different environmental cues, this could result in reduced infections over time. On the other hand, altered migration routes might facilitate contact between otherwise geographically separated host species, leading to novel pathogen introductions and increasing disease risks for some wildlife species (Harvell et al., 2009). One example of this phenomenon involves outbreaks of phocine distemper virus in harbor seals (Phoca vitulina) in the North Sea, which was likely introduced by harp seals (Pagophilus groenlandicus) migrating beyond their normal range and contacting harbor seal populations (Jensen et al., 2002). Moreover, if climate

124 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS warming extends hosts’ breeding seasons, migrations may cease altogether, with year-round resident populations replacing migratory ones (Box A1-1), leading to greater pathogen prevalence through a loss of migratory culling and escape. Outlook and Future Challenges Understanding the mechanisms by which long-distance movements affect host–pathogen systems offers exciting challenges for future work, especially in the context of global change and evolutionary dynamics. In terms of basic re- search, there remains a great need to identify conditions under which migration will increase host exposure to infectious agents versus cases where migration can reduce transmission, with the ultimate goal of predicting the net outcomes for host species where multiple mechanisms operate on the same or different pathogens (e.g., Box A1-1). To that end, mechanistic models are needed to examine how migration affects infectious disease dynamics and to explore the relevance of possible mechanisms. Such models must combine within-season processes (including host reproduction, overwintering survival, and pathogen transmission) with between-season migration (Figure A1-4). For example, to examine the importance of environmental transmission for the dynamics of LPAI in North American birds, Breban et al. (Breben et al., 2009) modeled a waterfowl population migrating between two geographically distant sites, with transmission dynamics occurring at both breeding and wintering grounds. Similarly, models describing interconnected networks of metapopulations could be useful in inves- tigating disease dynamics between habitats linked through seasonal migrations (Keeling et al., 2010). Although currently uncommon in the literature, epidemio- logical models can also be extended to capture mechanisms such as migratory culling and migratory escape and to include multiple infectious agents to explore questions of coinfection and multihost transmission dynamics (Figure A1-4). One outstanding question is whether parasites can increase the migratory propensity of their hosts by favoring the evolution of migratory behaviors. Long- distance migration has previously been hypothesized to reduce predation risks for ungulates and birds, with the general rationale being that the survival costs of mi- gration should be outweighed by fitness benefits associated with reproduction. In support of this idea, field studies of wolf predation on North American elk at their summer breeding grounds (Hebblewhite and Merrill, 2007) and nest predation on migrating songbirds (McKinnon et al., 2010) showed that animals traveling farthest experienced the lowest predation risk. Similar observational studies could ask how the prevalence, intensity, virulence, and diversity of key parasites change with migratory distances traveled. To that end, comparing infection dynamics between migratory and nonmigratory populations of the same species offers a powerful test of both pattern and process (e.g., Box A1-1), although research- ers will need to keep in mind that climate differences (e.g., milder climates for habitats used by nonmigratory populations) could confound some comparisons.

APPENDIX A 125 FIGURE A1-4 A compartmental model illustrating infectious disease dynamics (S-I model) in a migratory host population moving between geographically distinct breeding and overwintering habitats. Susceptible hosts (S) in the breeding grounds are born (v), die (μb) because of background mortality, and become infected at a rate, β. Infected hosts (I) suffer disease-induced mortality (αb). Different fractions of susceptible (xb) and in- fected hosts (yb) survive migration from the breeding habitat and arrive successfully at an overwintering habitat at some rate (δb). Here, natural (μw) and disease-induced mortality (αw) are both influenced by a different set of environmental conditions that characterize wintering grounds. The fraction of hosts surviving the spring migration the following year (xwδw, ywδw) will return to the breeding grounds to reproduce. A simple model like this can be readily modified to accommodate different parasite species and their transmission modes, host recovery, host age structure, and cross-species transmission. Modeling approaches are also needed to explore how seasonal migration might respond evolutionarily to parasite-driven pressures, similar to other studies that examined effects of within-site competition, costs of dispersal, and variation in habitat quality on random dispersal strategies (McPeek and Holt, 1992). Another question related to host evolution is whether the combined demands of migration and disease risk could select for greater or lower investment in resistance or immunity. Field and laboratory studies have already documented between-season changes in immune investment, suggesting that some migratory species suppress specific immune responses before or during migration (Owen

126 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS and Moore, 2006). The reduction in investment in immune defense could be an adaptive response to lower risks from certain parasites in migratory species (beyond issues related to energetic trade-offs) and might affect adaptive im- munity (shown to be costly for many vertebrate species) more strongly than in- nate defenses. Over longer time scales, long-distance migration could select for greater levels of innate immunity in migratory species or populations, especially if migrating animals encounter more diverse parasite assemblages (Møller and Erritzøe, 1998). With this in mind, comparisons of adaptive and innate immune defense and resistance to specific pathogens between migratory and nonmigra- tory populations represent a challenge for future work that could be especially tractable with invertebrate systems (Altizer, 2001). Pathogens might also respond to migration-mediated selection, with ecologi- cal pressures arising from migration leading to divergence in virulence. There is some evidence to show that less-virulent strains circulate in migratory popula- tions than in resident populations. The negative correlation between virulence and host migration distance, illustrated in the monarch system (Box A1-1), highlights the troubling possibility that pathogens infecting other migratory species could become more virulent if migrations decline. Moreover, dwindling migrations might affect host life history by altering pathogen virulence in once-migratory hosts. For example, a theoretical study showed that even moderate increases in virulence can change host breeding phenology to stimulate hosts to develop more quickly and breed earlier before they have a chance to become heavily infected (Restif et al., 2004). The recent facial tumor disease devastating Tasmanian devil populations provides a striking empirical example of high disease-induced mor- tality shifting host reproductive strategy from an iteroparous to a semelparous pattern through precocious sexual maturity in young devils (Jones et al., 2008). Although the hosts in this example are nonmigratory, they illustrate how virulent pathogens can generate longer-term fecundity costs beyond their direct impacts on host survival. Studying the migratory process in any wildlife species poses exceptional logistical challenges, in part because distances separating multiple habitats can sometimes span thousands of kilometers, making sampling for infection or im- munity intractable for field researchers. One problem is that historically, large numbers of animals have been sampled and marked at migratory staging areas, but for many species their subsequent whereabouts remain unknown (Webster et al., 2002). Tracking animals over long time periods and across vast distances has become more feasible with technological innovations such as radar and satel- lite telemetry for larger animals and ultra-light geolocators, stable isotopes, and radio tags to record or infer the movements of smaller animals (Robinson et al., 2010). Furthermore, physiological measurements such as heart rate, wing beat frequency, and blood metabolites can be obtained remotely for some species, enabling scientists to examine how infection status influences movement rates and the energetic costs of migration (Robinson et al., 2010).

APPENDIX A 127 Interdisciplinary studies to connect the fields of migration biology and in- fectious disease ecology are still in the early stages, and there are many excit- ing research opportunities to examine how infection dynamics relate to animal physiology, evolution, behavior, and environmental variation across the annual migratory cycle. Most evidence comes from studies of avian-pathogen systems, especially viruses. Although this is not surprising given the relevance of patho- gens such as avian influenza and WNV to human health, there remains a great need to explore other systems. Good places to start would be to make connections between disease and migration for species such as sea turtles, wildebeest, bats, dragonflies, and whales (Figure A1-2). Parasite infections and movement ecology in species in each of these groups have been well studied separately but not yet bridged. Taking a broad view of diverse host life histories and parasite trans- mission modes will allow future studies to identify ecological generalities and system-specific complexities that govern the mechanistic relationships between host movement behavior and infectious disease dynamics. Acknowledgments For helpful discussion and comments, we thank J. Antonovics, A. Davis, A. Dobson, V. Ezenwa, R. Hall, C. Lebarbenchon, A. Park, L. Ries, P. Rohani, P. R. Stephens, D. Streicker, and the Altizer/Ezenwa lab groups at the University of Georgia. This work was supported by an NSF grant (DEB-0643831) to S.A., a Ruth L. Kirschstein National Research Service Award through the NIH to R.B., an NSF Bioinformatics Postdoctoral Fellowship to B.A.H., and a National Center for Ecological Analysis and Synthesis working group on Migration Dynamics organized by S.A., L. Ries, and K. Oberhauser. References H. Dingle, Migration: The Biology of Life on the Move (Oxford Univ. Press, Oxford, 1996). L. McKinnon et al., Science 327, 326 (2010). M. S. Bowlin et al., Integr. Comp. Biol. 50, 261 (2010). C. A. Russell, D. L. Smith, J. E. Childs, L. A. Real, PLoS Biol. 3, e88 (2005). C. Viboud et al., Science 312, 447 (2006); 10.1126/science.1125237. A. White, A. D. Watt, R. S. Hails, S. E. Hartley, Oikos 89, 137 (2000). U. Carlsson-Graner, P. H. Thrall, Oikos 97, 97 (2002). J. Gog, R. Woodroffe, J. Swinton, Proc. R. Soc. London Ser. B 269, 671 (2002). P. Daszak, A. A. Cunningham, A. D. Hyatt, Science 287, 443 (2000). D. S. Wilcove, No Way Home: The Decline of the World’s Great Animal Migrations (Island Press, Washington, DC, 2008). J. H. Rappole, S. R. Derrickson, Z. Hubálek, Emerg. Infect. Dis. 6, 319 (2000). J. Owen et al., EcoHealth 3, 79 (2006). E. M. Leroy et al., Vector Borne Zoonotic Dis. 9, 723 (2009). E. R. Morgan, G. F. Medley, P. R. Torgerson, B. S. Shaikenov, E. J. Milner-Gulland, Ecol. Modell. 200, 511 (2007). B. Olsen et al., Science 312, 384 (2006).

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APPENDIX A 129 P. R. Ackery, R. I. Vane-Wright, Milkweed Butterflies: Their Cladistics and Biology (Cornell Univ. Press, Ithaca, NY, 1984). R. A. Bartel et al., Ecology; published online 19 July 2010 (10.1890/10-0489.1). S. M. Altizer, K. Oberhauser, L. P. Brower, Ecol. Entomol. 25, 125 (2000). J. C. de Roode, S. Altizer, Evolution 64, 502 (2010). A2 CLIMATE CHANGE AND INFECTIOUS DISEASES: FROM EVIDENCE TO A PREDICTIVE FRAMEWORK3 Sonia Altizer,4 Richard S. Ostfeld,5 Pieter T. J. Johnson,6 Susan Kutz,7 and C. Drew Harvell8 Abstract Scientists have long predicted large-scale responses of infectious diseases to climate change, giving rise to a polarizing debate, especially concerning human pathogens for which socioeconomic drivers and control measures can limit the detection of climate-mediated changes. Climate change has already increased the occurrence of diseases in some natural and agricul- tural systems, but in many cases, outcomes depend on the form of climate change and details of the host–pathogen system. In this review, we highlight research progress and gaps that have emerged during the past decade and develop a predictive framework that integrates knowledge from ecophysiol- ogy and community ecology with modeling approaches. Future work must continue to anticipate and monitor pathogen biodiversity and disease trends in natural ecosystems and identify opportunities to mitigate the impacts of climate-driven disease emergence. 3  Originally printed as Altizer et al. 2013. Climate change and infectious diseases: From evidence to a predictive framework. Science 341(6145):514-519. Reprinted with permission from the AAAS. 4  Odum School of Ecology, University of Georgia, Athens, GA 30602, USA. 5  Cary Institute of Ecosystem Studies, 2801 Sharon Turnpike, or Post Office Box AB, Millbrook, NY 12545-0129, USA. 6  Ecology and Evolutionary Biology, N122, CB334, University of Colorado, Boulder, CO 80309- 0334, USA. 7  Department of Ecosystem and Public Health, Faculty of Veterinary Medicine, University of Calgary, and Canadian Cooperative Wildlife Health Centre, Alberta Node, 3280 Hospital Drive NW, Calgary, Alberta T2N 4Z6, Canada. 8  Ecology and Evolutionary Biology, E321 Corson Hall, Cornell University, Ithaca, NY 14853, USA.

130 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS The life cycles and transmission of many infectious agents—including those causing disease in humans, agricultural systems, and free-living animals and plants—are inextricably tied to climate (Garrett et al., 2013; Harvell et al., 2002). Over the past decade, climate warming has already caused profound and often complex changes in the prevalence or severity of some infectious diseases (Figure A2-1) (Baker-Austin et al., 2013; Burge et al., 2014; Garrett et al., 2013; FIGURE A2-1  Animal–parasite interactions for which field or experimental studies have linked climate change to altered disease risk. (A) Black-legged ticks, Ixodes scapularis, vectors of Lyme disease, attached to the ears of a white-footed mouse, Peromyscus leuco- pus, show greater synchrony in larval and nymphal feeding in response to milder climates, leading to more rapid Lyme transmission. (B) Caribbean coral (Diploria labyrinthiformis) was affected by loss of symbionts, white plague disease, and ciliate infection during the 2010 warm thermal anomaly in Curaçao. (C) Malformed leopard frog (Lithobates pipiens) as a result of infection by the cercarial stage (inset) of the multihost trematode R. ondatrae; warming causes nonlinear changes in both host and parasite that lead to marked shifts in the timing of interactions. (D) Infection of monarchs (D. plexippus) by the protozoan O. elektroscirrha (inset) increases in parts of the United States where monarchs breed year-round as a result of the establishment of exotic milkweed species and milder winter climates. (E) Infection risk with O. gruehneri (inset shows eggs and larvae) the common abomasal nematode of caribou and reindeer (R. tarandus), may be reduced during the hottest part of the Arctic summer as a result of warming, which leads to two annual trans- mission peaks rather than one (e.g., Figure A2-3C). Photo credits (A to E): J. Brunner, E. Weil, D. Herasimtschuk, S. Altizer, P. Davis, S. Kutz.

APPENDIX A 131 Harvell et al., 2009). For human diseases, vector-control, antimicrobial treat- ments, and infrastructural changes can dampen or mask climate effects. Wild- life and plant diseases are generally less influenced by these control measures, making the climate signal easier to detect (Harvell et al., 2009). For example, although the effects of climate warming on the dynamics of human malaria are debated, climate warming is consistently shown to increase the intensity and/or latitudinal and altitudinal range of avian malaria in wild birds (Garamszegi, 2011; Zamora-Vilchis et al., 2012). Predicting the consequences of climate change for infectious disease severity and distributions remains a persistent challenge surrounded by much controversy, particularly for vector-borne infections of humans [boxes S1 and S2 (available as supplementary materials on Science Online)]. Work using climate-based envelope models has predicted that modest climate-induced range expansions of human malaria in some areas will be offset by range contractions in other locations (Rogers and Randolph, 2000). An alternative approach, based on mechanistic models of physiological and demographic processes of vectors and pathogens (Ruiz-Moreno et al., 2012), predicts large geographic range expansions of hu- man malaria into higher latitudes (Martens et al., 1995). Both approaches have their limitations (Garrett et al., 2013), and the challenge remains to accurately capture the contributions of multiple, interacting, and often nonlinear underly- ing responses of host, pathogen, and vector to climate. This challenge is further exacerbated by variation in the climate responses among host–pathogen systems arising from different life history characteristics and thermal niches (Molnár et al., 2013). A decade ago, Harvell et al. (2002) reviewed the potential for infectious diseases to increase with climate warming. Since then, the frequency of studies examining climate–disease interactions has continued to increase (Figure A2-2), producing clear evidence that changes in mean temperature or climate vari- ability can alter disease risk. Some of the best examples of climate responses of infectious diseases to date are from ectothermic hosts and from parasites with environmental transmission stages that can persist outside the host (Figure A2-1). Indeed, first principles suggest that the rates of replication, development, and transmission of these pathogens should depend more strongly on temperature relative to other host–pathogen interactions. The next challenges require integrat- ing theoretical, observational, and experimental approaches to better predict the direction and magnitude of changes in disease risk. Identifying the contribution of other environmental variables, such as precipitation, humidity, and climate variability remains a challenge (Paijmans et al., 2009; Raffel et al., 2013). Here, we review the individual, community, and landscape-level mechanisms behind climate-induced changes in infectious disease risk and illustrate how a quantitative, ecophysiological framework can predict the response of different host–pathogen relations to climate warming. We mainly focus on changes in temperature, which have been more thoroughly explored both empirically and

132 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS FIGURE A2-2  Rising interest in climate–disease interactions. Research focused on asso- ciations between infectious disease and climate change has increased steadily over the past 20 years. After correcting for total research interest in climate change (open symbols) or infectious disease (closed symbols), the frequency of papers referencing a climate–disease link in the title has nearly doubled over this period, based on long-term publication trends following a Web of Science search of article titles (1990 to 2012). Search criteria and statistical analyses are provided in the supplementary materials, and the total number of climate change–infectious disease papers identified by our search criteria ranged from 5 to 117 publications per year. theoretically, relative to other environmental variables. We consider impacts of climate change on human diseases and on pathogens affecting species of con- servation or economic concern, including agroecosystems [box S3 (available as supplementary materials on Science Online)]. A crucial need remains for long- term ecological studies that examine the consequences of climate-disease inter- actions for entire communities and ecosystems, as well as for efforts that couple effective disease forecasting models with mitigation and solutions. Ecophysiology of Host–Pathogen Interactions More than a century of research has firmly established that temperature and other climatic variables strongly affect the physiology and demography of free- living and parasitic species [e.g., (Walther et al., 2002)], with effects on behavior, development, fecundity, and mortality (Parmesan and Yohe, 2003). Because these

APPENDIX A 133 effects can be nonlinear and sometimes conflicting, such as warmer temperatures accelerating invertebrate development but reducing life span, a central challenge has been to identify the net outcomes for fitness (Harvell et al., 2002). For infec- tious diseases, this challenge is compounded by the interactions between at least two species—a host and a pathogen—and often vectors or intermediate hosts, which make the cumulative influence of climate on disease outcomes elusive [e.g., (Lafferty, 2009; Rohr et al., 2011)]. Immune defenses are physiological processes crucial for predicting changes in disease dynamics. Warmer temperatures can increase immune enzyme ac- tivity and bacterial resistance for insects, such as the cricket Gryllus texensis (Adamo and Lovett, 2011). Positive effects of temperature on parasite growth and replication, however, might outweigh greater immune function of the host. In gorgonian corals, for example, warmer temperatures increase cellular and hu- moral defenses (Mydlarz et al., 2006), but because coral pathogens also replicate faster under these conditions, disease outbreaks have coincided with warmer sea temperatures in the Caribbean (Figure A2-1) (Burge et al., 2014; Harvell et al., 2009). Warm temperatures also can lower host immunity; for example, melanization and phagocytic cell activity in mosquitoes are depressed at higher temperatures (Murdock et al., 2012). In addition, increased climate variability can interfere with host immunity, as illustrated by decreased frog resistance to the chytrid fungus Batrachochytrium dendrobatidis (Bd) in response to temperature fluctuations (Raffel et al., 2013). Even though Bd grows best in culture at cooler temperatures, which suggests that warming should reduce disease, incorporating variability-induced changes in host resistance suggests a more complex relation between climate change and Bd-induced amphibian declines (Rohr and Raffel, 2010). These issues are important for predicting the immunological efficiency of ectotherms outside of their typical climate envelope. One promising approach for predicting how host–pathogen interactions re- spond to climate warming involves infusing epidemiological models with re- lations derived from the metabolic theory of ecology (MTE). This approach circumvents the need for detailed species-specific development and survival parameters by using established relations between metabolism, ambient tempera- ture, and body size to predict responses to climate warming (Brown et al., 2004). One breakthrough study (Molnár et al., 2013) used MTE coupled with traditional host–parasite transmission models to examine how changes in seasonal and annual temperature affected the basic reproduction number (R0) of strongylid nematodes with direct life cycles and transmission stages that are shed into the environment. By casting R0 in terms of temperature-induced trade-offs between parasite development and mortality, this approach facilitated both general predic- tions about how infection patterns change with warming and, when parameterized for Ostertagia gruehneri, a nematode of caribou and reindeer (Figure A2-1), specific projections that corresponded with the observed temperature dependence of parasite stages. Moreover, this model predicted a shift from one to two peaks

134 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS in nematode transmission each year under warming conditions (Figure A2-3C), a result consistent with field observations (Hoar et al., 2012; Molnár et al., 2013). In some cases, ecophysiological approaches must consider multiple host species and parasite developmental stages that could show differential sensitivity to warming. Such differential responses can complicate prediction of net effects, especially for ectothermic hosts with more pronounced responses to tempera- ture. For instance, because both infectivity of a trematode parasite (Ribeiroia ondatrae) and defenses of an amphibian host (Pseudacris regilla) increase with FIGURE A2-3 Theoretical underpinnings and categorization of disease responses to climate change.

