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Suggested Citation:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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:"Workshop Overview." 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.

Workshop Overview1 THE INFLUENCE OF GLOBAL ENVIRONMENTAL CHANGE ON INFECTIOUS DISEASE DYNAMICS The twentieth century witnessed an era of unprecedented, large-scale, an- thropogenic changes to the natural environment. Many of the planet’s natural resources are treated as a “commons,” wherein individuals have the right to freely consume its resources and return their wastes to the collective environment. The “logic of the commons” ultimately results in the ruin of the commons as well as the demise of those who depend upon it for survival (Diamond, 2005). The commons relationship between people and their environment was noted over 40 years ago by ecologist Garrett Hardin in a seminal article published in the journal Science (Hardin, 1968). His “tragedy of the commons” has proven to be a useful metaphor for understanding how we have come to be at the brink of numer- ous environmental catastrophes—whether it be land use, global climate change, or access to uncontaminated and plentiful freshwater resources. Simply stated, we face a serious dilemma—an instance where individual rational behavior, acting without restraint to maximize personal short-term gain—can cause long-range, catastrophic damage to the environment, others, and ultimately to oneself. The 2003 Institute of Medicine (IOM) report, Microbial Threats to Health, identified changing ecosystems; economic development and land use; climate 1  The planning committee’s role was limited to planning the workshop, and the workshop summary has been prepared by the workshop rapporteurs (with the assistance of Rebekah Hutton, Katherine McClure, and Priyanka Nalamada) as a factual summary of what occurred at the workshop. State- ments, recommendations, and opinions expressed are those of individual presenters and participants and are not necessarily endorsed or verified by the Forum, the Institute of Medicine, or the National Research Council, and they should not be construed as reflecting any group consensus. 1

2 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS and weather; and international travel and commerce as ecological and environ- mental factors that can influence the emergence and spread of infectious diseases. The last several decades have provided ample evidence of the impact of these factors—individually and synergistically—on the ecology of microbes, vectors, and animal reservoirs; the transmissibility of microbes; and the exposure path- ways between microorganisms and new hosts. For example: · Modern, intensive farming practices in association with trade, travel, and ecological change are implicated in the emergence of diseases including bovine spongiform encephalopathy (mad cow disease), foot-and-mouth disease, and Nipah virus. The livestock disease of greatest contemporary concern for global human health is avian influenza. The size and crowding of flocks found in many poultry farms—an ecosystem that could never exist in nature—creates many opportunities for infectious disease emer- gence, establishment, and spread. · The direct movement of people into habitats associated with bushmeat2 hunting inevitably leads to contact with a wide variety of wild animal species. In Central Africa alone, between 1 and 3.4 million tons of bush- meat are harvested annually. It is likely that bushmeat hunting and the associated trade in wildlife have contributed to the emergence of “novel” infectious diseases such as Ebola and HIV/AIDS. · Dam building and irrigation projects to “manage” the flow of freshwa- ter resources have been associated with the emergence and spread of infectious diseases including schistosomiasis, malaria, Rift Valley fever, filariasis, leishmaniasis, dracunculiasis, onchocerciasis, and Japanese en- cephalitis. In the case of schistosomiasis—a parasitic worm disease that causes chronic urinary tract disease and often results in cirrhosis of the liver and bladder cancer—humans become exposed to this parasitic worm as they work or bathe in water infested with schistosoma larvae released by snails. The rise in water levels and change in flow rates that result from dam building may increase the contact between snails and parasites, as well as create fertile soil and sand beds that propagate the development of snails. · Land use changes such as deforestation and irrigation are often associated with increased incidence of malaria—a disease of global health impor- tance that is responsible for more than 1 million deaths annually. Irriga- tion projects in India in the 1990s, for example, improved breeding sites for the dominant malaria vector and led to endemic “irrigation” malaria among roughly 200 million people. · Climate change has been implicated in the emergence of known diseases in new regions. Bluetongue, a midge-borne viral disease of ruminant 2  Bushmeat is a term commonly applied to meat of terrestrial wild animals, killed for sustenance or commercial purposes throughout the humid tropics of the Americas, Asia, and Africa.