APPENDIX A 135 temperature; maximal pathology (limb malformations) (Figure A2-1) occurs at intermediate temperatures (Paull et al., 2012). Other work showed that the virulence of both a coral fungus (Aspergillus sydowii) and protozoan (Aplanochy- trium sp.) increased with temperature, probably because pathogen development rate continued to increase in a temperature range where coral defenses were less potent (Burge et al., 2013). Thus, the ideal approach will be an iterative one that combines metabolic and epidemiological modeling to predict general responses and to identify knowledge gaps, followed by application of models to specific host–pathogen interactions. Pathogen responses to climate change depend on thermal tolerance relative to current and projected conditions across an annual cycle. (A) Gaussian curves relating temperature to a metric of disease risk suggest symmetrical temperature zones over which warming will increase and decrease transmission, whereas left-skewing [a common response for many terrestrial ectotherms, including arthropod vectors (Deutsch et al., 2008)] indicates greater potential for pathogen transmission to increase with warming [box S2 (available as supplementary materials on Science Online)]. Bold arrows represent geographic gradients that span cool, warm, and hot mean temperatures, which indicate that the net effect of warming (at point of arrows) depends on whether temperatures grow to ex- ceed the optimum temperature (Topt) for disease transmission. Projected changes in disease will further depend on the starting temperature relative to Topt, the magnitude of warming, measurement error, adaptation, and acclimation. (B) Pathogens at their northern or altitudinal limits might show range expansion and nonlinear shifts in their life cycle in response to warmer temperatures (red) rela- tive to baseline (blue). For example, a shift from 2- to 1-year cycles of transmis- sion has occurred for the muskox lungworm (Kutz et al., 2009). This outcome could generate sporadic disease emergence in a naïve population (if extremes in temperature allow only occasional invasion and/or establishment), or could gradually increase prevalence and establishment. (C) At the low-latitude or low- altitude extent of a pathogen’s range, where temperature increases could exceed the pathogen’s thermal optimum, transmission might be reduced, or we might see the emergence of a bimodal pattern whereby R0 peaks both early and late in the season, but decreases during the midsummer [as in the case of the arctic O. gruehneri–reindeer example (Molnár et al., 2013)]. In (B) and (C), the lower blue line represents R0 = 1, above which the pathogen can increase; values above the pink line represent severe disease problems owing to a higher peak of R0 and a greater duration of time during which R0 > 1. Community Ecology, Biodiversity, and Climate Change Host–pathogen interactions are embedded in diverse communities, with cli- mate change likely leading to the loss of some host–pathogen interactions and the gain of novel species pairings. In some cases, pathogen extinction and the loss of

136 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS endemic parasites could follow from climate change, potentially reducing disease or conversely releasing more pathogenic organisms from competition. In other cases, multiple pathogens can put entire host communities at risk of extinction. Although ecosystems of low biodiversity, such as occur in polar regions, can be particularly sensitive to emerging parasitic diseases (Kutz et al., 2009), most knowledge of community-wide responses stems from tropical marine systems. For example, the wider Caribbean region is a “disease hot spot” characterized by the rapid, warming-induced emergence of multiple new pathogens that have caused precipitous coral declines with ecosystem-wide repercussions (Rogers and Muller, 2012; Ruiz-Moreno et al., 2012). Impacts of climate-induced changes in disease can be especially large when the host is a dominant or keystone species. For example, near extinction of the once-dominant, herbivorous abalone (genus Haliotis) by warming-driven rickettsial disease caused pervasive community shifts across multiple trophic levels (Burge et al., 2014). Similarly, seagrass (Zostera marina) declines caused by infection with the protist Labyrinthula zos- terae, which correlates positively with warming, have degraded nursery habitats for fish and migratory waterfowl and caused the extinction of the eelgrass limpet (Hughes et al., 2002). Microbial communities, which are often part of the extended phenotype of host defenses, are also likely to respond to climate changes. For instance, warm- ing sea-surface temperatures in coral reefs can inhibit the growth of antibiotic- producing bacteria, sometimes causing microbial communities to shift from mutualistic to pathogenic (Ritchie, 2006). In agroecosystems, higher tempera- tures can suppress entomopathogenic fungi and antibiotic production by bacte- rial mutualists in plants (Humair et al., 2009). Warming also underlies bacterial shifts from endosymbiotic to lytic within host amoebas that live in human nasal passages, increasing the potential risk of respiratory disease (Corsaro and Greub, 2006). Thus, effects of warmer temperatures on the diversity and function of commensal or mutualist microbes could promote pathogen growth and pest outbreaks. From a broader perspective, biodiversity loss is a well-established con- sequence of climate change (Jetz et al., 2007; Parmesan and Yohe, 2003) and can have its own impact on infectious diseases. For many diseases of humans, wildlife, and plants, biodiversity loss at local or regional scales can increase rates of pathogen transmission (Cardinale et al., 2012; Johnson and Hoverman, 2012; Keesing et al., 2010). This pattern can result from several mechanisms, includ- ing the loss of the dilution effect (Johnson and Hoverman, 2012). For example, lower parasite diversity could allow more pathogenic species to proliferate when endemic and competing parasites are lost from a system (Johnson and Hoverman, 2012). Climate warming can also weaken biotic regulation of disease vectors by inhibiting their predators (Hobbelen et al., 2013) and competitors (Farjana et al., 2012). Interactions between biodiversity and infectious disease underscore the need to put climate–disease interactions into the broader context of other forms

APPENDIX A 137 of global change, such as land-use change and habitat loss, when extending pre- dictions from focused host–pathogen interactions to larger spatial and taxonomic scales. Shifts in Behavior, Movement, and Phenology of Hosts and Parasites Changes in climate are already affecting the phenology of interactions be- tween plants and pollinators, predators and prey, and plants and herbivores (Parmesan and Yohe, 2003). Climate-induced shifts in phenology and species movements (Chen et al., 2011) will likely affect disease dynamics. Many species are already moving toward higher elevations or latitudes (Hickling et al., 2006), and an open question is whether these shifts could disrupt established interactions or bring novel groups of hosts and pathogens into contact (Morgan et al., 2004). For instance, the range expansion of the Asian tiger mosquito (Aedes albopictus) across Europe and the Americas has created the potential for novel viral diseases such as Chikungunya to invade (Ruiz-Moreno et al., 2012); this pathogen is already expanding in geographic range, and a recent outbreak in Europe empha- sizes the need for surveillance and preparedness. Along eastern North America, warming sea temperatures and changes in host resistance facilitated a northward shift of two oyster diseases into previously unexposed populations (Burge et al., 2014). Migratory species in particular can be sensitive to climate change (Hickling et al., 2006), with the routes and timing of some species’ migrations already shift- ing with climate warming (Parmesan and Yohe, 2003). Long-distance migrations can lower parasite transmission by allowing hosts to escape pathogens that accu- mulate in the environment or by strenuous journeys that cull sick animals (Altizer et al., 2011). In some cases, milder winters can allow previously migratory host populations to persist year-round in temperate regions (Bradshaw and Holzapfel, 2007); this residency fosters the build-up of environmental transmission stages, and mild winters further enhance parasite over-winter survival (Garrett et al., 2013). A case study of monarch butterflies (Danaus plexippus) and the protozoan parasite Ophyrocystis elektroscirrha (Figure A2-1) provides support for climate- warming shifts in migration and disease. Monarchs typically leave their northern breeding grounds in early fall and fly to Mexican wintering sites. Milder winters, combined with increased planting of exotic host plants, now allow monarch populations to breed year-round in parts of the United States (Howard et al., 2010). Relative to migratory monarchs, winter-breeding monarchs suffer from higher rates of infection (Altizer et al., 2011). Similarly, migration is considered an important parasite avoidance strategy for barren-ground caribou (Hoar et al., 2012), but the loss of sea ice with climate warming will likely inhibit migrations and prevent them from seasonally escaping parasites (Post et al., 2013). Thus, diminishing migration behaviors among animals that use seasonal habitats can increase the transmission of infectious diseases.

138 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS Changes in the timing of vector life stages and feeding behavior can also arise from interactions between climate and photoperiod. For several tick-borne infections (Figure A2-1), pathogens are sequentially transmitted from infected vertebrate hosts to naïve larval tick vectors, and from infected nymphal ticks to naïve vertebrate hosts. Asynchrony in the seasonal activity of larval and nymphal ticks can delay transmission and select for less virulent strains of the Lyme bacterium Borrelia burgdorferi (Kurtenbach et al., 2006), whereas synchrony allows for more rapid transmission and the persistence of virulent strains. In the case of tick-borne encephalitis (TBE), viral transmission occurs directly between cofeeding ticks; thus, viral maintenance requires synchronous larval and nymphal feeding (Randolph et al., 1999). Because synchrony of larval and nymphal ticks characterizes milder winter climates, climate change could increase tick syn- chrony and the transmission and virulence of several tick-borne infections. Changes in the timing of shedding or development of environmental trans- mission stages could result from climate warming. Some parasites could experi- ence earlier hatching, exposure to hosts earlier in the season, and encounters with earlier (and often more sensitive) life stages of hosts. For example, a long-term data set of lake plankton showed that warming shifted fungal prevalence patterns in diatom hosts from acute epidemics to chronic persistence, in part because of faster transmission and more widespread host population suppression under warmer temperatures (Ibelings et al., 2011). In contrast, Brown and Rohani (Brown and Rohani, 2012) argued for the opposite outcome with respect to avian influenza (AI) in reservoir bird hosts. Climate-driven mismatch in the timing of bird migration and their prey resources (e.g., horseshoe crab eggs) amplified vari- ability in epidemiological outcomes: Although mismatch increased the likelihood of AI extinction, infection prevalence and spillover potential both increased in cases where the virus persisted. Plasticity in parasite traits could allow parasites with environmental trans- mission stages to respond more rapidly to climate warming. For example, arrested development (hypobiosis) of the nematode O. gruehneri within its caribou host is a plastic trait more commonly expressed in areas with harsher winters as com- pared with milder climates (Hoar et al., 2012). This arrested state prevents wasted reproductive effort for the parasites, because eggs produced in late summer in colder regions are unlikely to develop to infective-stage larvae by fall. Ultimately, plasticity in life history traits could speed parasite responses to changing envi- ronments and allow parasites to deal with climate instabilities (e.g., a series of severe winters interspersed by mild), relative to the case where selection must act on genetically variable traits (Moritz and Agudo, 2013). For example, if climate warming extends the transmission season for O. gruehneri on tundra, a rapid decrease in the frequency of nematode hypobiosis could shorten the life cycle and increase infection rates.

APPENDIX A 139 Consequences for Conservation and Human Health Climate change is already contributing to species extinctions, both directly and through interactions with infectious disease (Thomas et al., 2004). Roughly one-third of all coral species and the sustainability of coral reef ecosystems are threatened by human activities, including climate warming and infectious dis- eases (Burge et al., 2014). In contrast to tropical marine systems, the Arctic is a less diverse and minimally redundant system that is warming at least twice as fast as the global average (International Panel on Climate Change, 2007) and si- multaneously experiencing drastic landscape changes from an expanding human footprint. Altered transmission dynamics of parasites, poleward range expansion of hosts and parasites, and disease emergence coincident with climate warming or extremes have all been reported in the Arctic (Kutz et al., 2009; Laaksonen et al., 2010). Together, these phenomena are altering host–parasite dynamics and causing endemic Arctic species—unable to compete or adapt rapidly enough—to decline (Gilg et al., 2012). Changes in wildlife health can also compromise the livelihoods and health of indigenous people who depend on wildlife for food and cultural activities (Meakin and Kurvitz, 2009). In humans, exposure to diarrheal diseases has been linked to warmer tem- peratures and heavy rainfall (Pascual et al., 2002). Human infections of cholera, typically acquired through ingestion of contaminated water (in developing coun- tries) or undercooked seafood (in the developed world), affect millions of people annually with a high case-fatality rate. Coastal Vibrio infections are associated with zooplankton blooms, warmer water, and severe storms (Baker-Austin et al., 2013). For example, in the Baltic Sea, long-term warming and temperature anomalies have been linked to increased disease from Vibrio vulnificus, which was first reported in 1994 along the German coast after an unusually warm sum- mer (Baker-Austin et al., 2013). Long-term sea surface warming can increase the geographic range, concentration, and seasonal duration of Vibrio infections, as seen in coastal Chile, Israel, and the U.S. Pacific Northwest. Modeling ap- proaches indicate that Vibrio illnesses from the Baltic region could increase nearly twofold for every 1°C increase in annual maximum water temperature (Baker-Austin et al., 2013). Human mosquito-borne diseases, such as malaria and dengue fever, are fre- quently proposed as cases where vector and disease expansion into the temperate zone could follow from climate warming (Mills et al., 2010). However, some researchers have argued that ranges will shift with warming, rather than expand, and that the best predictors of infection risk are economic and social factors, especially poverty (Lafferty, 2009; Randolph, 2010). Controversy has also arisen over which climatic variables are most important in delimiting the distributions of these diseases [boxes S1 and S2 (available as supplementary material on Science Online)]. Detecting impacts of climate change on human vector-borne diseases remains difficult, in part, because active mitigations, such as vector-control, an- timicrobials, and improved infrastructure, can complicate detection of a climate

140 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS signal. Several unresolved issues include identifying conditions under which climate warming will cause range expansions versus contractions, understanding the impact of increasing variability in precipitation, and determining the addi- tional economic costs associated with increased disease risk caused by warming. Ultimately, the societal implications of climate-driven shifts in diseases of humans, crops, and natural systems will demand solutions and mitigation, including early-warning programs. Recently, a forecasting system linking global coupled ocean-atmosphere climate models to malaria risk in Botswana allowed anomalously high risk to be predicted and anticipatory mitigations to be initiated (Thomson et al., 2006). Forecasting is well established in crop disease manage- ment and leads to improved timing of pesticide application and deployment of planting strategies to lower disease risk [box S3 (available as supplementary material on Science Online)]. Modeling efforts to better predict crop loss events are also tied to improved insurance returns against losses (Garrett et al., 2013). Similarly, accurate forecasting programs for coral bleaching have become a main- stay of marine climate resilience programs (Eakin et al., 2010) and are leading to the development of coral disease forecasting algorithms (Maynard et al., 2011). Appropriate management actions under outbreak conditions include reef closures to reduce stress and transmission, culling of diseased parts of some colonies, and increased surveillance (Beeden et al., 2012). In the ocean, efforts are also under way to increase the resilience of marine ecosystems to disease, including devel- oping no-fishing zones and reducing land-based pollution that can introduce new pathogens (Burge et al., 2014). Outlook and Future Challenges Climate change will continue to limit the transmission of some pathogens and create opportunities for others. To improve predictions and responses we need to deepen our understanding of mechanistic factors. Although the initial climatic drivers to be explored should be temperature variables (both mean and variability), because the data are available and we understand the mechanisms at work, future work must concurrently explore the effects of precipitation, relative humidity, and extreme events. In particular, models are needed that combine the principles of ecophysiology and MTE (Brown et al., 2004) with epidemiologi- cal response variables, such as R0 or outbreak size, and that are designed to ac- commodate distinct pathogen types (e.g., vector-borne, directly transmitted, or complex life cycle) and host types (ectotherm versus endotherm) (Molnár et al., 2013). These models should be applied, by using climate-change projections, to evaluate how broad classes of pathogens might respond to climate change. Building from this foundation, the next step is to extend such general models to specific pathogens of concern for human health, food supply, or wildlife conser- vation, which will require empirical parameterization, with attention to the on- the-ground conditions. Modeling efforts should be integrated with experiments to

APPENDIX A 141 test model predictions under realistic conditions, and with retrospective studies to detect the “fingerprint” of climate-induced changes in infection. Scientists still know relatively little about the conditions under which evolu- tion will shape host and pathogen responses to climate change. Although evo- lutionary change in response to climate warming has been reported for some free-living animals and plants, the evidence remains limited (Moritz and Agudo, 2013). Even less is known about how climate change will drive host–pathogen evolution. Corals have multiple levels of adaptation to intense selection by ther- mal stress that could also affect resistance to pathogens, including symbiont shuf- fling of both algae and bacteria, and natural selection against thermally intolerant individuals (Howells et al., 2011). In oysters (Crassostrea virginica), warming might have contributed to increased resistance to the protozoan multinucleated sphere X (MSX) disease (Ford and Bushek, 2012), but climate variability might also slow the evolution of oyster resistance (Powell et al., 2012). In cases where increased rates of transmission follow from warming, selection could favor higher pathogen virulence, although examples are now unknown. A persistent challenge involves the ability to detect changes in disease risk with climate across different systems. In the oceans, for example, impacts of disease on sessile hosts like corals, abalones, and oysters are readily apparent, and for terrestrial systems, clear impacts are seen for plant diseases and some wildlife-helminth interactions. But for highly mobile species and many human diseases, detecting signals of climate change remains problematic. For these less tractable systems, long-term ecological studies that examine the past distribu- tions of pathogens, important hosts, and severity of diseases are indispensable. Permanent repositories of intact physical specimens, as well as their DNA, can provide records of diversity that will be critical resources as new methodologies become available (Fernandez-Triana et al., 2011; Hoberg, 2010). Moreover, new technologies can detect variability in physiological processes and gene expression and can improve climate projections from global circulation models. Sophisti- cated experimental designs conducted under appropriate ranges of environmental conditions and retrospective studies to identify past climatic effects on disease (Burge et al., 2014; Hoverman et al., 2013) will help advance predictive power. An additional key challenge is predicting the impacts of climate–disease interactions for human societies and gauging how these compare with other components of climate change, such as the loss of arable land. By affecting food yields and nutrition, water quality and quantity, social disorder, population displacement, and conflict, past climate changes have long influenced the burden of infectious disease in many human societies (McMichael, 2012; Wheeler and von Braun, 2013). Predicting the regions where humans and natural systems are most vulnerable to pressures from infectious disease and how these pressures will translate to changes in global health and security constitute critical research pri- orities (Myers and Patz, 2009). Building a mechanistic understanding of climate– disease interactions will allow public health interventions to be proactive and will

142 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS facilitate effective responses to new or expanding health threats. Surveillance programs capable of detecting pathogen or disease emergence are essential and, in many instances, predicting and detecting local-scale impacts might be more important than predicting global-scale changes. To this end, the value of engag- ing local communities in disease surveillance is increasingly recognized, with the goal of advancing science on climate–disease linkages for practical solutions to protecting human and wildlife health. Acknowledgments This work was supported in part by an NSF grant (DEB-0643831) to S.A., a Fellowship from the David and Lucile Packard Foundation and NSF grant (IOS-1121529) to P.T.J.J., an NSF Research Coordination Network grant on the Ecology of Infectious Marine Diseases, NSF Ecology and Evolution of Infec- tious Diseases grant (OCE-1215977) to C.D.H., and by the Atkinson Center for a Sustainable Future at Cornell University. S.K. thanks the Natural Sciences and Engineering Council of Canada, the Nasivvik Centre for Inuit Health; the govern- ments of the Northwest Territories, Nunavut, and Yukon; and the government of Canada International Polar Year Program. References C. D. Harvell, C. E. Mitchell, J. R. Ward, S. Altizer, A. P. Dobson, R. S. Ostfeld, M. D. Samuel, Cli- mate warming and disease risks for terrestrial and marine biota. Science 296, 2158–2162 (2002). K. Garrett, A. D. M. Dobson, J. Kroschel, B. Natarajan, S. Orlandini, H. E. Z. Tonnang, C. Valdivia, The effects of climate variability and the color of weather time series on agricultural diseases and pests, and on decisions for their management. Agric. For. Meteorol. 170, 216–227 (2013). C. Baker-Austin et al., Emerging Vibrio risk at high latitudes in response to ocean warming. Nat. Clim. Change 3, 73 (2013). D. Harvell, S. Altizer, I. M. Cattadori, L. Harrington, E. Weil, Climate change and wildlife diseases: When does the host matter the most? Ecology 90, 912–920 (2009). 10.1890/08 C. Burge et al., Climate change influences on marine infectious diseases: implications for manage- ment and society. Annu. Rev. Mar. Sci. 6, (2014). 10.1146/annurev-marine-010213 L. Z. Garamszegi, Climate change increases the risk of malaria in birds. Glob. Change Biol. 17, 1751–1759 (2011). 10.1111/j.1365 I. Zamora-Vilchis, S. E. Williams, C. N. Johnson, Environmental temperature affects prevalence of blood parasites of birds on an elevation gradient: Implications for disease in a warming climate. PLoS ONE 7, e39208 (2012). D. J. Rogers, S. E. Randolph, The global spread of malaria in a future, warmer world. Science 289, 1763–1766 (2000). 10976072 D. Ruiz-Moreno, I. S. Vargas, K. E. Olson, L. C. Harrington, Modeling dynamic introduction of Chikungunya virus in the United States. PLoS Negl. Trop. Dis. 6, e1918 (2012). 10.1371/ journal.pntd.0001918 W. J. Martens, L. W. Niessen, J. Rotmans, T. H. Jetten, A. J. McMichael, Potential impact of global climate change on malaria risk. Environ. Health Perspect. 103, 458–464 (1995). P. K. Molnár, S. J. Kutz, B. M. Hoar, A. P. Dobson, Metabolic approaches to understanding climate change impacts on seasonal host-macroparasite dynamics. Ecol. Lett. 16, 9–21 (2013).

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146 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS T. Wheeler, J. von Braun, Climate change impacts on global food security. Science 341, 508–513 (2013). 10.1126/science. S. S. Myers, J. A. Patz, Emerging threats to human health from global environmental change. Annu. Rev. Environ. Resour. 34, 223–252 (2009). C. A. Deutsch, J. J. Tewksbury, R. B. Huey, K. S. Sheldon, C. K. Ghalambor, D. C. Haak, P. R. Martin, Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl. Acad. Sci. U.S.A. 105, 6668–6672 (2008). A3 MIGRATION, CIVIL CONFLICT, MASS GATHERING EVENTS, AND DISEASE Chris Beyrer9,10 and James Wren Tracy9 Introduction Human agency can drive infectious disease establishment, adaptation, and spread, which can subsequently have profound impacts on the health of individu- als, communities, and populations. Civil conflicts and the complex humanitarian emergencies they generate are widespread, common, and may increase in con- text of current global environmental change (Hsiang et al., 2013). Conflict, civil disruption, and the implicit migration that comes with both can compromise our ability to understand, track, respond, and mitigate infectious disease threats mak- ing their impact on human health even more difficult to address. With increased migration and mobility of peoples, a concurrent increase in exposure to multiple infectious diseases can occur. Populations mixing from the movement of individuals, groups, and sometimes whole communities can allow for a greater mixing of infectious diseases and heightened vulnerability to those diseases. Work by our group in Eastern Burma documented much higher rates of childhood and adult malaria, water-borne diarrheal diseases, childhood malnutri- tion, and land mine injuries among displaced populations in civil conflict zones than among stable and nondisplaced communities (Richards et al., 2009). More than just increasing the exposure to infectious diseases, migration can allow for greater acquisition and transmission of the diseases. Studies performed in South Africa, Kenya, Guinea-Bissau, and Nepal show an increased odds of HIV acquisition and infection among migratory groups, including rural to urban migration or migration out of the country (Beyrer et al., 2006). Migratory peoples 9  Centerfor Public Health and Human Rights, Johns Hopkins University. 10  Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, E 7152, Baltimore, MD, 21205.

APPENDIX A 147 seldom receive proper health care, particularly undocumented migrants who have left home countries. They can experience treatment delays and gaps, barriers to access and care, and lack many protective commodities such as bed nets, water filters, and condoms that would decrease further exposures. Given a lack of treatment and access to trained health care workers, morbid- ity and mortality can increase among migratory and mobile populations. Addi- tional limited access to essential medications can increase disease severity and the likelihood of onward transmission—sustaining cholera outbreaks, for example, as has occurred in Zimbabwe and Haiti among displaced populations (Piarroux et al., 2011; Sollom et al., 2009). The increase in infectious disease exposure and transmission within migra- tory groups does not only affect those within the group, but it can also affect those with whom the group comes into contact (Beyrer and Lee, 2008). A study performed in China showed that cities with a higher number of immigrants per 1,000 people also had a greater incidence of STDs (Tucker et al., 2005). There- fore, in order to protect the health of the displaced peoples and those they come into contact with, the underlying rights of these mobile groups and their access to adequate care must be protected and preserved. Often, particularly in the context of civil conflict, this does not happen. The flawed response to Cyclone Nargis, an enormous cyclone, which struck Burma/Myanmar, helps illustrate these issues— and demonstrates how climate change and human agency can interact in complex and challenging pathways—extracting heavy tolls on vulnerable populations. The conflict in Côte d’Ivoire illustrates another challenge—the loss of health care workers in conflict and our subsequent diminished capacity to both understand and address the health impacts of conflicts on populations. Conflict and Complex Humanitarian Emergencies Humanitarian emergencies can arise from many possible causes, but one of the more common causes, conflict, creates very complex problems. Conflict leads to displaced and marginalized people, as do many humanitarian crises. The political and social unrest that accompanies conflict is what makes the associated humanitarian issues much more difficult to right. Threats to humanitarian assis- tance are much more likely if conflict is ongoing, and the deliberate politicization of aid by forces in conflict is an increasing reality which can undermine responses and expose relief workers and beneficiaries to violence and intimidation, a feature of relief efforts in Sudan, DR Congo, and Burma (Lischer, 2006). The First Ivorian Civil War erupted in Côte d’Ivoire during 2002 with attacks by rebel forces on the government. These rebels took hold of the northern regions of the country, while the government maintained its claim to the southern regions, making the central region of the country a barrier zone. The conflict continued until 2007, despite numerous attempts at peace throughout the ensuing 4 years.