WORKSHOP OVERVIEW 3 animals, is endemic in tropical and subtropical countries and can cause major morbidity and mortality in sheep. Since 1998, outbreaks of blue- tongue in mainland Europe have become common events, moving steadily northward. The disease emerged for the first time in northern Europe in 2006, during the hottest summer on record for that region—following nearly a decade of anomalously warm years. In the summer of 2007, the disease was reported in nine European countries, including the United Kingdom and Denmark, during a massive outbreak that affected tens of thousands of farms. · International travel and commerce drives the rapid global distribution of microbial pathogens and the organisms that harbor them. These include humans, whose migrations have been implicated in the spread of diseases including, but not limited to, SARS, drug-resistant malaria, and chikungu- nya (a mosquito-borne viral disease). In 2010, cholera—a disease that had been absent from the island of Haiti for more than 80 years—was brought to Haiti by humanitarian workers from Nepal, who served as United Na- tions peacekeepers in the aftermath of the island’s 2010 earthquake. Understanding how environmental factors directly and indirectly affect the emergence and spread of infectious disease has assumed global importance for life on this planet. While the causal links between environmental change and disease emergence are complex, progress in understanding these links, as well as how their impacts may vary across space and time, will require transdisciplinary, transnational, collaborative research. This research may draw on the expertise, tools, and approaches from a variety of disciplines. Such research may inform improvements in global readiness and capacity for surveillance, detection, and response to emerging microbial threats to plant, animal, and human health. Statement of Task Progress in understanding and addressing the complex causal links between environmental change and disease emergence will require collaborative research that draws on expertise, tools, and approaches from a variety of disciplines. The Forum on Microbial Threats hosted a public workshop on September 24 and 25, 2013, to explore the scientific and policy implications of the impacts of global en- vironmental change on infectious disease emergence, establishment, and spread. Participants examined and discussed the observed and potential influence of en- vironmental factors, acting both individually and in synergy, on infectious disease dynamics, and considered a range of approaches to improve global readiness and capacity for surveillance, detection, and response to emerging microbial threats to plant, animal, and human health in the face of ongoing global environmental change. This meeting served to update two previous Forum workshops, Infec- tious Disease Movement in a Borderless World (IOM, 2010) and Global Climate

4 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS Change and Extreme Weather Events (IOM, 2008), as well as the aforementioned report Microbial Threats to Health (IOM, 2003). Organization of the Workshop Summary This workshop summary was prepared by the rapporteurs for the Forum’s members and includes a collection of individually authored papers and commen- tary. The contents of the unattributed sections of this summary report provide a context for the reader to appreciate the presentations and discussions that oc- curred over the 2 days of this workshop. The summary is organized into sections as a topic-by-topic description of the presentations and discussions that took place at the workshop. Its purpose is to present information from relevant experience, to delineate a range of pivotal issues and their respective challenges, and to offer differing perspectives on the topic as discussed and described by the workshop participants. Manuscripts and reprinted articles submitted by workshop participants may be found, in alphabeti- cal order by participant, in Appendix A. Although this workshop summary provides a description of the individual presentations, it also reflects an important aspect of the Forum’s philosophy. The workshop functions as a dialogue among representatives from different sectors and allows them to present their views about which areas, in their opinion, merit further study. This report only summarizes the statements of participants over the course of the workshop. This summary is not intended to be an exhaustive exploration of the subject matter, nor does it represent the findings, conclusions, or recommendations of a consensus committee process. Accelerating Toward Tragedy Although Earth has undergone many periods of significant environmental change, the planet’s environment has been unusually stable for the past 10,000 years. This period of stability—known to geologists as the Holocene—has seen human civilizations arise, develop and thrive. Such stability may now be under threat. Since the Industrial Revolution, a new era has arisen, the Anthropocene, in which human actions have become the main driver of global environmental change. This could see human activities push the Earth system outside the stable environmental state of the Holocene, with consequences that are detrimental or even catastrophic for large parts of the world. —Rockström et al., 2009a Mounting scientific evidence supports the proposition that the rise of in- dustrialization in the nineteenth century ushered in a new geological epoch, the Anthropocene (Autin and Holbrook, 2012; Revkin, 2011; Steffen et al., 2011; Zalasiewicz, 2008). First coined by biologist Eugene Stoermer and championed