148 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS Before the First Ivorian Civil War, around 2001, a sizeable number of health care staff worked around the country. In the north, there were 38 doctors and 257 nurses; in the central region, there were 127 doctors and 471 nurses; and, in the west, there were 69 doctors and 310 nurses. After the conflict started and had been going on for a few years, around 2004, these numbers changed dramatically. In the north, there were 2 doctors and 82 nurses; in the central region, there were 3 doctors and 67 nurses; and, in the west, there were 6 doctors and 42 nurses (Betsi et al., 2006). Access to care then arose as a major problem for everyone living within the borders of Côte d’Ivoire. While the health staff dwindled, the prevalence of STIs markedly rose. Baseline measurements around 2002 showed that 24,636 people in Côte d’Ivoire had been infected, making the prevalence risk at that time 10.1 per 1,000 people. Around 2004, the infection rate increased. Measurements taken showed that 29,688 people now lived with an STI, making the new prevalence risk 21.5 per 1,000 people. Within just a few years, not forgetting the conflict that started in 2002, the prevalence had doubled. This increase in STIs is not rare in conflict situations. With decreased access to and use of reproductive health services, the normalization of sexual predation and violence, and increased population mixing, among others, the increase is hard to combat (Mills et al., 2006). The First Ivorian Civil War not only left the country with very little health care infrastructure, but it also started a massive spread of STIs, making many health issues much more complicated. While the issues here discussed may be somewhat specific to Côte d’Ivoire, the complex nature of the humanitarian cri- sis, causing rapid displacement, is something shared by all conflicts. The current strife in Syria, with over 100,000 dead and over 2 million refugees, shows that the problems are indeed not isolatable. Natural disasters add additional challenges to these health threats. According to the Brookings-Bern Report (Brookings-Bern Project on Internal Displacement, 2008), the human rights of disaster victims are often not taken into account and include: · Unequal access to assistance · Discrimination in aid provision · Enforced relocation · Sexual and gender-based violence · Loss of documentation · Recruitment of children into fighting forces · Unsafe or involuntary return or resettlement · Property restitution These problems are additional to the many consequences of a natural disaster felt by its victims. The tsunamis, hurricanes, and earthquakes, which hit parts of Asia and the Americas in 2004/2005, highlighted the multiple human rights

APPENDIX A 149 challenges victims of disasters may face, but the 2008 Cyclone Nargis and the response of the Myanmar government best shows the overwhelming problem of human rights within the context of conflict and natural disasters. Case Study: Cyclone Nargis and Burma/Myanmar In May of 2008, Cyclone Nargis hit the southwest corner of Myanmar and sent a massive storm surge into the Irrawaddy Delta. At least 146,000 died, 2.4 million were displaced, and 700,000 homes were destroyed in the wake of this enormous storm. The cyclone washed over some 5,000 km, and radically altered the geography of the Irrawaddy Delta itself. Much of what was rice fields and farmlands before the storm is now open water. As a consequence, 60 percent of Burma’s rice crop was obliterated. Myanmar is no stranger to civil conflict. At the time of Cyclone Nargis’ landfall, a military dictatorship or junta, the State Peace and Development Coun- cil (SPDC), headed by Senior General Than Shwe, held power. Cyclone Nargis and the response of the Myanmar government to international aid revealed what many had known for decades: The regime of Senior General Than Shwe was incompetent, corrupt, and focused on political survival. On the third day after Cyclone Nargis hit, May 5, the BBC reported a death toll of 351 and that the toll was likely higher. In Labutta, a southwestern town- ship, 75 percent of buildings were said to have collapsed. MRTV, a state-owned television station, reported that 222 people were dead in Irrawaddy and 19 were dead in Rangoon. No official cleanup crews existed, and the Burmese embassy in Thailand closed for a (Thai) holiday. A Rangoon trishaw driver questioned, to the Associated Press, “Where are all the uniformed people who are always ready to beat civilians?” On day 4, the official death toll rose to 3,394, with 2,879 missing, which was later increased to 22,000 dead and 41,000 missing. Reports arose of looting in Rangoon from a lack of food and clean water. The European Union called the aftermath a “massive disaster . . . with destruction [of some communities] close to 100 percent.” International aid forces began to assemble as it became clear that the loss of life was enormous, and the response by the ruling junta clearly inadequate. UN Secretary General Ban Ki Moon put a UN Disaster Assessment & Coordina- tion team on standby in Thailand to assist the Burmese government as soon as necessary. The United States released $250,000 of cyclone aid funds and also has a disaster relief team on standby, awaiting permission. The WHO had “of- ficers [who were] on the ground and ready for rapid assessment, surveillance, and mobilization,” including medical teams. The only thing holding all of these groups back was permission from the Burmese government to supply visas and allow the aid to enter the country. Unfortunately, as Jean Maurice Ripert, French

150 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS ambassador to the UN, noted, they were “not able to [deliver aid] because they [wouldn’t] give visas to humanitarian workers.” On day 6, many top leaders in the government disappeared. No responses to world leaders’ condolence messages came in. Most importantly the government did not answer Ban Ki Moon’s calls to discuss aid restrictions. Images of monks helping in cyclone cleanup and relief were banned, while only aid from the SPDC aired on state television. The government asked for direct donations of cash and supplies. All international aid workers still awaited visas. On day 8, the junta proceeded with its long planned constitutional referen- dum, evicting all storm refugees from any polling places. The UN and the U.S. government, among others, had strongly urged the junta not to proceed with the referendum, and to focus on the relief effort. But the generals proceeded, and reported greater than 94 percent voter participation. To no one’s surprise, the referendum passed overwhelmingly. The SPDC rejected international monitors and barred international relief from the delta. The government went out of their way to ensure that all aid went through them. By day 10, the death toll had risen to 31,938 dead with 29,770 missing. It was not until the 16th day after the cyclone that Than Shwe visited show camps. All of the relief supplies still sat perfectly wrapped and unopened. A Myanmar newspaper reported that “the government took prompt action to carry out the relief and rehabilitation work after the storm” despite the differing report from the UN stating that only 20 percent of survivors had received some rudimen- tary aid. The Burmese regime then requested $11.7 billion for rehabilitation and reconstruction with no needs assessment, saying that the first phase of emergency relief was over and that they were moving into the rebuilding phase. Finally, on the 21st day after Cyclone Nargis hit, Than Shwe met with Ban Ki Moon, who had personally come to the country to break the block on assistance, and agreed to allow in aid workers. While it is certainly difficult to ascribe the power and scale of Cyclone Nar- gis to climate change, land use patterns and environmental destruction did likely play more measureable roles in the storm’s impact and loss of life. The Irrawaddy Delta is a very large, low-lying coastal marsh region, once protected from the open sea by dense mangrove forests. Under British rule in the nineteenth cen- tury, the delta was drained, and a century of intensive rice paddy cultivation and population in-migration followed. By the time Nargis hit, the delta was a densely populated region producing more than 50 percent of Burma’s wet rice crop, and the protective mangroves and coastal marshes had been decimated. This exposed rural and remote coastal communities to the full force of the storm, and many communities were washed over in the first, massive storm surge. Military misrule limited the humanitarian response to this natural disaster, but climate change and land use patterns exposed communities and led to enormous losses of life.

APPENDIX A 151 Instability Bias In times of conflict, diseases and health problems do not subside. In fact, as we have discussed, the opposite is true. Research of all kinds, including health research and disease surveillance, however, can markedly decline when conflict arises. This problem, which we have characterized, is known as instability bias and makes it difficult to assess health outcomes related to conflict. During the rule of Mobutu Sese Seko from 1965 to 1997, the Democratic Republic of the Congo or Zaire, as it was known at the time, faced corruption, state violence, and internal conflict. Zaire was also an epicenter of the emergence of HIV/AIDS, and a key country in early efforts to investigate and understand this newly emerging human pathogen. New HIV/AIDS studies in DR Congo peaked from 1986–1988 at 16 studies per year and then started to decline (Fig- ure A3-1). New malaria studies also peaked from 1986–1988 (Figure A3-2). In 1994, Mobutu ordered that international collaborative research stop (Beyrer and Pizer, 2007). Seventeen peer-reviewed publications on HIV/AIDS in the Democratic Re- public of the Congo came out in 1990. In 2002, none were published (Beyrer and Pizer, 2007). Of course, the issue of HIV/AIDS had not been resolved in DRC. Instead, the political unrest within the country halted the research. The example of research in the Democratic Republic of the Congo shows not only the power that conflict can have on controlling the amount of research FIGURE A3-1 Causal loop diagram of Cyclone Nargis. The causal loop diagram illus- trates the relationship between climate change, international and national governance, and conflict in Myanmar in the aftermath of Cyclone Nargis in 2008. SOURCE: Naples, 2011.

152 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS FIGURE A3-2 Bibliometric analysis of HIV publications, Democratic Republic of Congo, 1982–2004. SOURCE: Beyrer and Pizer, 2007. produced, but also the power of the ruling class. Researchers must utilize cre- ative methods such as expanding community engagement practices (Amon et al., 2012). Otherwise, research on issues such as infectious diseases will continue to be sparse on conflict zones. Ways Forward According to an ICE case study, “The Intergovernmental Panel on Climate Change (IPCC) predicts an increase in extreme weather events such as tropical cyclones in the Southeast Asian region. Cyclone Nargis, which struck Myanmar on May 2, 2008, illustrates the potential for extreme weather events to contribute to conflict” (Naples, 2011). In this same case study, the ICE proposed a frame- work that outlines how human agency in the form of climate change can lead to natural disasters and the outcome of conflict (Figure A3-3). Taking into account what we know about the impact of conflict on the spread of infectious disease and what we have learned from Cyclone Nargis and Myanmar, we must begin to rec- ognize human agency and its interactions with global infectious disease threats. To better address this researchers can partner with grassroots organizations and human rights groups in country and internationally. More importantly, part- nering with those we seek to serve facing these complex and overlapping threats and again expanding community engagement practices can provide opportunities for more effective health efforts in conflict zones (Amon et al., 2012). Migration, civil conflicts, and climate change are all likely to be more com- mon, and to interact with the well-being of communities and populations in the

APPENDIX A 153 FIGURE A3-3  Malaria studies initiated, Democratic Republic of Congo, 1980–2004. SOURCE: Beyrer and Pizer, 2007. years and decades to come. Relief efforts must be prepared for complex crises, and new approaches to delivery of relief will likely be required to address these emerging threats. References Amon, J. J., S. D. Baral, C. Beyrer, and N. Kass. 2012. Human rights research and ethics review: protecting individuals or protecting the state? PLoS Medicine 9(10):e1001325. Betsi, N. A., B. G. Koudou, G. Cisse, A. B. Tschannen, A. M. Pignol, Y. Ouattara, Z. Madougou, M. Tanner, and J. Utzinger. 2006. Effect of an armed conflict on human resources and health systems in Cote d’Ivoire: prevention of and care for people with HIV/AIDS. AIDS Care 18(4):356-365. Beyrer, C., and T. Lee. 2008. Responding to infectious diseases in Burma and her border regions. Conflict and Health 2(1):2. Beyrer, C., and H. Pizer. 2007. Civil conflict and health information: The Democratic Republic of Congo. In Public Health & Human Rights: Evidence-Based Approaches. Baltimore: Johns Hopkins University Press. Beyrer, C., S. Baral, and J. Zenilman. 2006. Holmes et al., eds. STDs, HIV/AIDS, and migrant popula- tions. In Sexually transmitted diseases 4th edn. Seattle: McGraw-Hill Professional. Brookings-Bern Project on Internal Displacement. 2008. Human Rights and Natural Disasters: Op- erational Guidelines and Field Manual on Human Rights Protection in Situations of Natural Disaster http://www.refworld.org/docid/49a2b8f72.html (accessed April 1, 2014). Hsiang, S. M., M. Burke, and E. Miguel. 2013. Quantifying the influence of climate on human con- flict. Science 341(6151): DOI: 10.1126/science.1235367

154 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS Lischer, S. K. 2006. Dangerous sanctuaries: Refugee camps, civil war, and the dilemmas of humani- tarian aid. Ithaca, N.Y: Cornell University Press. Mills, E. J., S. Singh, B. D. Nelson, and J. B. Nachega. 2006. The impact of conflict on HIV/AIDS in sub-Saharan Africa. Int J STD AIDS 17(11):713-717. Naples, E. 2011. ICE Case Study 249: Cyclone Nargis, Climate and Conflict. http://www1.american. edu/ted/ice/cyclone-nargis.htm (accessed April 1, 2014). Piarroux, R., R. Barrais, B. Faucher, R. Haus, M. Piarroux, J. Gaudart, R. Magloire, and D. Raoult. 2011. Understanding the cholera epidemic, Haiti. Emerging Infectious Diseases 17(7):1161-1168. Richards, A. K., K. Banek, L. C. Mullany, C. I. Lee, L. Smith, E. K. S. Oo, and T. J. Lee. 2009. Cross- border malaria control for internally displaced persons: observational results from a pilot pro- gramme in eastern Burma/Myanmar. Tropical Medicine & International Health 14(5):512-521. Sollom, R. C. Beyrer, D. Sanders, and A. F. Donaghue. 2009. Health in ruins: A man-made crisis in Zimbabwe. An emergency report by Physicians for Human Rights. Tucker, J. D., G. E. Henderson, T. F. Wang, Y. Y. Huang, W. Parish, S. M. Pan, X. S. Chen, and M. S. Cohen. 2005. Surplus men, sex work, and the spread of HIV in China. AIDS 19(6):539-547. A4 THE IMPORTANCE OF MOVEMENT IN ENVIRONMENTAL CHANGE AND INFECTIOUS DISEASE Nita Bharti11 Abstract Global environmental changes directly impact human movement and mobility, which in turn drive infectious disease dynamics and pathogen transmission. In addition to establishing the importance of movement in disease dynamics, characterizing the mechanistic relationship between envi- ronment, host behavior, and pathogen transmission is becoming increasingly necessary. Environmental systems are diverging from previous patterns while continuing to mediate individual and group movements as well as the complex interactions between population dynamics and disease dynamics. Introduction Movement and Disease The effects of environmental changes on infectious diseases are most often discussed in their direct links to wildlife diseases and vector borne pathogens. However, a closer look into the complexity of infectious disease systems reveals 11  Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA 16802.

APPENDIX A 155 that environmentally driven host movements are a critical element in infectious disease dynamics. Movement and mobility are known to be important underly- ing mechanisms driving the spatiotemporal dynamics of infectious diseases, both within and between populations (Altizer et al., 2011; Bharti et al., 2010; Bradley and Altizer, 2005; Gray et al., 2009; Loehle, 1995; Morgan et al., 2007; Tatem et al., 2009, 2012; Viboud et al., 2006). Examples of environmentally mediated movements include movement motivated by food and water security and seasonal migration patterns. The links between host movement and infectious diseases have been studied in the context of animal migrations (Altizer et al., 2011). Data show that long- distance mass animal migrations can either reduce disease via “migratory escape” (Loehle, 1995) or “migratory culling” (Bradley and Altizer, 2005) or increase disease by creating new or high density contacts at stopovers and destinations (Morgan et al., 2007). In addition to recognizing the importance of mobility, these examples from animal migrations illustrate why it is necessary to develop a mechanistic understanding of the relationship between movement and contact patterns as environmental changes occur. Despite establishing the importance of host movement in infectious disease dynamics (Bharti et al., 2010; Gray et al., 2009; Tatem et al., 2009, 2012; Viboud et al., 2006), many aspects of mobility remain difficult to measure, particularly in humans. Epidemiologically important patterns of movement can remain un- known or poorly understood outside of local knowledge. As a result, it can be challenging to incorporate these movements into public health planning and disease prediction efforts. Improved knowledge and quantification of movement patterns would help in planning and implementing more effective public health interventions (Camargo et al., 2000). We investigate the role of seasonal human movement in pathogen transmis- sion, disease incidence, and immunization programs. Specifically, we investigate measles dynamics in three cities in the West African nation Niger from 1995 to 2005 as an example of an environmentally driven migration impacting pathogen transmission and control in urban areas. We intentionally use an inherently simple disease, measles, to understand the complex dynamics of populations and disease. Population Dynamics and Disease Measles is a strongly immunizing, directly transmitted human disease with no vector, no animal reservoir, and no direct interaction with the environment. Recorded cases of measles in prevaccination industrialized nations are among the richest disease data sets (Fine and Clarkson, 1982). These data demonstrate the highly regular annual and multiennial cycles of measles incidence (Anderson and May, 1991; Cliff et al., 1993). Detailed demographic and measles case records clearly linked population dynamics to disease dynamics; births replenished the susceptibles in the population (Grenfell et al., 2002), the birth rate determined

156 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS the periodicity of outbreaks (Bartlett, 1957), and the aggregation of susceptibles increased contact rates raised transmission rates and triggered measles outbreaks (Fine and Clarkson, 1982). In prevaccination Europe and England this aggrega- tion took place in classrooms, and measles outbreaks were seasonally forced onto school terms with a mean age of infection around 5 years (Fine and Clarkson, 1982). After a vaccine was developed, mathematical analyses showed that the most effective time to vaccinate the population was during the troughs of infection (London and Yorke, 1973; Yorke et al., 1979). This reduced the density of sus- ceptibles prior to the start of school terms, preventing epidemics from taking off. This strategy was successful and became conventional practice in measles immu- nization. Throughout large regions of the world, similar preventative vaccination campaigns targeted during the troughs of infection were extremely effective in achieving and maintaining high levels of coverage and interrupting local chains of transmission (Cliff et al., 1993). Many high-income nations have maintained greater than 90 percent vaccination coverage for decades, locally eliminating measles infections during these periods (Cliff et al., 1993). In particular, the Pan American Health Organization (PAHO) has been widely commended for implementing a highly successful vaccination strategy that fo- cused on age-specific routine vaccinations and catch-up campaigns at regular intervals to keep immunization levels consistently high (Castillo-Chavez et al., 2011). PAHO’s strategy largely eliminated measles in the Americas and was her- alded as an example that would pave the way for measles eradication. The African Health Observatory (AHO) adopted a similar strategy for the African region’s measles control initiative. However, the program has not been as succcessful as it was in the Americas, and measles outbreaks continued to cause morbidity and mortality across the continent (Simons et al., 2012). So why was a strategy highly effective in one place yet failed to produce similar results in another? In contrast to the American region, the African region had a completely different geography along with higher levels of diversity and population movements that were not considered in PAHO’s vaccination strategy but were likely major contributors to its significantly reduced efficacy in the African region. Measles in Niger Today, measles continues to persist across many areas of the globe, but nowhere more than Asia and Africa (The case of measles, 2011; Simons et al., 2012), particularly in places with high birth rates (Bongaarts and Caterline, 2012). Throughout the past decades, Niger put forth significant public health efforts to reduce the national burden of measles. In addition to routine immuniza- tions and catch-up campaigns, the Ministry of Health maintained detailed records of measles cases and vaccine coverage. Despite significant investments, measles epidemics persisted, with the biggest cities at risk for particularly large outbreaks (Ferrari et al., 2008).

APPENDIX A 157 Niger’s measles outbreaks are strongly seasonal, occurring only during the annual dry season. Although the magnitude of outbreaks can vary greatly be- tween years, the timing is extremely consistent (Ferrari et al., 2008). We focus on measles epidemics in the three largest cities in Niger—Niamey, Maradi, and Zinder (Figure A4-1A)—where the seasonal forcing in transmission is stronger than previously observed anywhere else, including prevaccination cities (Ferrari et al., 2010). The strong seasonal forcing causes the outbreaks to subside; epi- demics are not self-limiting due to an exhaustion of susceptible individuals, as is often the case with measles (Cliff et al., 1993; Ferrari et al., 2008, 2010; Grenfell and Bolker, 1998). Despite frequent recurrences, measles is not endemic in Niger, often disap- pearing completely during the rainy season, even from the largest cities (Bartlett, 1957; Bjornstad and Grenfell, 2008; Ferrari et al., 2008; Grenfell and Bolker, 1998). The very high birth rates rapidly replenish the supply of susceptibles, creating the potential for frequent or large outbreaks in the absence of high vac- cination levels (Bartlett, 1957; Ferrari et al., 2008; Grenfell et al., 2002). The median age of measles infection in Niger is around 2 years, which is too young for transmission to be focused in schools (Ferrari et al., 2008). The observed seasonal outbreaks of measles in Niger had also been noted in other parts of the region. The underlying reason, though definitively unknown, was hypothesized to be the result of agricultural labor migrations (Ferrari et al., 2008). During the rainy season in this highly agricultural economy, it is not un- common to disperse to rural areas for agricultural work and then aggregate in ur- ban areas during the dry season (Faulkingham and Thorbahn, 1975; Rain, 1999). Measuring Movement and Interpreting Its Role Epidemiologically important movements within a familiar context may be relatively easy to detect; in western societies this may include weekday work commutes (Viboud et al., 2006), long-distance travel (Tatem et al., 2012), and travel around major holidays. It can be much more problematic to identify movement patterns found only outside of one’s own culture, including different livelihoods and the embedded movement patterns. In this case, inhabitants of western societies may be unfamiliar with nomadic pastoralism (Dyson-Hudson and Dyson-Hudson, 1980) or cyclical regional migration (Byerlee, 1974). In some cases, these types of movements are studied in an ethnographic context with relatively small sample sizes. Careful ethnographic research has detailed seasonal movement patterns for agricultural work in Niger (Faulkingham and Thorbahn, 1975; Rain, 1999). After detection, these movements must be measured and, to explain the observed city-level measles dynamics, the seasonal movements of small groups must be scaled to match large urban areas. However, extracting large-scale data from small-scale samples or scaling down movement data from large data sets, such as a national census, is simply not possible.

158 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS Fortunately, advances in technology and methodology have improved our abilities to measure movement patterns at high spatiotemporal resolution. Vari- ous aspects of human presence can be captured and traced by satellite imagery (Elvidge et al., 1997, 2009; Sutton et al., 1997, 2001), including changes in urban population density and spread (Bharti et al., 2011). Remote Measures of Changing Urban Populations To assess the relationship between changes in population density and measles epidemics, it was necessary to quantify urban populations across seasons. These data could not be extracted from existing ethnographic studies and census data, as mentioned earlier. Other existing data sources, such as composite satellite imagery, often present annually or biannually aggregated information. Commer- cial flight records illustrate high-temporal-resolution movements but track only long-distance movements. Lastly, there are no functioning railways in Niger; movement occurs along roads, but seasonal measurements of road use and traffic are not recorded. To quantify seasonally varying high-resolution spatial changes in popula- tions in these three cities in Niger, we developed a method using noncompos- ited serial nighttime light satellite images. These images capture anthropogenic visible light at night during low moon and cloud-free conditions (Bharti et al., 2011; Elvidge et al., 1997). We created an annual signature of brightness values for each city using 155 images, concurrent with the time period of measles data collection (Bharti et al., 2011; see supporting online materials 1 for method de- tails). We found a consistent, pronounced dip in brightness in each city during the rainy season and a peak during the dry season (Figure A4-1B-D), illustrating that population fluctuations were strongly correlated to the measles transmission curve specific to each city (Figure A4-1B-D) (Bharti et al., 2011; Sutton, 1997). To look more closely at the spatial relationship between brightness and measles cases, we focused on the three communes within the city of Niamey (Figure A4-2A, inset). Daily case records at the commune level from a 2004 out- break showed that the epidemic appeared earliest in the two largest communes, where 90 percent of the cases in the city occurred, and appeared last with the fewest cases in the smallest commune (Bharti et al., 2011; Dubray et al., 2006) (Figure A4-2A). The brightness curves for these three communes displayed a similar pattern: the two largest communes increased and peaked earlier with very high brightness values, and the third commune increased and peaked later with a relatively lower brightness value (Figure A4-2B) (Bharti et al., 2011). A Dynamic Model In simple theoretical models, migration may be thought of as a source of infected individuals. The dynamics of measles epidemics in the cities of Niger

APPENDIX A 159 FIGURE A4-1 Measles transmission rates and brightness for three cities in Niger (adapted from Bharti et al., in prep). Top left: map of Africa, Niger shaded; top right: map of Niger showing three largest cities and national health districts. B. Niamey. Left: annual brightness pattern against day of year shown in open circles. Color corresponds to time (blue to red = January to December); estimated measles transmission rates for biweekly time steps shown in circles connected by dark lines. Right: estimated transmission rates against brightness values; colors correspond to time of year as on left. C. Same as B for Maradi. D. Same as B for Zinder.

160 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS FIGURE A4-2  Measles and brightness in the communes of Niamey (adapted from Bharti et al., 2011). A. Inset: map of city of Niamey showing pixels of the city color-coded by commune. Formal boundaries of each commune are shown with black outlines. Time se- ries of weekly reported measles cases for Niamey’s 2003–2004 outbreak by commune. B. Time series of brightness values, colors by commune as in A. Red arrow indicates onset of measles epidemic in Niamey. C. Left: commune 1, right: commune 2. Points show daily reported measles cases, shading gives central 95 percent of predicted measles incidence from 25,000 model simulations from nighttime lights-informed model (red), no immigra- tion model (blue), and constant immigration model (gray). The x-axis spans the duration of the epidemic: day 307 of 2003 to day 153 of 2004. suggested that migration was also an important source of susceptible individu- als as well as a critical driver of changes in population density and contact rates (Bharti et al., 2011; Ferrari et al., 2008). To determine whether the satellite-derived quantified changes in population density could drive the seasonal forcing of the observed measles epidemics in each commune of Niamey, we adapted an SIR model that included fluctuations in the total population size. Conventional SIR models of measles have included seasonal forcing in the transmission rate, a single rate that encompasses (1) the

APPENDIX A 161 probability of a contact event occurring between an infected and a susceptible person, and (2) the probability of a transmission event occurring, given such a contact (Begon et al., 2002). We separated these two probabilities and assessed them independently. In the communes of Niger, the per capita rate of contacts between susceptible and infected individuals is unlikely to change across sea- sons. Instead, the number of contact events increases with the overall population density. So while more contacts occur during the dry season, the proportional number of contacts between susceptible and infected individuals does not change. Secondly, the probability of transmission, given a contact between a susceptible and infectious individual, does not change between seasons. Measles outbreaks consistently occur with host aggregation and have been historically observed across all seasons (Cliff et al., 1993). Thus, instead of including seasonality in the transmission term, we allowed the total population size to change with the derivative of the brightness curve for each commune (Bharti et al., 2011). In addition to the model with migration informed by commune-level changes in brightness, we fit two additional models for comparison. For each commune, we also fit (1) an SIR model with a constant migration rate, and (2) a model with no migration (static population size) to the daily measles case reports. The model results showed that for communes 1 and 2, where 90 percent of the measles cases within the city occurred, the brightness-informed changes in population size were required to produce the correct rate of increase and decrease of cases as well as the timing and height of the peak of cases (Figure A4-2C) (Bharti et al., 2011). Although commune 3 had far fewer cases, the model with brightness-informed population fluctuations was better than the other two at replicating the correct timing of the peak as well as the increase and decrease in cases (Bharti et al., 2011). The model illustrates that in order to reach the observed height at the peak of each commune’s epidemic, susceptible individuals must be added over the course of the epidemic’s increase. If the population starts with the necessary number of susceptible individuals to sustain the epidemic, the number of cases increases far too quickly and the peak appears much sooner than observed before declining exceptionally more rapidly than that of the recorded outbreaks. In the absence of population fluctuations, the epidemic trajectory in each of Niamey’s three com- munes would look very different (Bharti et al., 2011). Vaccination Often overlooked, migration has a strong impact on health care and immu- nization programs. In several instances, movement and mobility have been de- finitively identified not only as the underlying drivers of spatiotemporal epidemic patterns, but also as important disregarded elements in public health interven- tions. Within São Paulo, Camargo et al. (2000) showed that risk factors related to a 1997 measles epidemic included migration from other states and rural–urban

162 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS migration within the state and determined that movement should be considered when planning a measles vaccination strategy. Relocating also increased the risk that a child would miss a vaccination for polio in India, Angola, and Pakistan (Unicef, 2013). Perhaps most specifically, seasonal migration in Niger was identi- fied as a high-risk factor for children lacking measles vaccination in a 1990–1991 outbreak in Niamey (Malfait et al., 1994). In a place like Niamey, population fluctuations are not only strongly seasonal and pronounced, they are also the mechanism underlying measles outbreaks. This means that the troughs of infection align with troughs in population size and, contrary to conventional wisdom, may not be the most effective time to vaccinate the population. Practiced in Niger, this strategy would vaccinate fewer individuals than would be present in urban areas during other times of the year. This also means that individuals from hard to reach or remote locations are not opportunistically vaccinated when they are easily accessible in urban areas. Planning successful intervention strategies relies heavily on understanding local patterns of mobility. Instead of characterizing populations as static entities that can be described with relatively constant values of size and density, we may benefit from considering them to be more fluid with changing membership. It is possible to use fluctuations in populations as opportunities to immunize or provide access to health care to groups and individuals who might otherwise be difficult to reach. Understanding population fluctuations is also important in estimating the population size at the time of an intervention so that the correct number of vaccine doses can be provided. Previous research has illustrated the merits of regional coordination in infec- tious disease interventions in this area due to common transnational movement patterns (Bharti et al., 2010, 2012). When looking specifically at urban regions, this is an even more valid argument. Seasonal or rural–urban movements are not always contained within a state’s or nation’s borders, and regionally coordi- nated vaccination efforts will reduce the gaps in coverage created by population movements. Conclusion Though perhaps unintentionally, we often consider populations to be rela- tively stable and static in size and density. We know with certainty that this is not only an overly simplistic representation of human populations, it also overlooks the massive impact that movement has on health. This perspective inadvertently inhibits our ability to understand geographically varying important underlying mechanisms of pathogen transmission and epidemic spread as well as access to health care. Understanding the relationship between human movement patterns and dis- eases has presented unique challenges. Although known to be central in disease transmission and spatiotemporal patterns of disease dynamics, epidemiologically

APPENDIX A 163 important patterns of movement can be difficult to identify and measure. In- terdisciplinary research and technological and methodological advances have made immense progress towards enhancing our understanding of movement and mobility in the context of the environment and health. Mobility traces from cell phones (Bengtsson et al., 2011; Gonzalez et al., 2008; Tatem et al., 2009), satel- lite imagery (Bharti et al., 2011; Checchi and Grundy, 2012), and high-resolution aerial photography as well as ground-truthing some of these proxy measures (Min et al., 2013) have already greatly advanced the methods and data behind under- standing populations and their movements across a wide range of geographic areas, environmental settings, and health concerns. Measuring the many aspects of mobility and interpreting their prevalence across spatiotemporal scales is a difficult task, but it is a necessary step towards reducing disease and informing intervention strategies. References Altizer, S., R. Bartel, and B. A. Han. 2011. Animal migrations and infectious disease risk. Science 331:296-302. Anderson, R. M., and R. M. May. 1991. Infectious diseases of humans: Dynamics and control, Oxford science publications. Oxford; New York: Oxford University Press. Bartlett, M. S. 1957. Measles periodicity and community size. Journal of the Royal Statistical Society Series A-General 120(1):48-70. Begon, M., M. Bennett, R. G. Bowers, N. P. French, S. M. Hazel, and J. Turner. 2002. A clarification of transmission terms in host-microparasite models: Numbers, densities and areas. Epidemiology and Infection 129(1):147-153. Bengtsson, L., X. Lu, A. Thorson, R. Garfield, and J. von Schreeb. 2011. Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: A post-earthquake geospatial study in Haiti. PLoS Medicine 8(8):9. Bharti, N., A. Djibo, M. J. Ferrari, R. F. Grais, A. Tatem, C. McCabe, O. N. Bjornstad, and B. Grenfell. 2010. Measles hotspots and epidemiological connectivity. Epidemiology and Infection 138(09):1308-1316. Bharti, N., A. J. Tatem, M. J. Ferrari, R. F. Grais, A. Djibo, and B. T. Grenfell. 2011. Explaining sea- sonal fluctuations of measles in Niger using nighttime lights imagery. Science 334:1424-1427. Bharti, N., H. Broutin, R. F. Grais, M. J. Ferrari, A. Djibo, A. J. Tatem, and B. T. Grenfell. 2012. Spatial dynamics of meningococcal meningitis in Niger: Observed patterns in comparison with measles. Epidemiology and Infection 140(8):1356-1365. Bjornstad, O. N., and B. T. Grenfell. 2008. Hazards, spatial transmission and timing of outbreaks in epidemic metapopulations. Environmental and Ecological Statistics, 15:265-277. Bongaarts, J., and J. Caterline. 2012. Fertility transition: Is Sub-Saharan Africa different? Population and Development Review 38(s1):153-168. Bradley, C. A., and S. Altizer. 2005. Parasites hinder monarch butterfly flight: Implications for disease spread in migratory hosts. Ecology Letters 8:290-300. Byerlee, D. 1974. Rural-urban migration in Africa: Theory, policy and research implications. Inter- national Migration Review 8(4):543-566. Camargo, M. C. C., J. C. De Moraes, V. A. U. F. Souza, M. R. Matos, and C. S. Pannuti. 2000. Predic- tors related to the occurrence of a measles epidemic in the city of Sao Paulo in 1997. Revista Panamericana de Salud Pública 7(6):359-365. The case of measles. 2011. News feature: MacMillan Publishers Limited.

164 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS Castillo-Chavez, C., C. R. Matus, B. Flannery, M. C., G. Tambini, and J. K. Andrus. 2011. The Americas: Paving the road toward global measles eradication. Journal of Infectious Diseases 204(Suppl 1):S270-S278. Checchi, F., and C. Grundy. 2012. Satellite imagery for rapid estimation of displaced populations: A validation and feasability study. Final project report. Cliff, A. D., P. Haggett, and M. Smallman-Raynor. 1993. Measles: An historical geography of a ma- jor human viral disease from global expansion to local retreat, 1840-1990. Oxford [England]; Cambridge, MA: Blackwell. Dubray, C., A. Gervelmeyer, A. Djibo, I. Jeanne, F. Fermon, M. H. Soulier, R. F. Grais, and P. J. Guerin. 2006. Late vaccination reinforcement during a measles epidemic in Niamey, Niger (2003-2004). Vaccine 24(18):3984-3989. Dyson-Hudson, R., and N. Dyson-Hudson. 1980. Nomadic pastoralism. Annual Review of Anthropol- ogy 9:15-61. Elvidge, C. D., K. E. Baugh, E. A. Kihn, H. W. Kroehl, and E. R. Davis. 1997. Mapping city lights with nighttime data from the DMSP Operational Linescan System. Photogrammetric Engineer- ing and Remote Sensing 63(6):727:734. Elvidge, C. D., P. C. Sutton, T. Ghosh, B. T. Tuttle, K. E. Baugh, B. Bhaduri, and E. Bright. 2009. A global poverty map derived from satellite data. Computers & Geosciences 35(8):1652-1660. Faulkingham, R. H., and P. F. Thorbahn. 1975. Population dynamics and drought: A village in Niger. Population Studies 29(3):463-477. Ferrari, M. J., R. F. Grais, N. Bharti, A. J. K. Conlan, O. N. Bjornstad, L. J. Wolfson, P. J. Guerin, A. Djibo, and B. T. Grenfell. 2008. The dynamics of measles in sub-Saharan Africa. Nature 451:679-684. Ferrari, M. J., R. F. Grais, A. Djibo, N. Bharti, C. N. Bjornstad, and B. Grenfell. 2010. Rural-urban gradient in seasonal forcing of measles transmission in Niger. Proceedings of the Royal Society B-Biological Sciences 277(1695):2775-2782. Fine, P. E. M., and J. A. Clarkson. 1982. Measles in England and Wales—I: An analysis of factors underlying seasonal patterns. International Journal of Epidemiology 11(1):5-14. Gonzalez, M. C., C. A. Hidalgo, and A. L. Barabasi. 2008. Understanding individual human mobility patterns. Nature 453:779-782. Gray, R. R., A. J. Tatem, S. Lamers, W. Hou, O. Laeyendecker, D. Serwadda, N. Sewankambo, R. H. Gray, W. Wawer, T. C. Quinn, M. M. Goodenow, and M. Salemi. 2009. Spatial phylodynamics of HIV-1 epidemic emergence in east Africa. AIDS 23(14):F9-F17. Grenfell, B. T., and B. M. Bolker. 1998. Cities and villages: Infection hierarchies in a measles meta- population. Ecology Letters 1(1):63-70. Grenfell, B. T., O. N. Bjornstad, and B. F. Finkenstadt. 2002. Dynamics of measles epidemics: Scaling noise, determinism, and predictability with the TSIR model. Ecological Monographs 72(2):185-202. Loehle, C. 1995. Social barriers to pathogen transmission in wild animal populations. Ecology 76(2):326-335. London, W. P., and J. A. Yorke. 1973. Recurrent outbreaks of measles, chickenpox and mumps. I. Seasonal variation in contact rates. American Journal of Epidemiology 98:453-468. Malfait, P., I. M. Jataou, M. C. Jollet, A. Margot, A. C. Debenoist, and A. Moren. 1994. Measles epidemic in the urban-community of Niamey - Transmission patterns, vaccine efficacy and im- munization strategies, Niger, 1990 to 1991. Pediatric Infectious Disease Journal 13(1):38-45. Min, B., K. M. Gaba, O. F. Sarr, and A. Agalassou. 2013. Detection of rural electrification in Af- rica using DMSP-OLS night lights imagery. International Journal of Remote Sensing 34(22): 8118-8141. Morgan, E. R., G. F. Medley, P. R. Torgerson, B. S. Shaikenov, and E. J. Milner-Gulland. 2007. Parasite transmission in a migratory multiple host system. Ecological Modelling 200:511-520. Rain, D. 1999. Eaters of the dry season: Circular labor migration in the West African Sahel. Boulder, CO: Westview Press.

APPENDIX A 165 Simons, E., M. J. Ferrari, J. Fricks, K. Wannemuehler, A. Anand, A. Burton, and P. Strebel. 2012. Assesment of the 2010 global measles mortality reduction goal: Results from a model of surveil- lance data. Lancet 379(9832):2173-2178. Sutton, P. 1997. Modeling population density with night-time satellite imagery and GIS. Computers, Environment and Urban Systems 21(3-4):227-244. Sutton, P., D. Roberts, C. D. Elvidge, and H. Meij. 1997. A comparison of nighttime satellite imagery and population density of the continental United States. Photogrammetric Engineering and Remote Sensing 63(11):1303-1313. Sutton, P., D. Roberts, C. D. Elvidge, and K. E. Baugh. 2001. Census from heaven: An estimate of the global population using nighttime satellite imagery. International Journal of Remote Sens- ing 22(16):3061-3076. Tatem, A. J., Y. Qui, D. L. Smith, O. Sabot, A. S. Ali, and B. Moonen. 2009. The use of mobile phone data for the estimation of the travel patterns and imported P. falciparum rates among Zanzibar residents. Malaria Journal 8(1):12. Tatem, A. J., Z. Huang, A. Das, Q. Qi, J. Roth, and Y. Qui. 2012. Air travel and vector-borne disease movement. Parasitology 139(14):1816-1830. Unicef. 2013. Polio Info. http://polioinfo.org/ (accessed October 24, 2013). Viboud, C., O. N. Bjornstad, D. L. Smith, L. Simonsen, M. A. Miller, and B. T. Grenfell. 2006. Syn- chrony, waves, and spatial hierarchies in the spread of influenza. Science 312(5772):447-451. Yorke, J. A., N. Nathanson, G. Pianigiani, and J. Martin. 1979. Seasonality and the requirements for perpetuation and eradication of viruses in popluations. American Journal of Epidemiology 109(2):103-123. A5 TOWARD A COUNTY-LEVEL MAP OF TUBERCULOSIS RATES IN THE U.S.12 David Scales,13 John S. Brownstein,13 Kamran Khan,14 and Martin S. Cetron15 Introduction Active tuberculosis (TB) is a reportable communicable disease in all 50 states, but nationwide, county-level data are not released publicly. The CDC’s On- line Tuberculosis Information System (OTIS) provides public surveillance data only by state. Owing to an agreement with the states, the CDC cannot publicly 12  Reprinted from American Journal of Preventive Medicine, 46(5), Scales et al. Toward a county- level map of tuberculosis rates in the U.S. Pp. e49-e51, Copyright 2014, with permission from Elesevier. 13  Children’s Hospital Informatics Program, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts. 14  Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, On- tario, Canada. 15  Division of Global Migration and Quarantine, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia.

166 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS release TB data at the county level, precluding the development of publicly avail- able, county-level maps of TB cases and incidence rates. The lack of a more granular nationwide data set has limited the study of TB trends and socioeconomic risk factors to states (Holtgrave and Crosby, 2004), Metropolitan Statistical Areas (Greenwood and Warriner, 2011), or census tracts within a single state (Myers et al., 2006). A nationwide county-level data set of TB rates provides opportunities to examine TB-related trends across multiple states, metropolitan areas, and across counties with similar demographic char- acteristics, such as the number of people deemed to be at high risk (Cain et al., 2008). Methods TB statistics were generated after extracting publicly available data from state health department websites and requesting public but unpublished county- level data from state TB programs. States providing TB data assented to their use and presentation. The data set, metadata, and sources are published on an interactive map with downloadable data at healthmap.org/tb. TB incidence rates were calculated using 5-year county-level case counts with corresponding (2006–2010) population estimates from the American Com- munity Survey (ACS). Specifically, the total county-level case counts for 2006– 2010 were divided by five to obtain the average number of cases per year. This average was divided by the average population in the county in those 5 years, and finally multiplied by 100,000 to calculate the incidence rate (cases/ 100,000). Therefore, “rates” reported in Table A5-1 and Figure A5-1 represent average an- nual incidence during the 5-year period. Counties were cross-classified by four U.S. regions (Midwest, Northeast, South, and West) and by urban/rural classification (metropolitan [urban area of ≥50,000]; micropolitan [urban area of 10,000–49,999]; and rural), according to the Office of Management and Budget classifications. ANOVA was performed to assess differences in means across these 12 cross-classifications. ANOVAs were examined using Welch two-sample t-tests with Bonferroni adjustment for 42 comparisons (α = 0.0012); significant comparisons were defined as p < 0.0012. Maps were created using ESRI ArcMap, version 10.3, and statistical analyses were performed using R, version 2.14.2. Data were available on a year-by-year basis for 2,892 (92.0%) counties; supra-county health district level for 161 (5.1%) counties; and only multi-year aggregated data for 90 counties (2.9%). Collectively, these data enabled the cre- ation of a U.S. map depicting 5-year average TB incidence rates (Figure A5-1) and a corresponding data set of 3,006 counties for analysis. Henceforth, we use the term “county” to refer collectively to counties; county-equivalents (e.g., bor- oughs); and health districts.

TABLE A5-1  Comparison of Average Annual TB Rates of U.S. Counties and Regions by Urban (Rural/Micropolitan/ Metropolitan) Classification, 2006–2010a Number of counties, county equivalents, and health districts Median annual TB rates per 100,000 by county and urban classification Region Rural Micropolitan Metropolitan Total Region Rural Micropolitan Metropolitan All Classes Midwest 486 218 259 963 Midwest 0 0.805 0.95 0.33 Northeast 41 53 123 217 Northeast 0.52 0.90 1.78 1.10 South 594 297 570 1,461 South 1.67 2.49 2.305 2.16 West 165 82 118 365 West 0 1.28 2.21 1.22 Total 1,286 650 1,070 3,006 All regions 0 1.38 1.78 1.28 Mean annual TB rates per 100,000 by county and urban classification, Annual TB rates per 100,000 by region and urban classification, 2006–2010, M (SD) 2006–2010 Region Rural Micropolitan Metropolitan All Classes Region Rural Micropolitan Metropolitan All Classes Midwest 1.01 (3.06) 1.30 (2.28) 1.26 (1.64) 1.14 (2.57) Midwest 0.95 1.17 2.70 2.32 Northeast 0.68 (0.82) 0.94 (0.67) 2.49 (2.57) 1.77 (2.16) Northeast 0.63 0.99 4.50 4.14 South 3.07 (6.21) 3.72 (5.86) 2.90 (2.70) 3.14 (5.06) South 3.09 3.43 4.60 4.33 West 1.70 (3.95) 1.81 (1.88) 3.33 (3.34) 2.25 (3.46) West 2.33 1.95 5.64 5.31 All regions 2.04 (4.93) 2.44 (4.40) 2.50 (2.66) 2.29 (4.14) All regions 2.20 2.27 4.48 4.11 Significant difference of M pairs from Table 1Cb Between regions National: South versus all other regions (p < 0.0001); Midwest versus all other regions (p < 0.001) Within regions Rural: South versus all other regions (p < 0.001) Northeast: Metro versus rural or micro (p < 0.0001) Micro: South versus all other regions (p < 0.0001); Northeast versus West (p < 0.001) West: Metro versus rural or micro (p < 0.001) Metro: Midwest versus all other regions (p < 0.0001) aRegion and metropolitan/micropolitan classifications follow Office of Management and Budget definitions. Rural counties are those not defined as either metropolitan or micropolitan. bSignificant according to Welch two-sample t-test and Bonferroni adjustment, where α = 0.0012. Number of comparisons = 42. TB, tuberculosis 167

168 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS FIGURE A5-1  Average annual tuberculosis rate per 100,000 population, 2006–2010, by county tuberculosis data from publicly available sources. Population estimates from U.S. Census American Community Survey, 2006–2010. Results More than 600 counties have TB rates above the 2011 national rate of 3.4 cases per 100,000 people (Miramontes et al., 2012). The top 15 counties exceeded a rate of 20 cases per 100,000 (range = 20.9–120.3 cases); nine of these were rural, and eight were in the Southern region. TB case rates were generally high- est in U.S. metropolitan areas; the South had the highest mean and median rates among U.S. regions (Table A5-1). Only the Northeast and West had statistically different mean rates when metropolitan counties were compared with micropoli- tan and rural means. Discussion A publicly available, county-level TB data set enables analysis of TB rates (per 100,000) at the sub-state level. Although TB rates in the U.S. are expected to be high in urban areas (Oren et al., 2011) that have large at-risk foreign-born populations (Liu et al., 2009), certain rural areas also have high TB rates, par- ticularly in Southern states.

APPENDIX A 169 Publicly available county-level TB data can assist TB surveillance and con- trol efforts. TB “hotspots” that cross state borders can be identified. Socio- economic variables can now be tested to identify nationwide trends in at-risk populations for targeted prevention efforts. Thus, we encourage all states to publish county-level TB data online. Additional demographic information distinguishing cases by birth country will help researchers and public health officials understand emerging TB trends. Although TB data should be interpreted within a local context, these data will facilitate more efficient identification of locales where high rates of TB cross state lines, facilitate collaboration between states to jointly target those areas, and al- low health departments to discern regional and nationwide trends. Acknowledgements We gratefully acknowledge the state and county public health departments that contributed to this project by posting data online (Alaska, Alabama, Ari- zona, California, Colorado, Connecticut, District of Columbia, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Kentucky, Louisiana, Maryland, Michigan, Minnesota, Missouri, Mississippi, Montana, North Carolina, Nebraska, New Hampshire, New Jersey, New Mexico, Nevada, New York, Ohio, Oklahoma, Oregon, Pennsylvania, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Utah, Virginia, Washington, Wisconsin, West Virginia, and Wyoming) or providing data via e-mail (Arkansas, Hawaii, Iowa, Kansas, Massachusetts, Maine, North Dakota, and Vermont). We thank Rachel Chorney for her work on the healthmap.org/tb website, for which no direct compensation was received. DS had full access to all study data and takes responsibility for its integrity and the accuracy of the data analysis. Dr. Brownstein is supported by grant R01 LM010812-04 from the National Li- brary of Medicine. This study was funded by the CDC. Dr. Cetron from the Division of Global Migration and Quarantine at the CDC participated as a full scientific collaborator in the investigation; however, the find- ings and conclusions in this report are those of the authors and do not necessarily represent the official position of the CDC. No financial disclosures were reported by the authors of this paper.

170 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS References Cain KP, Benoit SR, Winston CA, Mac Kenzie WR. Tuberculosis among foreign-born persons in the U.S. J Am Med Assoc 2008;300(4):405–12. Greenwood MJ, Warriner WR. Immigrants and the spread of tuberculosis in the U.S.: a hidden cost of immigration. Popul Res Policy Rev 2011;30:839–59. Holtgrave DR, Crosby RA. Social determinants of tuberculosis case rates in the U.S. Am J Prev Med 2004;26(2):159–62. Liu Y, Weinberg MS, Ortega LS, Painter JA, Maloney SA. Overseas screening for tuberculosis in U.S.-bound immigrants and refugees. N Engl J Med 2009;360(23):2406–15. Miramontes R, Pratt R, Price SF, Jeffries C, Navin TR, Oramasionwu GE. Trends in tuberculosis— U.S., 2011. MMWR Morb Mortal Wkly Rep 2012;61(11):181–5. Myers WP, Westenhouse JL, Flood J, Riley LW. An ecological study of tuberculosis transmission in California. Am J Public Health 2006;96(4):685–90. Oren E, Winston CA, Pratt R, Robison VA, Narita M. Epidemiology of urban tuberculosis in the U.S., 2000–2007. Am J Public Health 2011; 101(7):1256–63. A6 ASSESSING THE ORIGIN OF AND POTENTIAL FOR INTERNATIONAL SPREAD OF CHIKUNGUNYA VIRUS FROM THE CARIBBEAN16 Kamran Khan,17 Isaac Bogoch,18 John S. Brownstein,19 Jennifer Miniota,20 Adrian Nicolucci,19 Wei Hu,19 Elaine O. Nsoesie,21 Martin Cetron,22 Maria Isabella Creatore,19 Matthew German,19 and Annelies Wilder-Smith23 16  Originally printed as “Assessing the Origin of and Potential for International Spread of Chikun- gunya Virus from the Caribbean.” PLoS Currents Outbreaks. 2014 Jun 6. Edition 1. doi: 10.1371/ currents.outbreaks.2134a0a7bf37fd8d388181539fea2da5. 17  Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada. 18  Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Can- ada; University Health Network, Divisions of Internal Medicine and Infectious Diseases, Toronto, Canada. 19  Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts, USA. 20  Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, Canada. 21  Children’s Hospital Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, USA; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA; Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, USA. 22  Division of Global Migration and Quarantine, Centers for Disease Control and Prevention, Atlanta, USA; Departments of Medicine and Epidemiology, Emory University School of Medicine and Rollins School of Public Health, Atlanta, USA. 23  Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Institute of Public Health, University of Heidelberg, Germany.

APPENDIX A 171 Abstract Background: For the first time, an outbreak of chikungunya has been reported in the Americas. Locally acquired infections have been confirmed in fourteen Caribbean countries and dependent territories, Guyana and French Guiana, in which a large number of North American travelers vacation. Should some travelers become infected with chikungunya virus, they could potentially introduce it into the United States, where there are competent Aedes mosquito vectors, with the possibility of local transmission. Methods: We analyzed historical data on airline travelers departing areas of the Caribbean and South America, where locally acquired cases of chikungunya have been confirmed as of May 12th, 2014. The final destina- tions of travelers departing these areas between May and July 2012 were determined and overlaid on maps of the reported distribution of Aedes aeygpti and albopictus mosquitoes in the United States, to identify potential areas at risk of autochthonous transmission. Results: The United States alone accounted for 52.1% of the final des- tinations of all international travelers departing chikungunya indigenous areas of the Caribbean between May and July 2012. Cities in the United States with the highest volume of air travelers were New York City, Miami and San Juan (Puerto Rico). Miami and San Juan were high travel-volume cities where Aedes aeygpti or albopictus are reported and where climatic conditions could be suitable for autochthonous transmission. Conclusion: The rapidly evolving outbreak of chikungunya in the Carib- bean poses a growing risk to countries and areas linked by air travel, includ- ing the United States where competent Aedes mosquitoes exist. The risk of chikungunya importation into the United States may be elevated following key travel periods in the spring, when large numbers of North American travelers typically vacation in the Caribbean. Introduction Chikungunya virus is a mosquito-transmitted alphavirus endemic to sub- Saharan Africa and South and East Asia. In recent years, chikungunya has been appearing outside of its endemic zone as a result of increasing international travel (Enserink, 2007; Tomasello and Schlagenhauf, 2013). Concurrently, the geo- graphic ranges of Aedes aeygpti and albopictus—the primary vectors for chikun- gunya virus—have been expanding, a phenomenon thought to be a consequence of climate change and globalization (Reiter et al., 2006). The combination of international travel by potentially infected persons and the increasing geographic availability of competent vectors has set the stage for the introduction and spread

172 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS of Chikungunya to previously unaffected areas. In recent years, autochthonous transmission of chikungunya has occurred in non-endemic areas such as the 2007 outbreak in Italy and 2010 outbreak in France, and most recently, in multiple Caribbean Islands where competent Aedes mosquitoes exist (Tomasello and Schlagenhauf, 2013). The geographic dispersion of chikungunya virus may occur in instances where susceptible travelers in endemic areas are bitten by infected female Aedes mosquitoes (Powers and Logue, 2007). After the typical incubation period of 3-7 days (range 2-12 days), infected individuals become viremic (Borgherini et al., 2007; Sissoko et al., 2008). Among those who develop illness, common symptoms include fever, headache, rash, and severe symmetrical polyarthralgia. The potential for an infected individual to then transmit chikungunya virus to a susceptible Aedes mosquito is greatest during the first 2-6 days of illness, during the viremic phase (Appassakij et al., 2013). For the first time in the Americas, chikungunya was reported among non- travelers on the Caribbean island of St. Martin in December 2013 (CDC, 2013). Since then, locally acquired cases have been reported in multiple countries and territories in the region for a total count of over 4,000 probable or confirmed cases, raising concerns that this virus could spread into and within neighboring areas, including parts of the United States (Gibney et al., 2011; Reiskind et al., 2008). Every year, large numbers of North American tourists vacation in the Carib- bean during spring and summer months. After returning home, these individuals could potentially introduce chikungunya virus into areas where the conditions necessary for autochthonous transmission exist. We used a novel approach com- bining a number of datasets related to travel routes, volumes of travelers, historic temperature data and zoonotic distribution of Aedes mosquitoes in order to model the recent outbreak in the Caribbean and the risk of spread to other countries via international travel. Due to the large travel volume between the Caribbean and the U.S. we conducted an analysis to determine the vulnerability of U.S. cities and states to the importation of chikungunya virus and subsequent local transmission due to favorable environmental conditions. Methods We accessed anonymized, worldwide, passenger-level flight itinerary data for 2012 from the International Air Transport Association (IATA). The IATA dataset represents an estimated 93% of the world’s commercial air traffic at the passenger level. Flight itinerary data includes information on the airport where the traveler initiated their trip, and where relevant, connecting flights leading up to their final destination. Using this dataset, we first analyzed the origins of all air travelers depart- ing chikungunya endemic areas of the world (as defined by the U.S. Centers

APPENDIX A 173 for Disease Control and Prevention, [2014a]) that had final destinations in the Caribbean region (as defined by the United Nations [2014]) during the period from October to December 2012 (to assess potential origins of chikungunya virus introduction into the Caribbean in December 2013). Next, we analyzed the final international destinations of all travelers (be- tween May and July 2012) departing areas of the Caribbean where locally ac- quired cases of chikungunya have been confirmed as of May 12th, 2014 (i.e. Aruba, Anguilla, Antigua, British Virgin Islands, Dominica, Dominican Republic, French Guiana, Guadeloupe, Haiti, Martinique, St. Barthelemy, St. Kitts and Nevis, and St. Martin, Sint Maarten, St. Vincent and the Grenadines). We then calculated the volume of travelers departing these indigenous areas of the Caribbean between May and July 2012 with and their countries of final destination. We also calculated city-level volumes of travelers with final destina- tions in North America. These monthly city-level travel data were mapped and overlaid with the geographic extents of Aedes aeygpti and Aedes albopictus mosquitoes across the United States (CDC, 2014b). We then determined the aver- age monthly temperature of each state between May and July using 60 years of historical data (WeatherBase, 2014). While there are many unknowns regarding the climatic conditions necessary for Aedes aeygpti and albopictus mosquitoes to transmit chikungunya virus (Ruiz-Moreno et al., 2012), an average temperature of 20° Celsius was identified as an important threshold in the 2007 chikungunya outbreak in Italy (Charrel et al., 2008; Fischer et al., 2013; Tilston et al., 2009). Results While the specific origin of the Caribbean chikungunya epidemic is not precisely known, we found that five countries were the source of 84.4% of all international air travelers departing chikungunya endemic areas of the world with final destinations in the Caribbean region between the months of October and December 2012. These countries included South Africa (4,348 travelers; 23.4% of all travelers from chikungunya endemic areas of the world), India (4,012 travelers; 21.6%), China (2,561 travelers; 13.8%), Philippines (2,555 travelers; 13.7%) and the French territory of Réunion (2,218 travelers; 11.9%). With respect to the possibility of receiving an imported case via international air travel, the final destinations of travelers departing areas of the Caribbean where locally acquired cases of chikungunya have been confirmed (as of May 12th, 2014), over the three-month period from May to July 2012 are shown in Table A6-1. Three countries represented the final destinations of 70.0% of all travelers worldwide. The United States, including Puerto Rico, had the strongest links through international air travel (1,071,658 travelers; 52.1% of the global total), followed by France (298,921 travelers; 14.5%), and the Netherlands An- tilles, not including Sint Maarten (68,604 travelers; 3.3%). By comparison, ten cities represented the final destinations of 49.0% of all travelers. These included

174 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS TABLE A6-1  Leading Destination Countries for Travelers Departing Chikungunya Indigenous Areas of the Caribbean Country Traveler Volume* Global Total (%) Cumulative Total (%) United States† 1,071,658 52.2 52.2 France 298,921 14.5 66.7 Netherland Antilles 68,604 3.3 70.0 Canada 64,736 3.2 73.2 Spain 55,329 2.7 75.9 Venezuela 42,774 2.1 78.0 Germany 36,984 1.8 79.8 United Kingdom 28,480 1.4 81.1 Italy 27,159 1.3 82.4 St. Lucia 24,102 1.2 83.6 Panama 23,576 1.2 84.8 *Between May and July 2012. †Includes Puerto Rico. New York (283,224 travelers; 13.8% of the global total), Paris (240,204 travelers; 11.7%), Miami (161,430 travelers; 7.8%), San Juan, Puerto Rico (80,571 travel- ers; 3.9%), Curacao (48,594 travelers; 2.4%), Fort Lauderdale (45,076 travelers; 2.2%), Madrid (41,286 travelers; 2.0%), Boston (40,829 travelers; 1.9%), Toronto (36,162 travelers; 1.7%), and Caracas (29,973 travelers; 1.4%). Discussion Global forces from climate change to surging worldwide air travel are con- tributing to the globalization of vector-borne diseases such as West Nile virus, dengue and chikungunya (Fischer et al., 2013; Greer et al., 2008; Sutherst, 2004; Tatem et al., 2006). In December 2013, chikungunya virus was identified for the first time in the Americas, where it has since caused over four thousand lo- cally acquired cases across numerous Caribbean islands in addition to the South American nations of Guiana and French Guiana. While the origins of chikungu- nya introduction in the Caribbean are not precisely known, molecular diagnos- tics have determined that the strain currently circulating in the region belongs to the subtype CHIKV-JC2012 and closely resembles a strain found in China, the Philippines and Micronesia (Laniciotti and Valadere, 2014). Our analysis suggests that five chikungunya endemic countries account for the vast majority of international air travel into the Caribbean region in the months leading up to the first reported cases, with China and the Philippines accounting for 27.5% of all such travelers. However, the probability of importation into the Caribbean is a function not only of travel volumes but also of chikungunya incidence in the origin countries. Our analyses indicate that the United States is the final destination of over half of all travelers departing chikungunya indigenous areas of the Caribbean,

APPENDIX A 175 followed by France, which accounts for almost 15% of all travelers. The United States has never reported local transmission of chikungunya virus, despite the presence of Aedes aeygpti and albopictus mosquitoes across the southeastern region of the country, while autochthonous transmission of chikungunya has pre- viously been documented in southeastern France in 2010, where Aedes albopictus is known to exist (Vega-Rua et al., 2013). Furthermore, many North American travelers vacationing in the Caribbean will return to areas of the United States where the climate may be suitable for autochthonous transmission. We found that New York City, Miami and San Juan are the leading U.S. des- tination cities of travelers from chikungunya indigenous areas of the Caribbean between May and July. Healthcare providers in these locations should familiarize themselves with the clinical presentation of chikungunya, which overlaps sig- nificantly with dengue fever. The early detection of chikungunya is particularly important in areas such as San Juan, Miami, and Charlotte where competent mos- quito vectors could become infected through bites of viremic travelers (Reiskind et al., 2008). Symptomatic individuals with suspected or confirmed chikungunya infection should take special measures to avoid mosquito bites in the week fol- lowing the onset of their illness (when viremia is greatest) to decrease the poten- tial for autochthonous spread. Although there are many unknowns about the biology of Aedes mosquitoes and the specific climatic conditions that would support autochthonous transmis- sion, warmer weather is thought to shorten the interval between the time when an Aedes mosquito is infected by a viremic patient and when that mosquito can transmit the virus to another susceptible human host (i.e. the extrinsic incubation period) (Liu-Helmersson et al., 2014). Since the 2013-2014 winter season has been unseasonably cool across many parts of the United States, this could favor longer extrinsic periods, and consequently a lower probability of viral transmis- sion from vector to human host. Although belonging to a different strain from the one currently circulating in the Caribbean, of potential concern is the chikun- gunya E1-A226V mutation identified during the 2005-2006 Réunion epidemic, which facilitated more efficient transmission specifically in Aedes albopictus mosquitoes (Schuffenecker et al., 2006; Tsetsarkin et al., 2007). This mutation was subsequently imported to Italy, and has since appeared in China and Papua New Guinea (Bordi et al., 2008; Horwood et al., 2013; Schuffenecker et al., 2006; Wu et al., 2013). However, this mutation does not appear to dominate in the major chikungunya outbreaks that occurred in India 2006-2010 (Kumar et al., 2014). Our analysis has several important limitations. First, we are relying on ac- curate identification of indigenous chikungunya cases in the Caribbean region to conduct our analyses of population movements through air travel. Some countries in the Caribbean may have limited infectious disease surveillance capacity, par- ticularly for a newly emerging pathogen such as chikungunya. Our transportation analysis was also limited to commercial air travel despite the fact that many indi- viduals vacationing in the Caribbean may travel on cruise ships or other means of

176 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS transport. This limitation would presumably lead to an underestimate of travelers arriving in U.S. port cities that face the Caribbean islands, though the length of travel by sea may exclude them spreading disease further. Similarly, we ana- lyzed commercial air travel data from 2012, which may not reflect forthcoming patterns of travel in 2014. While we found a highly consistent seasonal pattern of travel between the United States and chikungunya indigenous areas of the Caribbean in earlier years (analyses not shown), travel behaviors this year could be influenced by evolving news of chikungunya in the media. We also relied on accurate vector surveillance data for Aedes aeygpti and albopictus to identify areas at risk of potential autochthonous transmission. While such vector surveil- lance has limitations, we used contemporary data reported by the U.S. Centers for Disease Control and Prevention as of January 2014 (CDC, 2014b). Finally, the environmental factors necessary to support autochthonous transmission of chikungunya are complex and influenced not only by the type of vector, but also chikungunya virus characteristics. The climatic conditions required for efficient viral transmission are still under investigation; however, it is likely that warmer temperatures are more favorable. Therefore climatic conditions that evolve over the next several months will likely play a significant role in either hindering or supporting autochthonous transmission of chikungunya. At a time when locally acquired cases of dengue (also transmitted by Aedes aeygpti and albopictus mosquitoes) have recently been reported in southern regions of the United States (Adalja et al., 2012; Bouri et al., 2012; Effler et al., 2005; Radke et al., 2012; Ramos et al., 2008), our findings highlight the risk for introduction and potential autochthonous transmission of chikungunya virus in selected areas of the country. The effectiveness and efficiency of interventions to mitigate these risks could be optimized through a combination of public educa- tion, early detection by medical providers, and the strategic use of public health resources in areas of greatest risk. Author Contributions Kamran Khan and Isaac Bogoch jointly developed the design of the study, oversaw the completion of all analyses, and produced the first draft of the manu- script. Jennifer Miniota, Wei Hu, and Adrian Nicolucci conducted reviews of the literature, performed all transportation and spatial analyses, created figures and cartograms, and edited the final version of the manuscript. John Brownstein contributed epidemiological data pertaining to chikungunya in the Caribbean and made significant content contributions and edits to the final manuscript. Marisa Creatore, Martin Cetron and Annelies Wilder-Smith made significant content contributions to the initial draft of the manuscript and edits to the final draft of the manuscript.

FIGURE A6-1  Volume of travelers from chikungunya indigenous areas of the Caribbean* to the United States and Canada in May†. *As of May 12th 2014. 177 †Using historic air travel data from May 2012.

178 FIGURE A6-2  Volume of travelers from chikungunya indigenous areas of the Caribbean* to the United States and Canada in June†. *As of May 12th 2014, †Using historic air travel data from June 2012.

FIGURE A6-3  Volume of travelers from chikungunya indigenous areas of the Caribbean* to the United States and Canada in July†.   *As of May 12th 2014. 179   †Using historic air travel data from July 2012.

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182 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS A7 EIGHT CRITICAL QUESTIONS FOR PANDEMIC PREDICTION Toph Allen,24 Kris Murray,24 Kevin J. Olival,24 and Peter Daszak24 Introduction Like hurricanes or earthquakes, pandemics are rare events that can be ex- tremely devastating, causing substantial mortality and economic damages. How- ever, unlike hurricanes or earthquakes, efforts to identify where pandemics are most likely to originate, and to intervene and preempt their impact, are in their nascence. Here, we review recent advances in disease ecology, virology, and biogeography that move the field towards these goals and pose a series of critical questions that must be addressed to sufficiently improve our predictive capacity. This provides a framework for pandemic prediction that may allow us to better allocate our global resources to mitigate this threat. Because the majority of recent pandemics are zoonotic in origin, most in- volving wildlife reservoirs, we consider this group specifically. The emergence of pandemic zoonoses reflects a complex interplay of socioeconomic, ecological, and biological factors and can be thought of as a three-stage process (Morse et al., 2012). Initially, pathogens with pandemic potential exist only in their natural reservoirs. In the first stage, pre-emergence, our encroachment into a reservoir’s natural habitat, often related to changing land use, may bring these pathogens into contact with livestock or humans or otherwise alter the ecological system in which it and its host exist. In the second stage, localized emergence, initial transmission to humans occurs, directly from a wildlife host or via domesticated animals. Some of these events may involve small chains of person-to-person transmission. When a pathogen achieves sustained person-to-person transmis- sion, the right confluence of circumstances can lead to pandemic emergence, ultimately with large outbreaks propelled internationally by the movement of people and disease vectors. Each of these stages is itself driven by a plethora of socioeconomic, eco- logical, and biological factors (e.g., change in land use, migration, agricultural intensification) that alter pathogen dynamics and expose human populations to increasing risk of zoonotic disease emergence, amplification, and spread. It fol- lows that to predict and pre-empt pandemics, we must improve our understanding of how these factors drive increased risk of each stage of the pandemic process (Morse et al., 2012). The complexity of these processes is daunting, but the in- terplay of ecology, demography, virology, and biology provides a wide range of new tools and approaches that can be used in pandemic prediction and prevention. 24  EcoHealth Alliance, 460 West 34th Street, New York, USA.

APPENDIX A 183 These include strategies to analyze prior outbreaks, model future trends in pan- demic drivers, conduct targeted surveillance in wildlife and human populations, and probe the depth of the zoonotic “pool” from which novel EIDs arise. Here we review some of these by posing eight critical questions for pandemic prediction. Eight Critical Questions for Pandemic Prediction Are Emerging Infectious Diseases (EIDs) Really on the Rise? The literature on emerging infectious diseases, and concern among policy makers and the public, has grown substantially in recent years (IOM, 1992, 2003). Does this reflect a public health threat that is also growing, or is this trend driven by increased surveillance, or simply better reporting of outbreaks as they occur? To test this, we expanded and updated a database of all known emerging infectious disease, first collated by Mark Woolhouse’s group (Taylor et al., 2001). We focused on “EID events,” which we defined as “the first temporal emergence of a pathogen in a human population . . . related to the increase in distribution, increase in incidence or increase in virulence or other factor which led to that pathogen being classed as an emerging disease” (Jones et al., 2008). For each event, we collected data on location, time, and host and/or vector, as well as on associated ecological, biological, and sociodemographic drivers of disease emergence, and performed a number of temporal and spatial regression analyses. Our analyses showed that the number of EID events has increased over time, peaking in the 1980–1990 decade. This peak was associated with increased sus- ceptibility to infection due to the HIV/AIDS pandemic. Like Taylor et al. (2001), we found that zoonoses comprised the majority of EID events (60.3 percent), and that almost 71.8 percent of zoonotic EIDs were from wildlife (43.3 percent of all EID events). Furthermore, zoonoses from wildlife were increasing as a propor- tion of all EID events—in the last decade analyzed (1990–2000), 52.0 percent of EID events were zoonoses with known a wildlife origin. We attempted to correct for increasing infectious disease reporting effort over time by including in our regression model the number of articles published in the Journal of Infectious Diseases (which gives a crude measure of research effort for infectious diseases generally, not just EIDs) for each decade as an offset. Controlling for reporting effort gave further support to the conclusions that EID events are becoming more common, that zoonoses comprise the majority of EID events, and that zoonoses are rising significantly faster as a proportion of all EID events. Are There Predictable Patterns to Disease Emergence? The first step in predicting a biological phenomenon is to look for patterns that underlie previous events. This approach underpins hurricane forecasting and the identification of earthquake zones, and is a logical strategy for pandemic

184 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS prediction. Both hurricane forecasting and the identification of earthquake zones look to the underlying drivers of these phenomena to identify patterns. We used a similar approach for disease emergence, focusing on the hypothesized drivers of zoonotic disease emergence. We assigned geographic coordinates to EID events and, using a logistic regression, tested associations between subsets of EID events and a small selection of hypothesized drivers. We found that drug-resistant and vector-borne pathogens, and zoonoses with wildlife and non-wildlife origins, differed in their global patterning and in their associations with different drivers. In particular, all categories of EID events were strongly associated with human population density, which we have suggested “. . . supports previous hypotheses that disease emergence is largely a product of anthropogenic and demographic changes. . . .” Human population growth—taken as a broad proxy for change in socio-economic factors—predicts zoonoses from non-wildlife and the emergence of drug-resistant pathogens. However, zoonoses from wildlife are alone in their association with wildlife host species richness—patterns of wildlife diversity. The overall predicted risk from different categories was differentially distributed across the globe. For instance, wildlife zoonoses and vector-borne pathogens were more likely to have originated in lower-latitude, developing countries (Jones et al., 2008) (Figure A7-1). Describing these patterns provides the first step towards pandemic prediction: predictive models exist of future trends in socio- economic and demographic drivers, and may be used to derive predictive models of the future trends in disease emergence. The analyses of Jones et al. (2008) show that EID emergence is driven by socioeconomic as well as biological factors, but they are somewhat preliminary, and substantial gaps remain. For example, what aspects of human population density drive disease emergence? Is it anthropogenic environmental changes (e.g., road building, deforestation, land use change)? Is it increased contact with wildlife, or the perturbation of pathogen transmission dynamics in wildlife? Or do dense human populations simply provide an “amplification zone” that allows more frequent recognition of new EIDs otherwise lost to our analyses? Efforts to tease apart the mechanisms underlying these patterns will involve ecological, vi- rological, and biological disciplines collaborating in exciting new ways (Murray and Daszak, 2013). Finally, it is interesting to note that the Jones et al. (2008) models leave 85 percent of the variation in global patterns of disease emergence unexplained. This emphasizes the magnitude of the problem, sets the bar for fu- ture studies, and highlights that efforts to gradually improve the model’s power need to be prioritized if we are to accurately predict the next pandemic. Where Will the Next Pandemic Originate? There are significant geopolitical and logistical constraints to pandemic prevention. Newly emerged pathogens often originate in remote areas that are difficult to access, and in resource-constrained countries that cannot afford to

FIGURE A7-1  Map of relative risk of a zoonotic disease of wildlife origin emerging in people. Because almost all prior pandemics, and the majority of emerging infectious diseases, are zoonotic in origin, with the majority of these having a wildlife host, this map acts as a potential basis for future targeted surveillance and the pre-empting of potential pandemics. 185

186 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS systematically identify novel pathogens in their early stages of emergence. Once emerging diseases become pandemic, the large number of cases and wide geo- graphic distribution make response programs costly and complicated by geopo- litical issues. Given the finite global capacity for pandemic preparedness, and a limited global budget, can we reconfigure where our resources are spent, based on a scientific understanding of where novel diseases emerge and where our current effort is lacking in relation? To this end, our previous “EID hotspots” analysis attempts to correct for bias caused by this unequal distribution of surveillance resources and to make recommendations about where surveillance should be increased in response to predicted disease emergence risk. We can draw two conclusions from this work. First, reporting effort significantly influences where we observe EID events. This implies that EID events of a similar scale are occurring, unobserved, in locations with weaker disease reporting infrastructure. Second, reporting infrastructure is stronger in developed countries, in northern latitudes, whereas wildlife zoonoses more commonly emerge in lower latitudes, in countries with weaker reporting effort. The implications are that our resources to rapidly identify novel EIDs poorly match their likely occurrence, and that this can be remedied by improving infrastructure in EID hotspot developing countries to identify pathogens spilling over from wildlife into people. It is important to note that our analysis, while suggestive, is preliminary. Reporting effort is likely more collinear with population density than a country- level measure can show, and the Journal of Infectious Diseases may not be the most accurate measure of where infectious disease reporting is strongest. Con- structing higher-fidelity maps of infectious disease reporting effort would allow us to better correct for the lens through which we view disease emergence and identify areas with the greatest need for increased surveillance. Furthermore, the EID hotspot maps are relevant only at large spatial scales. New approaches are needed to identify where, within a region, country, or landscape, the highest risk of a new disease originating exists. One approach is to conduct targeted surveil- lance efforts at specific wildlife–human interfaces such as people living in remote villages close to forests in EID hotspots, or people engaged in hunting bushmeat, producing livestock, selling live animals in markets, or butchering them in abat- toirs or restaurants. Better analysis of the spatial distribution and relative risk of these interfaces is likely to be a productive research line. Finally, with the grow- ing availability of “big data,” increasing ease by which it can be manipulated and analyzed, and new models that predict future trends in the underlying drivers of EIDs, hotspot models will become more rigorous, accurate, and based on con- crete hypotheses about biological mechanisms. How Many Unknown Pathogens Are There? The perfect pandemic prevention program would prevent spillover of patho- gens from wildlife to human hosts before they have the opportunity of infecting

APPENDIX A 187 people, amplifying their transmission, and becoming pandemic. This approach is theoretically possible. If we target surveillance of wildlife to EID hotspot coun- tries and conduct pathogen discovery in these species, we can identify pathogens with pandemic potential before they emerge and target prevention efforts to block their spillover. This is the basis for a number of new programs, includ- ing the USAID Emerging Pandemic Threat (EPT) program (Morse et al., 2012) and research programs that target pathogen discovery in bats and other zoonotic disease reservoirs (Drexler et al., 2012; Marsh et al., 2012; Wacharapluesadee et al., 2013). However, even when we have narrowed down interfaces and locales of inter- est, two significant challenges remain. Firstly, the diversity of unknown pathogens may be so high that it is not cost effective to identify them all. Indeed, until recently there was no systematic attempt to predict the unknown viral diversity in any single species, let alone all wildlife. Using samples collected and tested through the USAID EPT PREDICT program, we have recently published the first attempt at a strategy to estimate unknown viral diversity. We did this using incidence-based species richness estimators, which have their origin in the “mark- recapture” modeling approach used by conservation biologists to estimate the density of rare animals in a patch of land. In this method, animals are captured, tagged, and released, and the number of recaptures of tagged individuals relative to the number of untagged individuals gives a way to statistically predict the total number of individuals in a region. For pathogen discovery, we repeatedly sampled a large population of Pteropus giganteus, a bat species known to carry zoonotic viruses, collecting high-quality samples from around 2,000 unique indi- vidual bats. We then used degenerate viral family-level primers (12,793 separate consensus PCR assays) to discover 55 viruses from nine viral families known to harbor zoonoses (Anthony et al., 2013). We then used statistical approaches to estimate the total viral richness of these nine families in this single species. Our analysis suggests that this bat species harbors 58 viruses (i.e., 3 not yet discov- ered) in these viral families, and if this is extrapolated simplistically to all 5,517 known mammal species, we estimate that there are at least 320,000 mammalian viruses awaiting discovery in these nine viral families. This is a large number, but using the PREDICT program costs of field and lab work, we estimate the cost to uncover 100 percent of virodiversity in this critical group of wildlife reservoirs to be $6.8 billion, and to uncover 85 percent of virodiversity to be only $1.4 billion, considering the exponentially diminishing returns of continued sampling. The latter figure is less than the cost of a single SARS-scale pandemic and, if spread over a decade, a small portion of current global pandemic prevention spending. Which Wildlife Species Harbor the Next Pandemic Pathogen? The second challenge to wildlife pathogen discovery as a pandemic pre- vention strategy is knowing which wildlife are the highest-risk reservoirs (i.e., which species to sample so that we can maximize the discovery of pathogens

188 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS with zoonotic, and pandemic, potential). Species differ in the composition of their viral diversity and in the propensity of those pathogens to infect people, but the genetic, behavioral, and ecological rules that underpin these relationships are poorly understood (Bogich et al., 2012b). A recent analysis of the literature found that sampling effort, IUCN threat status, and population genetic structure of bat species were the best predictors of how many viral species they harbored, inde- pendent of their phylogenetic relationships (Turmelle and Olival, 2009). Among mammal groups, rodents and bats host a particularly large number of zoonotic pathogens: Rodents have a larger diversity, while bats host more per species (Luis et al., 2013). Within bat and rodent species, those with greater sympatry (range overlap) with other related species host more viral diversity, and bats with smaller litters, greater longevity, and more litters per year tended to host more zoonoses. These are tantalizing glimpses of ecological and evolutionary patterns that likely drive viral speciation and zoonotic risk, and may ultimately inform which species we target for viral discovery. However, there is much more to learn. For example, a logical assumption is that viruses are more able to infect more closely related species, due to the sharing of host cell receptors, for example. Thus, mammals are the source of the majority of zoonotic EIDs (Jones et al., 2008; Taylor et al., 2001) and across all mammal-virus associations, more closely related mammals are more likely to share virus species (Bogich et al., 2012b). However, when two unrelated species have extensive, intimate contact over long periods of time (e.g., humans and domesticated mammals), does this phylogenetic rule still hold? If we continue to expand the wildlife trade, bringing more diverse animals from differ- ent regions into close contact with people, will we see pathogens emerging that would normally have difficulty successfully infecting humans? Can We Predict the Pandemic Potential of a Newly Discovered Pathogen? With targeted improvements in public health infrastructure and surveil- lance for pathogen discovery, we can increase our odds of catching a zoonotic outbreak in its nascence or discovering novel pathogens of pandemic potential. But will we be able to identify which ones, out of the hundreds of thousands of new species of virus to be discovered in wildlife, will be able to infect humans? With most of these potential zoonoses being identified by only a short RNA or DNA sequence, is there a logical strategy to identify their potential pandemicity? Identifying which novel pathogens in a wildlife species are most likely able to infect, replicate in, cause cycles of human-to-human infection, and then amplify into pandemics remains one of the biggest challenges to pandemic prevention. Morse et al. (2012) reviewed some of the known factors that affect whether a particular virus can infect a species and what gaps remain. In some pathogens, receptor specificity and other biological characteristics may be used to predict host range and potential pathogenicity to humans. However, animal models, hu- man cell cultures, and similar methods cannot empirically validate a pathogen’s

APPENDIX A 189 capacity to infect humans. Some characteristics that may yield improvements in our predictive ability include the effects of host relatedness, relatedness of a virus to known human viruses, host range and evolutionary capacity, and predictive capacity of virulence in humans (some pathogens can infect humans but cause no disease, whereas others cause severe illness) (Morse et al., 2012). As we work towards a better understanding of these factors, we can use a few simple heuristics to prioritize certain pathogens. Certainly, if a pathogen exists at a zoonotic interface, and if there has been documented human infection, it should be prioritized. Pathogens that cause small chains of human-to-human infection with a basic reproductive number (R0) approaching or higher than 1 should be considered “prime epidemics in waiting,” as small evolutionary changes could boost their transmissibility and enable them to cause epidemics. In fact, though none of the models outlined above can tell us exactly how dangerous a pathogen is, they all contribute valuable information to a risk assess- ment. Whether a pathogen exists at an interface of interest, how closely related it is to known human pathogens, how closely related to humans its reservoir host is, and various viral traits all convey information about a particular pathogen. Future work may involve testing the zoonotic potential of wildlife pathogens by sequencing receptor binding domains, producing pseudo-type viruses with these proteins expressed, and conducting binding assays, in vitro culture assays, and ultimately animal infections with transgenic animals that express human cell sur- face receptors. This work has already shown the capacity to identify high-priority potential zoonoses for SARS-like viruses in bats, which bind to human, civet, and bat ACE2 (Ge et al., 2013). Can We Predict How, and to Where, a New EID Will Spread? The emergence of triple reassortant A/H1N1 influenza in 2009 highlighted how rapidly diseases can spread once they have achieved capacity for effective human-to-human transmission. Targeting these diseases may be effective if we can accurately predict their likely pattern of spread out of a region and strategi- cally allocate resources to respond. Analyses of travel and trade data have shown that predicting spread is relatively straightforward and can provide accurate estimates of spread and case numbers when applied to prior outbreaks (e.g., of SARS (Hufnagel et al., 2004) and A/H1N1 influenza (Hosseini et al., 2010)). This approach has been used to analyze recent historical spread of vectors through shipping trade, and their likely routes of spread via air travel (Tatem, 2009; Tatem et al., 2006a,b). It has also been used to predict the spread of ongoing emergence events such as the MERS-CoV outbreak in Saudi Arabia (Khan et al., 2010). It has particular relevance in zoonotic disease spread when patterns of wildlife mi- gration and trade are implicated, and where policy can be rapidly set to prevent importation. This approach has been used to examine the likely cause of past spreading events for A/H5N1 influenza and to predict and set policy for its likely

190 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS route of introduction to the New World (Kilpatrick et al., 2006a). Finally, it has been used effectively in Hawaii and the Galapagos Islands to allocate resources to reduce the risk of West Nile virus introduction via the most likely pathway of mosquitoes transported via air travel (Kilpatrick, 2011; Kilpatrick et al., 2004, 2006b). As in all predictive models, their rigor improves as the quality of data on travel and trade pathways and volumes, on biological characteristics of patho- gen and host, and on the human contact networks that allow transmission also improves. For example, the capacity and willingness of countries to identify and report outbreaks early are critical to make accurate predictions about spread, once a pandemic has begun. Analyses of the spread of the 2009 H1N1 influenza showed that two key factors influenced the pandemic’s arrival time—a country’s global accessibility through air travel, and the percentage of GDP per capita spent on health care (a proxy for testing and reporting capacity) (Hosseini et al., 2010). Again, gaps in this approach remain, including the need for a better understanding of the role of intra-country human movement in disease spread. Newly available datasets on road infrastructure, migration, and human network connectivity will increasingly illuminate this area. Can We Eventually Stop Pandemics from Emerging? The new approaches described above to identifying novel pathogens in emerging disease hotspots, and predicting their pandemic potential and likely spread, have likely improved our global pandemic preparedness. But what prog- ress has been made in using this approach to prevent pandemics? One significant shift is in the way pandemic prevention programs are funded and managed. Traditionally, outbreak threats were dealt with by state and national agencies, the World Health Organization, and field laboratory networks funded through these programs. The emergence of H5N1 influenza via small-scale outbreaks, which suggested chronic persistence in backyard poultry farms, led to calls for a “systems approach” to the pandemic prevention (Bogich et al., 2012a), and a cross-sectorial “One Health” collaboration of animal health, public health, and environmental agencies (FAO et al., 2008; Karesh, 2009; Zinsstag et al., 2011). International development agencies, which had been trending towards specialized programs to target specific infectious diseases, are now actively involved in this systems approach to pandemic prevention. This involves funding for crucial in- frastructure investments required for pandemic prevention, and a specific focus on collaborative One Health programs (Bogich et al., 2012a). With most EID events occurring in regions that are under-resourced in public health capacities, disease- based programs for AIDS, malaria, TB, and polio do not address the underlying flaws in public health systems that predispose locations to outbreaks of emerging infectious diseases (Standley and Bogich, 2013). Standley and Bogich (2013) propose an “ecohealth” approach, addressing destructive land use change and biodiversity loss in places like China, Brazil, and India. This approach defines how we can deal with pandemics as distinct

APPENDIX A 191 from dealing with hurricanes or earthquakes: by identifying and mitigating the underlying causes, particularly anthropogenic activities that promote pathogen spillover, amplification, and spread. Strategies include programs that educate and promote alternatives to high pandemic risk behavior like the trading, butchering, and consumption of wild animals, or the comingling of livestock and wildlife on farms. They also include more fundamental approaches that address large-scale anthropogenic changes. For example, 43 percent of past EID events are attribut- able to land use change and agricultural changes, including extractive industries (timber/logging, oil and gas, mining, and plantations). The economic impact of EIDs from land use change is estimated to be $10–40 billion over the next 10 years, which could be considered a potential liability to extractive industries. Industrialized mining and plantation operations in EID hotspot countries are likely to be on the front line of disease outbreaks, and are often under pressure to improve their environmental impacts. Programs that better quantify the risk of novel pathogens to these industries, and the economic damages they might entail, may become valuable in mitigating their impact on global health, conservation, and the environment (Murray and Daszak, 2013). The two-fold approach of treating emerging pandemics as targets for interna- tional development programs and as byproducts of economic activity is relatively new and suggests that long-term solutions to their emergence can be found. A future without pandemics may be possible, but only with the very best interdisci- plinary science, ambitious approaches to risk prediction, and bold strategies taken by industry and development agencies to ensure against them. References Anthony, S. J., J. H. Epstein, K. A. Murray, I. Navarrete-Macias, C. M. Zambrana-Torrelio, A. Solovyov, R. Ojeda-Flores, N. C. Arrigo, A. Islam, S. Ali Khan, P. Hosseini, T. L. Bogich, K. J. Olival, M. D. Sanchez-Leon, W. B. Karesh, T. Goldstein, S. P. Luby, S. S. Morse, J. A. Mazet, P. Daszak, and W. I. Lipkin. 2013. A strategy to estimate unknown viral diversity in mammals. Mbio 4(5). Bogich, T. L., R. Chunara, D. Scales, E. Chan, L. C. Pinheiro, A. A. Chmura, D. Carroll, P. Daszak, and J. S. Brownstein. 2012a. Preventing pandemics via international development: A systems approach. PLoS Medicine 9(12). Bogich, T. L., K. J. Olival, P. R. Hosseini, E. Loh, S. Funk, I. L. Brito, J. H. Epstein, J. S. Brownstein, D. O. Joly, M. A. Levy, K. E. Jones, S. S. Morse, A. A. Aguirre, W. B. Karesh, J. A. K. Mazet, and P. Daszak. 2012b. Using mathematical models in a unified approach to predicting the next emerging infectious disease. In New directions in conservation medicine, edited by A. A. Aguirre, R. S. Ostfeld, and P. Daszak. New York: Oxford University Press. Pp. 607-618. Drexler, J. F., V. M. Corman, M. A. Müller, G. D. Maganga, P. Vallo, T. Binger, F. Gloza-Rausch, A. Rasche, S. Yordanov, and A. Seebens. 2012. Bats host major mammalian paramyxoviruses. Nature Communications 3:796. FAO, OIE, WHO, UNSIC, UNCF, and WHO. 2008. Contributing to One World, One Health: A strategic framework for reducing risks of infectious diseases at the animal-human-ecosystems interface. Rome: Food and Agriculture Organization; World Organisation for Animal Health; World Health Organization; United Nations System Influenza Coordinator; United Nations Children’s Fund; World Bank.

192 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS Ge, X.-Y., J.-L. Li, X.-L. Yang, A. A. Chmura, G. Zhu, J. H. Epstein, J. K. Mazet, B. Hu, W. Zhang, C. Peng, Y.-J. Zhang, C.-M. Luo, B. Tan, N. Wang, Y. Zhu, G. Crameri, S.-Y. Zhang, L.-F. Wang, P. Daszak, and Z.-L. Shi. 2013. Isolation and characterization of a bat SARS-like Coronavirus that uses the ACE2 receptor. Nature 503:535-538. Hosseini, P., S. H. Sokolow, K. J. Vandegrift, A. M. Kilpatrick, and P. Daszak. 2010. Predictive power of air travel and socio-economic data for early pandemic spread. PLoS ONE 5(9):e12763. Hufnagel, L., D. Brockmann, and T. Geisel. 2004. Forecast and control of epidemics in a global- ized world. Proceedings of the National Academy of Sciences of the United States of America 101(42):15124-15129. IOM (Institute of Medicine). 1992. Emerging infections: Microbial threats to health in the United States. Washington, DC: National Academy Press. IOM. 2003. Microbial threats to health: Emergence, detection, and response. Washington, DC: The National Academies Press. Jones, K. E., N. Patel, M. Levy, A. Storeygard, D. Balk, J. L. Gittleman, and P. Daszak. 2008. Global trends in emerging infectious diseases. Nature 451:990-994. Karesh, W. B. 2009. One world—One health. Clinical Medicine 9(3):259-260. Khan, K., Z. A. Memish, A. Chabbra, J. Liauw, W. Hu, D. A. Janes, J. Sears, J. Arino, M. Macdonald, F. Calderon, P. Raposo, C. Heidebrecht, J. Wang, A. Chan, J. Brownstein, and M. Gardam. 2010. Global public health implications of a mass gathering in Mecca, Saudi Arabia, during the midst of an influenza pandemic. Journal of Travel Medicine 17(2):75-81. Kilpatrick, A. M. 2011. Globalization, land use, and the invasion of West Nile virus. Science 334(6054):323-327. Kilpatrick, A. M., Y. Gluzberg, J. Burgett, and P. Daszak. 2004. A quantitative risk assessment of the pathways by which West Nile virus could reach Hawaii. Ecohealth 1:205-209. Kilpatrick, A. M., A. A. Chmura, D. W. Gibbons, R. C. Fleischer, P. P. Marra, and P. Daszak. 2006a. Predicting the global spread of H5N1 avian influenza. Proceedings of the National Academy of Sciences of the United States of America 103:19368-19373. Kilpatrick, A. M., P. Daszak, S. J. Goodman, H. Rogg, L. D. Kramer, V. Cedeno, and A. A. Cunningham. 2006b. Predicting pathogen introduction: West Nile virus spread to Galapagos. Conservation Biology 20(4):1224-1231. Luis, A. D., D. T. S. Hayman, T. J. O’Shea, P. M. Cryan, A. T. Gilbert, J. R. C. Pulliam, J. N. Mills, M. E. Timonin, C. K. R. Willis, A. A. Cunningham, A. R. Fooks, C. E. Rupprecht, J. L. N. Wood, and C. T. Webb. 2013. A comparison of bats and rodents as reservoirs of zoonotic viruses: Are bats special? Proceedings of the Royal Society B-Biological Sciences 280(1756). Marsh, G. A., C. de Jong, J. A. Barr, M. Tachedjian, C. Smith, D. Middleton, M. Yu, S. Todd, A. J. Foord, V. Haring, J. Payne, R. Robinson, I. Broz, G. Crameri, H. E. Field, and L. F. Wang. 2012. Cedar virus: A novel henipavirus isolated from Australian bats. PLoS Pathogens 8(8). Morse, S. S., J. A. K. Mazet, M. Woolhouse, C. R. Parrish, D. Carroll, W. B. Karesh, C. Zambrana- Torrelio, W. I. Lipkin, and P. Daszak. 2012. Prediction and prevention of the next pandemic zoonosis. Lancet 380:1956-1965. Murray, K. A., and P. Daszak. 2013. Human ecology in pathogenic landscapes: Two hypotheses on how land use change drives viral emergence. Current Opinion in Virology 3(1):79-83. Standley, C. J., and T. L. Bogich. 2013. International development, emerging diseases, and eco-health. Ecohealth 10:1-3. Tatem, A. J. 2009. The worldwide airline network and the dispersal of exotic species: 2007-2010. Ecography 34:94-102. Tatem, A. J., S. I. Hay, and D. J. Rogers. 2006a. Global traffic and disease vector dispersal. Proceed- ings of the National Academy of Sciences of the United States of America 103(16):6242-6247. Tatem, A. J., D. J. Rogers, and S. I. Hay. 2006b. Estimating the malaria risk of African mosquito movement by air travel. Malaria Journal 5. Taylor, L. H., S. M. Latham, and M. E. J. Woolhouse. 2001. Risk factors for human disease emer- gence. Philosophical Transactions of the Royal Society B-Biological Sciences 356:983-989.

APPENDIX A 193 Turmelle, A. S., and K. J. Olival. 2009. Correlates of viral richness in bats (Order Chiroptera). Eco- health 6(4):522-539. Wacharapluesadee, S., C. Sintunawa, T. Kaewpom, K. Khongnomnan, K. J. Olival, J. H. Epstein, A. Rodpan, P. Sangsri, N. Intarut, A. Chindamporn, K. Suksawa, and T. Hemachudha. 2013. Iden- tification of group C betacoronavirus from bat guano fertilizer, Thailand. Emerging Infectious Diseases [Internet](August 2013). Zinsstag, J., E. Schelling, D. Waltner-Toews, and M. Tanner. 2011. From “one medicine” to “one health” and systemic approaches to health and well-being. Preventive Veterinary Medicine 101(3-4):148-156. A8 MISCONCEPTIONS AND EMERGING PATHOGENS: WHAT CAN MATHEMATICAL MODELS TELL US? Andrew Dobson25 The last 25 years have seen a renaissance in the use of mathematical models in epidemiology; much of this is largely due to the influence of An- derson and May and their colleagues (Anderson and May, 1992; Grenfell and Dobson, 1994, 1995). The transformation came about as the models they developed were based upon empirical assumptions. This allowed the whole discipline to move from an overt fascination with mathematical elegance, to embrace data and become the pragmatic powerhouse that is at the center of quantitative insight to any modern epidemiological problem. At first glance, this creates problems for the use of these models in studies of emerging dis- eases, as almost by definition, there will be no data prior to emergence. None- theless, all of the recent major studies of disease emergence have quickly led to the almost obligatory use of mathematical models in infectious disease biology. A nice index of this was the chance remark by the editor of one major journal during a recent influenza outbreak, “Half the world is wor- ried about this new pathogen—while we’re facing an epidemic of submitted papers, all claiming to have produced the definitive predictive model for it!” In this short overview, I will take a brief personal and idiosyncratic review of the key ways in which mathematical models have been used, misused, or could potentially be used to provide insights into the dynamics of emerging pathogens. I will offer no specific recommendations or recipes for the “best way” to use models to understand pathogen emergence. This is partly because different model structures will provide different insights to different pathogens; moreover, each new emergence usually leads to the development of new mathematical tricks, techniques, and approaches that 25  Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544.

194 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS provide powerful new tools for the current crisis and often retrospective insights into older emergences. Dynamics of Initial Cross-Over A huge number of pathogens are circulating in all free-living species of animals and plants. One of the most profound testimonies to the shortsighted- ness of scientific exploration is that we know neither how many other species share the planet with us, nor how many are pathogens or parasites of the more apparent and better classified free-living species (Dobson et al., 2008). The most conservative estimate is that 50 percent of species are parasitic, but it could be significantly higher, potentially larger than 90 percent. Although a huge num- ber of pathogens could potentially colonize humans (or domestic livestock and crops), only a relatively small proportion seem to have done so. Although search- ing for “the next pandemic virus” has achieved the momentum of a well-oiled government job-creation scheme (a curious European phenomenon that may be unfamiliar to USA readers!), I suspect that a large proportion of pathogens that might jump the species barrier to humans may already have attempted this leap and have failed the test. The simple logic here is humans have explored most of the terrestrial parts of the planet and exposed themselves to a multitude of insect bites, scratches by plants, and samplings of local cuisine; this suggests there are very few pathogens that one of us has not yet been exposed to. Yet it would be foolish to be apathetic. A pathogen that failed to establish in the past might get a second chance in the future if the host it contacts has different susceptibility and moves further or contacts more people while infectious. Conservatively, there are likely to be a couple hundred other pathogens out there that could create a new pandemic, but our attempts to locate them on virological fishing expeditions do a poor job of differentiating between exotic minnows and efficient pike. So my principle focus here will be to assemble the known structures of a mathematical framework that needs to be applied if we are to quantify zoonotic disease emer- gence of a pathogen and our immediate response to it. A recent review explains the mathematical logic of epidemic dynamics at the human–animal interface (Lloyd-Smith et al., 2009). The classification of epi- demic potential is based on the relative magnitude of R0, the basic reproductive number of the pathogen, or more formally the number of secondary cases that an initial infected individual creates in a wholly susceptible population, before “case zero” either recovers or succumbs to infection. Lloyd-Smith et al. base their classification on an earlier review by Wolfe et al. (2007) that classified pathogens along a five-point spectrum with those that are exclusive to wildlife as type I, while those that are exclusive to humans as type V. Most zoonotic pathogens can be arranged along the spectrum from II to IV based upon their affinity for sus- tained transmission in the novel human hosts and the associated pathology. Type II pathogens are those that can cause primary infections in humans, but humans

APPENDIX A 195 are unable to transmit the pathogen on to other humans; classic examples would be Brucella abortus and West Nile virus. Type III pathogens occur when the primary infections are able to infect a number of secondary hosts, but these stut- tering chains of transmission quickly fade out. Classic examples here would be Nipah and Hendra virus that are endemic in Pteropid fruit bats, and humans either acquire infection either directly or indirectly from livestock (pigs and horse,s re- spectively) (Plowright et al., 2011; Pulliam et al., 2011). The most worrying type of zoonotic disease is that in type IV, where the primary infection can give rise to self-sustaining chains of infection. Classic examples here are plague (Yersinia pestis) and pandemic influenza. Lloyd-Smith et al. (2009) point out that each level of classification corresponds to a different range of values of R0; thus type IV (and V) will have R0 > 1, type III will have R0 < 1, and type II, R0 = 0. All of this has made estimation of R0 a central part of any emerging disease outbreak. Before briefly considering ways in which R0 might be measured, it is worth noting that the vast majority of pathogens will be type I. There will then be a smaller number of type II, even less of type III, and so on. This does suggest a future and potentially more focused line of inquiry for virus hunters. Is there any underlying taxonomic signal in the R0 values for zoonotic pathogens? Are there some families of viruses with high proportions of type III and type IV zoonotic pathogens, and are the relative proportions of these different types of pathogen similar or very different in different taxa of viruses, bacteria, fungi, and so on? Putting together a database to address these questions might well provide a more quantitative framework of where to look for potential future pandemic pathogens. It should also provide important background information on how hosts infected with similar pathogens have been diagnosed and treated. All of this assumes a significant degree of phylogenetic inertia in the ways in which the pathology of taxonomically similar pathogens are expressed. R0 or Not? Most mathematical models for emergent disease start with the development of an expression for R0, the basic reproductive number of the disease. It played a central role in the development of the initial response to the SARS epidemic (Dye and Gay, 2003; Lipsitch et al., 2003; McLean et al., 2005). There is a sig- nificant volume of mathematical literature on R0 and many examples of the use of this concept (Diekmann and Heesterbeek, 2000; Diekmann et al., 1990). Most mathematical epidemiologists were delighted that R0 played a central role in the movie Pandemic, and it was a testimony to the elegance of the concept that it resonated well with audiences. There are two approaches to estimating R0: one is classified by the publica- tion feeding frenzy that follows the appearance of data describing the course of an epidemic outbreak; a range of increasingly sophisticated statistical methods are used here, and they increasingly prove central to guiding the public health

196 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS response. The second approach involves deriving algebraic expressions for R 0 in the absence of any epidemiological data. These “mathods” are potentially highly informative for identifying the weakest links in the transmission cycle and then determining methods of control that can break these weak links in the chain of transmission. The simple threshold condition (R0 > 1) is useful for defining the absolute conditions for whether a pathogen will establish, while also pointing towards the level of control needed to contain an outbreak. Nonetheless, there are some important clarifications about the magnitude of R0 that strongly determine what happens once an outbreak begins to take off. The first of these insights deals with the relationship between the magnitude of R0 and persistence time of the epidemic. If R0 is significantly larger than 1, anywhere above 4, and host popu- lation is relatively restricted, then the epidemic may rise quickly, but will con- comitantly run out of new susceptible host, and the epidemic will quickly burn itself out as chains of transmission are broken. This consistently happens with measles in small towns and villages (Keeling and Grenfell, 1997) and with Ebola and Nipah virus outbreaks. In contrast, when R0 is a little bit larger than unity, but less than 2, then outbreaks can persist for longer. An interesting example of this is illustrated in theoretical work recreating distemper outbreaks in different carnivore species in Serengeti National Park (Craft et al., 2008). Species such as jackals with large populations exhibit sharp epidemics of short durations; in contrast, outbreaks persist for much longer in hosts with lower abundance, particularly if they are split into relatively isolated social groups (such as lions). The pathogen then causes an outbreak in each social group, but then more slowly jumps between social groups, or between species, and despite having a lower R 0, persists for much longer. The first (and most eloquent) demonstration of this is found in the work of Swinton and colleagues on the outbreaks of distemper in seal population in the North Sea (Swinton, 1998; Swinton et al., 1998). Theoretical models for persistence sharply illustrated that persistence time increases hugely as populations are subdivided into social groups whose rate of contact is always lower than rates of contact within group (Figure A8-1). This effectively lowers R0 for populations of identical size, but hugely increases pathogen persistence. Anderson and May proposed that this sort of mechanism was likely central to the initial emergence of HIV with the virus entering the human population in small, weakly coupled villages in Africa, none of which could support a sustained outbreak, but each of which was weakly coupled to an unexposed village that could keep the chain of infection intact (Anderson and May, 1986). Eventually the pathogen was passed into hosts who had contact with much larger and more actively interacting community, and the epidemic was detected in the United States and other Western countries. The epidemic of AIDs that emerged in the 1980s in major Western cities contrasts sharply with the previous half century of HIV in rural Africa, where dynamics were most likely characterized by low

APPENDIX A 197 FIGURE A8-1  The expected persistence time of a pathogen that infects its hosts for 2 weeks and is infectious for the second of those weeks in populations of different sizes. The four different graphs compare populations that are divided into social groups of different sizes (left GS = 4; right GS = 10); the different lines compare persistence in a well-mixed population with no groups with ones where the rate of between-group contacts is 5 percent, 20 percent, 50 percent, and 90 percent of the rate of within-group contacts. The upper two graphs are for groups arranged in a linear sequence (along a coastline) and that only contact the groups on either side of them; in the bottom two figures the groups tessellate a plane, so each group is in contact with at least four other groups SOURCES: McCallum and Dobson, 2006; Swinton, 1998. Reprinted with permission of Cambridge University Press. R0 slowly moving the pathogen through a sequence of weakly connected and relatively isolated villages somewhere in the forests of central Africa. A related effect that operates in a similar fashion has been proposed by Pulliam and colleagues (Pulliam et al., 2007, 2011). They observe that many immunity-inducing pathogens may facilitate emergence by initially creating an outbreak that creates an intermediate level of herd immunity in the novel host population that reduces the pool of susceptible hosts to a level where a second crossing over of the pathogen from the reservoir creates an outbreak with an R0 closer to unity, and thus one that will persist for longer and be more able to spread

198 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS to the new population. They suggest this was a dominant factor in the emergence of Nipah virus in Malaysia and show that many other pathogens have epidemic demography that would also allow them to establish in this fashion (Pulliam et al., 2007). When they plot the transmission and virulence parameters of a number of pathogens into a graph that determines emergence potential they find that many emergent pathogens have these characteristics (Figure A8-2). FIGURE A8-2 (A) Deterministic prediction of the parameter ranges where epidemic enhancement may be observed. The range of parameter values (grey) for a population size of N = 50,000 and the initial condition (S0, I0) = (N − 1, 1) which demonstrate the behavior of an initial epidemic which dies out (there exists t > 0 such that It < 1) followed by persistence upon reintroduction (I* > 1), depending on the level of population turnover between pathogen extinction and reintroduction. R0 is the basic reproductive number of the pathogen in a naïve host population; ρ is the duration of infectiousness relative to the average duration of immunity. Stars represent parameter values taken from the literature for a variety of common and emerging infectious diseases. Note that the x-axis is shown on a log scale. (B) Enhancement of epidemic duration for diseases in human populations. Epidemic duration in a population of N = 50,000 individuals for a variety of human patho- gens as a function of population immunity at introduction. Solid lines show the median duration in disease generations for 1,000 simulation runs at each level of initial population immunity; dashed lines show quartiles. Each pathogen shows some level of enhancement of epidemic duration with increased immunity except pertussis. Enhancement of epidemic size is not observed for these pathogens for N = 50,000. SOURCE: Pulliam et al., 2007.

APPENDIX A 199 Incubation and Infectious Period Two parameters of any model for R0 are central to our ability to control the initial emergence of a novel pathogen. Ironically we tend to worry more about the transmissibility of the pathogen, which is always the hardest thing to estimate, than we do about these other two equally vital parameters: incubation and infectious period. Key insights into the importance of these parameters come from work comparing SARS, influenza, and HIV (Fraser et al., 2004). This work shows that although R0 is fundamental in determining the level of intervention, even pathogens with low R0 can cause huge epidemics if they have a long silent incubation period during which transmission occurs without any apparent symp- toms. The classic example of this is HIV, which has caused the largest epidemic in human history since the plague epidemics of thirteenth-century Europe. This contrasts sharply with SARS where symptoms of infection appear almost simul- taneously to ability to infectiousness; this made it relatively easy to contain SARs through a combination of isolation of infectious individuals and contact tracing (Figure A8-3). This problem with long “silent” incubation periods is particularly worrying from the current vogue for virus hunting. I think that if the approaches currently used to detect emerging pathogens were retrospectively applied to the HIV virus, they would dismiss it as a mild and innocuous pathogen of limited concern. This is primarily because the incubation period of the virus is as long as the life ex- pectancy of most primate species used in laboratory research (Anderson, 1991). If injected into humans, we would only see an initial rise in virus abundance that was quickly knocked back by the host’s immune system. Although an astute clinician might detect the virus’s rapid mutation rate, I suspect that the absence of any symptoms in the first 5 to 10 years postinfection would lead to the virus being dismissed as a hazard. From a simple mathematical perspective this makes me much more concerned about viruses with long silent incubation periods (and the opportunities to infect thousands of people) than it does about highly virulent viruses whose violent symptoms make for powerful movies, but also for ready detection, isolation, and the development of a rapid response. There Be Dragons! Maps are powerful tools that have multiple uses in biology and epidemiol- ogy. For example, they have been widely used in conservation biology to identify areas of unusually high biological diversity in areas with relatively low land val- ues, or to identify areas with unusually high extinction rates (Bibby et al., 1992; Conroy and Noon, 1996; Dobson et al., 1997). Conservation biology is essentially a complementary discipline to infectious disease biology; one discipline seeks to drive organisms to extinction, the other hopes to bring back rare species from the brink. Both disciplines have benefitted from the huge rise of geographical information systems (GIS) that allows maps to be readily created from detailed

200 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS FIGURE A8-3  (A) Parameter estimates. Plausible ranges for the key parameters R0 and θ for four viral infections of public concern are shown as shaded regions. The size of the shaded area reflects the uncertainties in the parameter estimates. The areas are color-coded to match the assumed variance values for β(τ) and S(τ) of Fig. 1 in Fraser et al. (2004) appropriate for each disease, for reasons that are apparent in Fig. 3 in Fraser et al. (2004). (B) Criteria for outbreak control. Each curve represents a different scenario, consisting of a combination of interventions and a choice of parameters. For each scenario, if a given in- fectious agent is below the R0–θ curve, the outbreak is always controlled eventually. Above the curve, additional control measures (e.g., movement restrictions) would be required to control spread. Black lines correspond to isolating symptomatic individuals only. Colored lines correspond to the addition of immediate tracing and quarantining of all contacts of isolated symptomatic individuals. The black (isolation only) line is independent of distri- butional assumptions made (low or high variance), whereas the colored (isolation + contact tracing) lines match the variance assumptions made in Fig. 1 in Fraser et al. (2004) (red = high variance; blue = low variance). The efficacy of isolation of symptomatic individuals is 100% in Panel B-1, 90% in Panel B-2, and 75% in Panel B-3. Contact tracing and isolation is always assumed 100% effective in the scenarios in which it is implemented (colored lines). Curves are calculated using a mathematical model of outbreak spread incorporating quarantining and contact tracing. SOURCE: Fraser et al., 2004.

APPENDIX A 201 geographical data and more sparse biological and epidemiological surveys. Maps also have a very distinguished history of use in epidemiology. This stretches from John Snow’s original map identifying the Broad Street pump as the most likely source of cholera in London, through the path-breaking work of Cliff, Haggett, and Ord (Cliff and Haggett, 1988; Cliff and Ord, 1981): their studies of the history of measles in Iceland, combined with the work of Bartlett (1957, 1960, 1966), paved the way for our current deep understanding of the dynamics of SIR pathogens (Anderson and May, 1983, 1985, 1990; Bjornstad et al., 2002; Ferrari et al., 2008; Grenfell and Anderson, 1985; Grenfell et al., 2001). The great power of maps is that politicians and government decision makers have an instinctive understanding of maps (they use them to plan their vacations and political or military campaigns); in contrast, they seem much more wary of mathematical models, or even graphs (although they have teams of policy mak- ers that happily abuse these models to plan economies and election campaigns!). The central problem with maps is apparent in some of the oldest maps; when there was limited or no information for an underexplored region there was a tendency to assume “There be dragons.” This creates a historical precedent to use maps to identify the location of unknown scary monsters such as emerging pathogens or endangered species that have not been seen for some time. More disconcertingly, it means that we tend to forget that the data that underlie maps need to be verified and tested against an epidemiological model that provides some mechanism to explain the observed geographical patterns of incidence. Sometimes this is done (see Cliff and Haggett, 1988; Cliff and Ord, 1981). All too frequently the map is presented as a predictive tool, when all it is really presenting is a rather undigested mass of data detectable in the literature. When these maps are used as a predictive tool, many quantitative disease modelers become nervous. These fears could be allayed by some relatively simple math- ematical or statistical tests of the map’s utility. The simplest approach to seeing if a map of emerging disease hot spots has any predictive value would be to take the first half of the historical data used to make the map and see if it has any predictive “skill” in reproducing the latter half of the observed data (an approach widely used by climatologists). I suspect that these approaches would exhibit high skill in predicting the locations of large urban areas with major medical facilities that consistently detect new antibiotic resistant strains of bacteria. The approach will be less powerful at detecting areas for new unknown (viral) dis- eases (which may or not emerge), as this will reflect where people have decided to work on a hunch that this will be a hot spot, or because it was one in the past. I was very amused when a colleague told me that he had received funding for his emerging disease work because a hot spots map had identified his site as a likely hot spot. This was because he was one of the few people to have published a previous epidemiological survey from within this broad geographical location! Most quantitative ecologists working on emerging pathogens are exception- ally skeptical about maps produced that purport to predict hot spots of disease

202 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS emergence. This skepticism is well justified by the very limited ability of these maps to predict anything other than antibiotic-resistant strains of pathogens that tend to emerge in around Western cities where drugs are widely used and there are well-funded medical schools focused on detecting these strains. This form of prediction is essentially a self-fulfilling fantasy! A Role for Climate? There is increasing interest in the role that climate change will play in the emergence of new pathogens. At the risk of being hypocritical, I am going to use a couple of maps to shape the discussion about how climate change will interact with other aspects of global change to affect pathogens. Here it is important to explicitly acknowledge that climate change is only one component of global change. While all sane scientists not in the deep pockets of oil industry now acknowledge that anthropologically driven climate change is a real effect that is increasingly influencing the Earth’s climate, predicting how this will influence patterns of infectious disease dynamics and outbreaks will not be straightforward (Rodó et al., 2013), not least the influence of climate change may be masked by other aspects of global change, particularly in the parts of the world where most people are going to be living over the next 100 years. The work of Jetz et al. (2007) on how geographic distributions of all the world’s bird species are likely to change over the next 100 years is instructive here. Jetz et al. (2007) base their analysis on the Millennium Ecosystem Assess- ments of land use change under four different scenarios. The work is based on detailed forecasts for climate, land use change, human population growth, and agricultural expansion over the next 100 years (Alcamo et al., 2005; Reid et al., 2005). These scenarios were then applied to the current distribution data for each of the world’s bird species. Birds were chosen as we have better data for birds than for any other taxa. The impact of land use change was applied to the geo- graphical range of each species, and this was used to quantify the proportional loss of habitat for all bird species under complementary drivers of anthropologi- cal land use change (agriculture and urbanization) and climate change. Figures A8-4 and A8-5 present map projections and latitudinal cross sections that result from these analyses. The results generalize for other taxa and so provide impor- tant implications for pathogens, as well as for their nonhuman reservoir species. The figure illustrates that although climate change will dominate the future of polar regions, the impacts of land use change will hugely mask any climate change signature in the tropics and temperate regions. As the vast majority of birds (and mammals, plants, insects, etc.) live in the tropics, then it is going to be very hard to detect a climate change signal when we try and predict any aspect of the future of these systems.

FIGURE A8-4  Geographic patterns and projected impact of environmental change. (A, B) Patterns of change in land cover due to land use and climate change by 2100. This represents the summed, current-day occurrence of qualifying species across a 0.5° grid. Patterns are given for the environmentally proactive “Adapting Mosaic” scenario, and the environmentally reactive “Order from Strength” scenario. Maps are in equal-area cylindrical projection. 203 SOURCE: Jetz et al., 2007.

204 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS FIGURE A8-5  Environmental change, avian biogeography, and loss in range size. Pro- jected latitudinal pattern in type of global environmental change, geographic range size, species richness, and the resulting loss in geographic range size (8,750 bird species, 1° bands of latitude). Climate (cyan, on top and semitransparent) and land use (red) changes between now and 2100 are evaluated for two scenarios: on the left, “Adapting Mosaic” (A, C), and on the right, “Order from Strength” (B, D). Top (A, B): Total area transformed (area plot, lighter color indicates overlap) and average (± SE) current geographic range size of species per latitudinal band (point and line plot); Bottom (C, D): Average pro- portional loss of range size (area plot, lighter color indicating overlap) and total number of bird species whose range currently overlaps at each latitudinal band (point and line plot). Whereas climate change leads to a significant net change of habitat in the polar and temperate regions, the small numbers of bird species that live there on average have very large geographic ranges. Thus, proportional contractions in range size there are much smaller than for the vast majority of bird species that live in the tropics and experience significant reductions in their smaller range sizes due to land use change. The outcome is many species with significant range reduction in the tropics and subtropics, because of the coincidence of habitat conversion with areas of high species richness. This is particularly the case in the environmentally reactive “Order from Strength” scenario, where large areas of land are converted to agriculture. SOURCE: Jetz et al., 2007.

APPENDIX A 205 There are three insights that I want to make from these figures: 1. This does not mean that climate change is not important; it means we need to understand how climate interacts with other aspects of global change. 2. In particular, if we want to understand how climate change will affect disease dynamics then we should expand studies of disease dynamics in the Arctic as these systems have a much stronger climate signal and many less confounding effects. Work undertaken here is already providing important insights that will eventually help interpret what will eventually happen in the temperate and tropical regions. 3. Our biggest worry about emerging pathogens in the tropics will come from land use change modifying the natural habitats of wild reservoir spe- cies living in these regions and the increasingly large human population that interact with them. R0, Biodiversity, and Dilution Effects The principle scientific justification for virus hunting in the tropics is that these regions contain the highest levels of biological diversity and hence more species should equate with more undiscovered pathogens. This in turn has led to some dubious estimates of the number of undiscovered viral species that assume all host species harbor the same number of pathogen species (Anthony et al., 2013). The logic of this approach assumes some of the methodology (and none of the rigor) of previous attempts to estimate global insect diversity by taking the numbers of insects associated with a small number of host trees and multiplying these numbers up by the known number of tree species (Erwin, 1982; Gaston, 1991, 1994; Hodkinson, 1992). Future attempts to estimate viral diversity would benefit hugely from the adoption of the methodology employed in these earlier entomological studies. In particular it should also be realized that host population size, density, and spatial distribution will all play a crucial role in determining the diversity of microbial pathogens harbored by any host species, and it is highly likely that rare species will host lower pathogen diversity than more common species. Rare hosts and hosts of low abundance create significant challenges for pathogens who adapt to these constraints by either reducing their virulence, so as to reduce the chance the hosts die between encounters, or increase their efficiency of transmission to ensure it occurs on the rare occasions that hosts encounter each other. These constraints mean the pathogens of rare species with low abun- dance are most likely to be STDs; there is little chance for anything else to be transmitted or maintained in the host population (Altizer et al., 2003; Lockhart et al., 1996). Common hosts are likely to harbor a greater diversity of pathogens, particularly if they live in large social groups (Ryan et al., 2013). All of which

206 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS suggests that virus hunters who head for the tropics to look for undiscovered viruses in rare species are significantly, scientifically deluded. An alternative perspective on biodiversity considers the role it may play in buffering pathogen emergence and reducing the potential for the emergence of novel pathogens. Disease ecologists call these phenomena “the dilution effect” (Dobson et al., 2006; Hudson et al., 1995; Keesing et al., 2006; Schmidt and Ostfeld, 2001), and there is an intense debate about the role they play in buffer- ing disease outbreaks (Lafferty and Wood, 2013; Ostfeld and Keesing, 2013; Randolph et al., 2012). Dilution effects can only occur when a pathogen uses multiple species of hosts. When one or more of these host species is able to withstand infection with the pathogen, but fails to transmit it efficiently to other hosts, it effectively creates a dilution of transmission rates and slows the rate of epidemic spread. Dilution effects are likely to be most efficient for vector-borne diseases than for directly transmitted pathogens (although evidence does suggest they are important for directly transmitted pathogens with frequency-dependent transmission such as Hanta virus). Dilution effects are also likely to be stronger for mosquito-borne pathogens than for tick-borne pathogens, as the abundance of the vectors is independent of host abundance for mosquitoes, but not for ticks (Dobson, 2009). Ironically, the best studied example of the dilution effect comes from work on tick-transmitted Lyme disease (LoGuidice et al., 2003; Ostfeld and Keesing, 2000; Ostfeld and LoGiudice, 2003). From the perspective of pathogen emergence, we simply do not know whether these effects are strong enough to buffer rates of disease emergence. These is some correlative evidence that supports the case that they might be operating (Bonds et al., 2012; Ezenwa et al., 2006; Roche et al., 2012), and if this is the case then it presents a powerful argument for finding ways to conserve species diversity as agriculture and land use intensifies. Subsequent Evolution The emerging pathogens that cause me to lose most sleep are those caused by the evolution of resistance to the drugs and antibiotics we have used over the last 50 years (Cohen, 1992; Palumbi, 2001). These are pathogens that we know have caused significant mortality to humans and domestic livestock in the past. We know they have no difficulty establishing and multiplying in their host popu- lations. People now in their mid-50s have benefited from their absence for most of their lives; yet, it is likely that at least half of us will acquire them late in life (probably in a hospital or while travelling on public transport), and they will be our terminal interaction with a pathogen. The mathematics of drug resistance has been studied from a variety of per- spectives. One of the earliest and simplest insights comes from May and Dobson who show simply that the rate at which drug (or pesticide/insecticide) resistance evolves is mainly determined by the log of the basic reproductive rate of the or- ganism evolving resistance (May and Dobson, 1986). This explains very simply

APPENDIX A 207 why mosquitoes and bacteria quickly evolve resistance; in contrast, birds of prey were unlikely to ever evolve resistance to egg-shell thinning: if it takes a hundred generations to evolve resistance, then what takes months for bacteria requires centuries for a bird of prey. There is really only one pathogen that has emerged recently where no attempt has been made to eradicate the pathogen and its evolution has been studied beyond the first few generations of cases. Work on Mycoplasma gallisepticum (MG) in the North American house finch provides a number of important insights that are likely to generalize to other emergent pathogens should they escape detection and control at the initial stages of emergence. MG emerged from domestic fowl into wild finches in the fall of 1993. House finches were the most conspicuous hosts as their pathology of MG is characterized by pronounced swelling of the eyes, which reduces their ability to locate food and likely increases their susceptibility to predation (Dhondt et al., 1998; Dobson, 2013). Through a large Citizen Sci- ence network established by the Laboratory of Ornithology in Cornell, the spread and impact of the disease has been monitored for the last 20 years (Hosseini et al., 2006). The pathogen has now spread across the entire United States infecting house finches both in their introduced range (eastern United States) and in their native range (western United States) as well as up to 30 different species of birds. The eastern population has declined by around 60 percent (around half a billion birds), less significant declines are observed in the west (Dhondt et al., 2006; Hochachka and Dhondt, 2000). Detailed laboratory, field, and genetic studies illustrate that the pathogen has evolved continuously since emerging, and this evolution has both increased and decreased virulence depending on the condition determining selection. This evolution can happen quite quickly and is reversible (Dhondt et al., 2005; Hawley et al., 2010, 2013). There is essentially no differ- ence in the behavior of identical strains of the pathogen in inbred eastern birds and outbred, more genetically diverse western birds; essentially, the hosts show no real evidence for genetic resistance to the pathogen. This is not surprising as their age-structured population has only been exposed to less than 10 generations of selection; the pathogen has likely had several thousand generations of selection in this time. All of which should give pause for thought to those who think or preach that we can readily breed or otherwise genetically modify hosts for disease resistance. Even for small rapidly breeding hosts like passerine birds, the asym- metry in host and pathogen demographic rates will always allow the pathogen to evolve at rates that make the hosts genetic response essentially inert. The situa- tion is even more asymmetrical when we consider humans and emerging viruses. Conclusions There is increasing evidence that pathogens play a significant role in deter- mining the economic well-being of most of the world’s nations (Bonds et al., 2012); this occurs through their direct effects on the size and efficiency of the labor force. The traditional economic argument that the wealth of nations is

208 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS determined solely by governance is now seen as a deeply flawed and biased argument (Acemoglu et al., 2000, 2003; McArthur and Sachs, 2001). Although the economic impact of recent disease outbreaks is frequently cited as a central reason to increase funding for research on emerging pathogens, I suspect that the continued impact of older diseases such as malaria and the neglected tropical diseases has a larger annual effect on the global economy than any of the recent emerging disease threats. Ultimately we need more funds to study the dynamics of both emerging and endemic pathogens and their hosts. Disease is as important as governance in driving national economies—biodiversity may play a role in buffering pathogens. Pathogens are a large component of natural ecosystems—perhaps as much as 90 percent of biodiversity is parasitic on free-living species. Pathogens emerge when we disturb natural ecosystems—but, we have as much chance of a new pathogen emerging in our own back yards as we have of something else emerging from the tropics. We need to think more deeply about the population dynamics of pathogen emergence, and step back a little from the romance of fishing for tropi- cal viruses with microchips. If we ask the simple question “Would the tropical virus hunters have identified HIV?” I suspect the long incubation period with no initial pathology would have led them to dismiss it as inert and innocuous and to miss it entirely. Ultimately pathogens emerge and cause problems for humans, domestic live- stock, and other wildlife species because we have disturbed their natural habitat in ways that modifies their transmission rates. This suggest that developing a better mathematical understanding of the dynamics of food webs and the role that parasites play in these large complex nonlinear systems will provide alternative insights into the way in which pathogens emerge (Dobson et al., 2009; Hudson et al., 2006; Lafferty et al., 2008). Similar mathematical models will also be needed to understand how immune systems function and how the brain commu- nicates with the nervous and endocrine systems. These mathematical understand- ings of the function of complex systems are easily as important to the future of human health as is the current focus on genomic understanding. Indeed, in the absence of the understanding about how the parts coded for by genes interact together, we are simply dealing with the “natural history” of these systems at a heroically tiny, but essentially disconnected, biological scale. Acknowledgements APD’s research is sponsored by the NSF/NIH Ecology of Infectious Dis- ease Program and a grant from the McDonnell Foundation for Studies of Com- plex Systems. An initial draft of this paper was prepared for discussion at the ANTIGONE workshop on interspecies barriers and zoonotic disease emergence in Toledo, Spain, September 2014. All of the work described above benefited from discussions with colleagues at this workshop and in the Ecology and Evolu- tion of Infectious Disease Group at Princeton.

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APPENDIX A 213 A9 ENVIRONMENTAL CHANGE AND INFECTIOUS DISEASE: HOW NEW ROADS AFFECT THE TRANSMISSION OF DIARRHEAL PATHOGENS IN RURAL ECUADOR26 Joseph N. S. Eisenberg,27 William Cevallos,28 Karina Ponce,27 Karen Levy,29 Sarah J. Bates,30 James C. Scott,30 Alan Hubbard,30 Nadia Vieira,28 Pablo Endara,28 Mauricio Espinel,28 Gabriel Trueba,28 Lee W. Riley,30 and James Trostle31 Abstract Environmental change plays a large role in the emergence of infectious disease. The construction of a new road in a previously roadless area of northern coastal Ecuador provides a valuable natural experiment to examine how changes in the social and natural environment, mediated by road con- struction, affect the epidemiology of diarrheal diseases. Twenty-one villages were randomly selected to capture the full distribution of village population size and distance from a main road (remoteness), and these were compared with the major population center of the region, Borbón, that lies on the road. Estimates of enteric pathogen infection rates were obtained from case- control studies at the village level. Higher rates of infection were found in nonremote vs. remote villages [pathogenic Escherichia coli: odds ratio (OR) = 8.4, confidence interval (CI) 1.6, 43.5; rotavirus: OR = 4.0, CI 1.3, 12.1; and Giardia: OR = 1.9, CI 1.3, 2.7]. Higher rates of all-cause diarrhea were found in Borbón compared with the 21 villages (RR = 2.0, CI 1.5, 2.8), as well as when comparing nonremote and remote villages (OR = 2.7, CI 1.5, 26  Reprinted with permission from the Proceedings of the National Academy of Sciences of the United States of America. Originally printed as Eisenberg et al. 2006. Environmental change and infectious disease: how new roads affect the transmission of diarrheal pathogens in rural Ecuador. Pro- ceedings of the National Academy of Sciences of the United States of America 103(51):19460-19465. 27  School of Public Health, University of Michigan, Ann Arbor, MI 48104. 28  School of Public Health, University of California, Berkeley, CA 94720. 29  Department of Environmental Science, Policy, and Management, University of California, Berke- ley, CA 94720. 30  Universidad San Francisco de Quito, Quito, Ecuador. 31  Department of Anthropology, Trinity College, Hartford, CT 06106.   Notes: Author contributions: J.N.S.E., M.E., G.T., L.W.R., and J.T. designed research; W.C., K.P., K.L., S.J.B., N.V., P.E., and J.T. performed research; J.N.S.E., J.C.S., and A.H. analyzed data; and J.N.S.E., K.L., and J.T. wrote the paper.   The authors declare no conflict of interest.   Abbreviations:   CI: confidence interval; OR: odds ratio.

214 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS 4.8). Social network data collected in parallel offered a causal link between remoteness and disease. The significant and consistent trends across viral, bacterial, and protozoan pathogens suggest the importance of considering a broad range of pathogens with differing epidemiological patterns when as- sessing the environmental impact of new roads. This study provides insight into the initial health impacts that roads have on communities and into the social and environmental processes that create these impacts. The more public health scientists learn about infectious disease processes, the more they can implicate environmental changes in the recent emergence or reemergence of infectious diseases (Colwell et al., 1998; Morse, 1995; Patz et al., 2000). Given the increasing number of emerging pathogens recently identified, there is an urgent need to understand how environmental change influences disease burden. Such changes are potentially more visible in places where they have been caused by human activity, such as construction of dams, pipelines, and roads. Anthropogenic environmental changes that cause populations to move and settle in new ways can provide the opportunity to observe the relationship between environmental change and disease transmission. Where such environ- mental changes are unevenly distributed across a region, thereby producing the conditions of a natural experiment, these relationships can be observed easily and systematically. The construction of a new road in a previously roadless area in northern coastal Ecuador provides just such a natural experiment to examine how changes in the social and natural environment, mediated by road construction, affect the epidemiology of diarrheal diseases. Various studies have examined the impact of road construction on disease incidence (Birley, 1995). For example, the building of the TransAmazon Highway was associated with an increase in malaria (Ault, 1989; Coimbra, 1988). These in- creases in incidence were attributed to the presence of water pools created by road construction practices. More recently, a study in the Peruvian Amazon indicated that mosquito biting rates are significantly higher in areas that have undergone deforestation and development associated with road development (Vittor et al., 2006). Analogously, a study in India measured a higher prevalence of dengue vec- tors along major highways than elsewhere (Dutta et al., 1998). Studies in Uganda suggest that the main road linking Kenya to Kampala has higher proportions of HIV-positive women working in bars and HIV-positive truck drivers than does the surrounding area (Carswell, 1987). In general, transportation changes mobility and circulation of humans, which can affect the incidence of sexually transmitted diseases (Panos Institute, 1988), as well as health-care-seeking behavior (Airey, 1991, 1992). As opposed to sexually transmitted diseases, fecal–oral pathogens can survive outside of the human host and therefore will behave differently under environmental changes. Some studies have suggested that remote villages sepa- rated by large distances are less able to sustain transmission of certain fecal–oral pathogens, such as amoebas and rotavirus (Black, 1975; Gilman et al., 1976;

APPENDIX A 215 Gunnlaugsson et al., 1989). The impact that environmental changes from road construction have on these diarrheal diseases remains largely unexplored and un- known, despite the fact that diarrheal diseases remain a major cause of mortality among infants and children under 5 years of age (WHO/UNICEF, 2004). In 1996 the Ecuadorian government began a road construction project to link the southern Colombian border with the Ecuadorian coast. A two-lane asphalt highway was completed in 2001, spanning 100 km across the southern end of the Chocó rainforest near the Pacific Ocean. Secondary roads continue to be built, linking additional villages to the paved road (Figure A9-1). These roads provide a faster and cheaper mode of transportation compared with rivers. The extent to which roads influence communities should be measured by their proximity in time and distance to a given village (e.g., remoteness) and not merely by their presence or absence. To examine the impact of remoteness on diarrheal disease we implemented a hierarchical design that collects data by village to obtain information about the region, and by individual to obtain information about potential confounding factors that may bias the analysis. Roads influence disease transmission through a variety of mechanisms. For example, road proximity can increase in- and out- migration rates causing multiple demographic changes in the age, racial, and socioeconomic profile. These rapid and complex changes can reduce social con- nectedness within a community, which may in turn reduce a community’s ability to maintain good sanitation and hygiene conditions. Road proximity can also affect short-term travel patterns, thereby increasing the potential for the introduc- tion of new pathogen strains into communities. In addition to diarrheal symptoms, three specific marker pathogens (Esch- erichia coli, rotavirus, and Giardia) were followed, each with a distinct epidemi- ology. Both pathogenic E. coli and rotavirus are responsible for a large proportion of diarrhea mortality and severe morbidity throughout the developing world, whereas Giardia, also a major cause of diarrhea, is more pervasive, resulting in higher infection rates (Blaser et al., 2002). Taken together, these three pathogens represent the primary pathways (food, water, and person-to-person) for transmis- sion of diarrhea. Results Table A9-1 presents community characteristics, with two methods for char- acterizing location: remoteness of a community relative to the town Borbón, and river basin in which a community resides. The least remote community has a remoteness value of 0.012, and the most remote village has a remoteness value of 0.198. Close villages were defined as those with a remoteness value of < 0.03; medium villages were defined as those with a remoteness value between 0.03 and 0.13; and remote villages were defined as those with a remoteness value > 0.13. These classifications are also represented in the regional map (Figure A9-1).

216 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS FIGURE A9-1 Map of study region. The 21 villages are categorized by river basin (Santiago, Cayapas, Onzole, Bajo Borbón, and road) and by remoteness (close, medium, and far).

APPENDIX A 217 TABLE A9-1  Community Characteristics Remoteness Remoteness Village Population size metric category River basin 1 284 0.012 Close Road 2 731 0.015 Close Road 3 78 0.022 Close Cayapas 4 482 0.027 Close Road 5 156 0.040 Medium Santiago 6 55 0.040 Medium Bajo Borbón 7 138 0.040 Medium Bajo Borbón 8 72 0.049 Medium Road 9 90 0.049 Medium Santiago 10 60 0.061 Medium Onzole 11 86 0.080 Medium Onzole 12 110 0.113 Medium Cayapas 13 135 0.122 Medium Santiago 14 83 0.140 Far Onzole 15 300 0.152 Far Santiago 16 228 0.155 Far Santiago 17 79 0.158 Far Cayapas 18 268 0.165 Far Cayapas 19 28 0.173 Far Onzole 20 443 0.190 Far Onzole 21 130 0.198 Far Cayapas Borbón 864 0 Remoteness is a measure of the time and cost of travel to Borbón. Roads provide cheaper and faster access to Borbón, and therefore remoteness is a measure of the proximity to the road. Note that the population of Borbón is the sample size enrolled in the study, rather than the size of the entire population (≈ 5,000). Village population size ranged from 28 to 731, and the random sample of 200 houses in Borbón resulted in 864 individuals, or ≈20% of the population. A total of 298 cases of diarrhea were identified in the communities during the three case-control cycles, and 44 cases were identified in Borbón during the one case-control cycle (Table A9-2). In addition, a total of 845 and 125 controls were sampled from the communities and Borbón, respectively. Crude prevalence estimates are shown in Table A9-3 for diarrhea and infection by both case status and remoteness category. The crude prevalence estimates for diarrhea [RR = 2.0, 95% confidence interval (CI) 1.5, 2.8] and pathogenic E. coli (RR = 16.0, 95% CI 13.2, 19.2) were significantly higher in Borbón compared with those in other communities (Table A9-4). These large differences between infection prevalence in Borbón vs. the community are seen in both cases and controls (Table A9-3). We found no evidence that crude prevalence estimates for rotavirus and Giardia varied between Borbón and the other 21 communities.

218 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS TABLE A9-2  Number of Cases and Controls by Remoteness No. of Remoteness No. of collection No. of category villages Population days No. of cases controls Remote 8 1,669 45 112 317 Medium 9 895 45 91 248 Close 4 1,592 45 95 280 Community* 21 4,156 45 298 845 Borbón 1 867 15 44 125 For communities other than Borbón, figures are the sum from three 15-day case-control studies across all 21 study villages between August 2003 and February 2006. Borbón figures are from one 15-day case-control study in July 2005. *Total from all 21 villages (sum of remote, medium and close villages). Adjusting for age of individuals, community population size, and sanitation level, the prevalence of infection was significantly higher in villages closer to or along a road compared with those communities far from the road for pathogenic E. coli [odds ratio (OR) = 3.9, 95% CI 1.1, 13.6], rotavirus (OR = 4.1, 95% CI 2.0, 8.4), and Giardia (OR = 1.6, 95% CI 1.0, 2.4); the same was true for all- cause diarrhea (OR = 1.8, 95% CI 1.2, 2.6) (Table A9-5). Precipitation was not included in the final model because its P value was > 0.2. These overall infection trends were largely driven by the controls, as evident from the crude prevalence estimates in Table A9-3 that are stratified by case status. Although the crude diar- rhea prevalence values show no trend as a function of remoteness, the adjusted risk estimates comparing both remote and medium as well as remote and close were significant, after adjusting for the population size and sanitation level of each community (Table A9-5). To test for a trend, remoteness was modeled as a continuous variable. The relative risk of infection associated with a decrease in remoteness from the far- thest to the closest village was significant for all infections: pathogenic E. coli (OR = 8.4, 95% CI 1.6, 43.5), rotavirus (OR = 4.0, 95% CI 1.3, 12.1), and Giar- dia (OR = 1.9, 95% CI 1.3, 2.7). For all-cause diarrhea the relative risk was also significant (OR = 2.7, 95% CI 1.5, 4.8) (Table A9-5). Discussion We observed strong trends in infection rates and all-cause diarrhea in villages across a gradient of remoteness for our marker pathogens even after adjusting for population size, sanitation, and precipitation. This result suggests that villages farther from the road have lower infection rates than villages closer to the road. This relationship between infection and road proximity is also seen in Borbón, the only community directly connected to both the primary road and all of the major rivers that serve the region. We observed significantly higher rates of E.

TABLE A9-3  Crude Infection Prevalence by Case Status and Remoteness (prevalence per 100 persons) Overall infection prevalence, Asymptomatic infection prevalence, Symptomatic infection prevalence, Diarrhea infections/100 infections/100 infections/100 Remoteness prevalence, category cases/100 E. coli Rotavirus Giardia E. coli Rotavirus Giardia E. coli Rotavirus Giardia Remote 2.6 1.0 2.7 16.7 0.6 2.2 15.8 0.4 0.6 0.9 Medium 4.6 3.1 3.6 16.6 2.3 2.7 15.2 0.5 0.9 1.5 Close 2.2 3.9 6.7 23.2 3.0 6.2 22.4 0.1 0.5 0.8 Community 2.8 2.4 4.5 19.4 1.9 4 18.4 0.3 0.6 0.9 Borbón 5.6 22.5 3.6 19.5 20.7 2.3 17.6 1.7 1.2 1.9 For communities other than Borbón estimates are based on the average of three 15-day case-control studies across all 21-study villages. Borbón estimates are based on one 15-day case-control study. Overall infection prevalence is based on a weighted average of infection in cases and controls. Prevalence estimates are based on a 15-day period prevalence. 219

220 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS TABLE A9-4  Comparison of Infection Prevalence in Communities vs. Borbón Community, cases/100 Borbón, cases/100 Relative risk (95% CI) E. coli 1.6 22.5 16.0 (13.2, 19.2) Rotavirus 4.5 3.6 0.8 (0.6, 1.2) Giardia 19.4 19.5 1.0 (0.9, 1.2) Diarrhea 2.8 5.6 2.0 (1.5, 2.8) For communities other than Borbón estimates are based on the average of three 15-day case-control studies across all 21 study villages. Borbón estimates are based on one 15-day case-control study. Pathogen prevalence is based on infection (a weighted average of cases and controls). Relative risk is the prevalence risk ratio (the risk of illness or infection in Borbón relative to the communities). coli and all-cause diarrhea in Borbón than in the other 21 study communities. These health differences have policy significance given that both pathogenic E. coli and rotavirus are major causes of mortality and severe morbidity in children. These data were collected across three river basins during three visits to each town over 2 years, minimizing the chance that unmeasured localized events either temporally or spatially confounded the risk estimates. We found no statistical relationship between diarrhea or infection rates and time period or river basin. Any unmeasured confounding would have had to continue over the 2-year study period or had to occur across the three river basins. Explaining the causes of the trends discussed here requires understanding the ecological and social impacts of roads. One common purpose (and consequence) of a new road is increased logging. Deforestation causes major changes in water- shed characteristics and local climate, both of which can affect the transmission of enteric pathogens (Curriero et al., 2001). Perhaps more important than ecologi- cal processes, social processes facilitated by roads such as migration, creation of new communities, and increased density of existing communities can affect pathogen transmission. Changes in community social structures often create or are accompanied by inadequate infrastructure, which affects hygiene and sanita- tion levels, and in turn the likelihood of transmission of enteric pathogens. Roads TABLE A9-5  Infection as a Function of Remoteness OR (95% CI) E. coli Rotavirus Giardia Diarrhea Remote 1.00 1.00 1.00 1.00 Medium 3.0 (0.8, 11.9) 1.3 (0.5, 3.2) 1.2 (0.7, 2.0) 1.8 (1.1, 3.0) Close 3.9 (1.1, 13.6) 4.1 (2.0, 8.4) 1.6 (1.0, 2.4) 1.8 (1.2, 2.6) Continuous 8.4 (1.6, 43.5) 4.0 (1.3, 12.1) 1.9 (1.3, 2.7) 2.7 (1.5, 4.8) OR of infection/disease for individuals in communities that are classified as close or medium from Borbón as compared with those communities that are classified as far (remote). The continuous mea- sure is the OR comparing the farthest with the closest using a continuous measure of remoteness. Esti- mates were adjusted for age of individual, population size of village, and community-level sanitation.

APPENDIX A 221 can also increase flows of consumer goods such as processed food, material goods, and medicines and may also provide communities with increased access to health care, health facilities, and health information. By determining the transmission potential of the causal factors associated with new roads, we can better interpret the observed trends in infection rates across our study region. The propensity of a pathogen to persist within a com- munity is characterized by the reproductive number Ro, defined as the average number of infections caused by an infectious individual in a completely suscep- tible population (Anderson and May, 1991). For directly transmitted diseases, Ro is a function of (i) contact rate among others within or outside the community, (ii) infectivity (the probability of infection given a contact), and (iii) duration of the infectious period. For enteric pathogens that can persist in the environment, Ro is also a function of a pathogen’s viability outside the human host and its ability to move to a new susceptible one. The consistent and strong trends observed in these data across viral, bacterial, and protozoan pathogens suggest that Ro for many enteric pathogens is lower for remote villages compared with nonremote villages; i.e., these remote communities are less able to sustain transmission of pathogens. The trends in infection rates that we observed are partially explained by the effect of social connectedness on the risk of transmission of many pathogens. Figure A9-2 shows a causal diagram that illustrates how demographic changes, measured by rates of in- and out-migration for a community, and contact outside of village, measured by short-term travel of people in and out of a community, might increase levels of infection or disease for fecal–oral pathogens. Localized migration facilitated by roads can lead to a community whose residents have few social connections, which is one measure of social capital (Bebbington and Perreault, 1999). Previous studies have shown that communities with more social capital tend to be successful in creating adequate water and sanitation infrastruc- ture because they tend to know one another, are accustomed to working together, and share social norms (Grootaert and van Bastelaer, 2002; Isham and Kahkonen, 1998; Watson et al., 1997). On the other causal pathway, road proximity can increase the contact that individuals within a village have with those outside the village, increasing the rate of introduction of pathogens. FIGURE A9-2  Causal diagram linking proximity of the road to increases in infection and diarrheal disease.

222 FIGURE A9-3  Relationship between social factors and remoteness. (A) Movement outside of community, measured by the percentage leaving the village during the past week (linear fit R2 = 0.25, P ≤ 0.05). (B) The social connectedness within a community, as measured by the number of villagers a given individual spent time with during the past week (linear fit R2 = 0.50, P ≤ 0.05).

APPENDIX A 223 Our study villages show some evidence of these hypothesized relationships among demographic characteristics, social connectedness, and movement of people. Village data suggest that connectedness, as measured by the average number of individuals a given person spends time with (social network degree), is positively associated with remoteness (Figure A9-3B). Additionally, villages closer to the road have increased movement of people (Figure A9-3A), which provides opportunities for pathogen incursion. The slope of the line reflects the strength of the relationship: twice as many connections exist in the most remote village compared with the least remote. Likewise, 28% of the remote villagers said they had left the village in the last week, compared with 48% of the least remote villagers. Pathogen-specific outcomes provide additional insight into the relationship between remoteness and transmission. Observed trends were strongest for E. coli, followed by rotavirus and then Giardia. This differential can be partially explained by the biological and environmental factors that govern transmission dynamics and level of Ro; e.g., pathogen infectivity, as measured by infectious inoculum, shedding rates, and environmental persistence, as measured by the ability of the pathogen to remain viable in the environment, all directly affect Ro. Infectivity data suggest that Giardia, with a low ID50 (the inoculum at which 50% of exposed subjects are infected) and long shedding duration, and rotavirus, with a low ID50 and high shedding rates, are more infectious (Carter 2005; Regli et al., 1991; Teunis et al., 1986) than diarrhea-causing E. coli (Dupont et al., 1971, 1989; Feachem, 1983; Haas et al., 1999; Karch et al., 1995; Teunis et al., 1986). Diarrheagenic E. coli species tend to persist in the environment for shorter peri- ods of time than either Giardia or rotavirus (Carter, 2005; deRegnier et al., 1989; Enriquez et al., 1995; Estes, 1991; McFeters et al, 1974; Raphael et al., 1985). The above observations on both infectivity and environmental persistence suggest that Giardia is able to maintain transmission within the more remote vil- lages despite limited outside social contact and higher levels of social connected- ness. Likewise, E. coli would be less able to maintain transmission, and rotavirus would lie somewhere in between. The significant difference in E. coli infection rates between Borbón and the other communities and the lack of difference in Giardia infection rates are consistent with this hypothesis. The significant and consistent trends across viral, bacterial, and protozoan pathogens suggest the importance of considering a broad range of health out- comes when assessing environmental impact. Each of our marker pathogens has a different epidemiology that is affected by environmental changes in different ways. A stratified analysis that looks across pathogen types, and not just at a broader disease category like diarrhea, allows for a more sensitive measure of change and can elucidate more specific interventions to alleviate these environ- mental impacts. We propose this design as a general model that can be used to examine anthropogenic environmental determinants of health in other places.

224 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS A number of issues require further examination. In this regional analysis we compare remote and nonremote villages at a given point in time. Investigating changes in incidence compared with changes in remoteness over time may pro- vide additional causal information about how road development affects disease, because the time scale of these social changes may take years or decades, and the details are complex and poorly understood. In addition, molecular analysis of pathogens could elucidate transmission patterns across the landscape, and data on human migration patterns might provide information on causal linkages between roads and diarrheal disease. To substantiate the causal diagram shown in Figure A9-2, better measures of social capital and its relation to water and sanitation are needed. Gathering information on other health outcomes such as nutrition and vectorborne and sexually transmitted disease would also provide the opportunity to broaden our examination of causal linkages between road development and disease, because these are likely to vary for different etiologies. Environmental effects are often both geographically widespread and tempo- rally extended and therefore can be difficult to correlate with disease outcomes. The ability to observe change requires a study design and analysis that involve data collection within a systems-level framework. The natural experiment created by road construction in this region, combined with the regional design, allows these relationships to be studied. When associations between exposure and out- come are placed in the broader context of processes in which they occur (Figure A9-2), one can examine the causal linkages between environmental change and disease at a systems level. When international agencies like the World Bank make decisions about whether to invest or how best to proceed in large-scale infrastructure projects, their impact assessments have begun to pay attention to variables associated with environmental, social, and health factors (World Bank, 1997). Although the World Bank now inc