WORKSHOP OVERVIEW 5 by atmospheric chemist and Nobel laureate Paul Crutzen, the term Anthro- pocene—which has yet to achieve formal acceptance—marks the advent of humankind as an agent of global change. Researchers studying how humans influence the global environment have noted a sharp increase in anthropogenic, environmental impacts in the post–World War II era, a period they have named the “Great Acceleration” (Steffen et al., 2011). Figure WO-1 illustrates a range of social and economic indicators from the beginning of the Industrial Revolution to the beginning of the new millennium—each of which undergoes a dramatic shift around 1950. FIGURE WO-1  The increasing rates of change in human activity since the beginning of the Industrial Revolution. Significant increases in rates of change occur around the 1950s in each case and illustrate how the past 50 years have been a period of dramatic and un- precedented change in human history. From Steffen et al. (2004), including references to the individual databases on which the individual figures are based. SOURCE: Steffen et al., 2011.

6 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS According to Steffen and coauthors (2011), [o]ne of the most dramatic trends of the past half-century has been the wide- spread abandonment of the farm and the village for a life in the city. Over half of the human population—over three billion people—now live in urban areas, with the fraction continuing to rise. Migration to cities usually brings with it rising expectations and eventually rising incomes, which in turn brings an increase in consumption, forming yet another driver for the Great Acceleration. A similar suite of indicators, presented in Figure WO-2, reveals the wide-scale, global effects of human activity during the same time period, including rising atmospheric greenhouse gas concentrations, conversion of natural ecosystems to human-dominated landscapes, increasing reactive nitrogen in the environment (due to the use of fertilizers), and dramatic global loss of species biodiversity. The recent, rapid development of populous countries such as Brazil, China, and India is further spurring the Great Acceleration. Fueled as it is by Earth’s finite resources, the Great Acceleration cannot last forever. How close we are to collapse is a matter of considerable debate. What is clear is that the global environment has been rapidly and drastically altered by human activities at a scope and scale that is unprecedented in geologic time. Rockström and coworkers (2009a) have defined several biophysical thresholds that, if crossed, could be catastrophic for sustaining life as we know it on this planet (see Figure WO-3 and Table WO-1), including climate change, biodiver- sity loss, and the use of land and water. The authors emphasize those planetary systems underlying key thresholds may respond in nonlinear ways to environ- mental change, and that these systems are also tightly coupled. “If one boundary is transgressed, then other boundaries are also under serious risk,” they warn. Ecosystems and Disease Dynamics Energy, food, and water crises; climate disruption; declining fisheries; increas- ing ocean acidification; emerging diseases; and increasing antibiotic resistance are examples of serious, intertwined global-scale challenges spawned by the accelerating scale of human activity. —Walker et al., 2009 Changing Ecosystems—Changing Disease Patterns? Over the last century, large-scale, anthropogenic changes to the natural en- vironment have altered the structure and functioning of the world’s ecosystems at an accelerating pace. According to Myers and Patz (2009), We now appropriate 1/3 to 1/2 of global ecosystem production for human con- sumption. We have converted roughly 40 percent of the planet’s ice-free land surface to croplands or pasture. We use roughly half of the planet’s accessible

WORKSHOP OVERVIEW 7 FIGURE WO-2  Global scale changes in the Earth system as a result of the dramatic increase in human activity: (i) atmospheric CO2 concentration; (ii) atmospheric N2O con- centration; (iii) atmospheric CH4 concentration; (iv) percentage total column ozone loss over Antarctica, using the average annual total column ozone, 330, as a base; (v) Northern Hemisphere average surface temperature anomalies; (vi) natural disasters after 1900 re- sulting in more than 10 people killed or more than 100 people affected; (vii) percentage of global fisheries either fully exploited, overfished, or collapsed; (viii) annual shrimp production as a proxy for coastal zone alteration; (ix) model-calculated partitioning of the human-induced nitrogen perturbation fluxes in the global coastal margin for the period since 1850; (x) loss of tropical rainforest and woodland, as estimated for tropical Africa, Latin America, and South and Southeast Asia; (xi) amount of land converted to pasture and cropland; and (xii) mathematically calculated rate of extinction. Adapted from Steffen et al. (2004), including references to the individual databases on which the individual figures are based. SOURCE: Steffen et al., 2011.

8 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS FIGURE WO-3  Beyond the boundary. The inner green shading represents the proposed safe operating space for nine planetary systems. The red wedges represent an estimate of the current position for each variable. The boundaries in three systems (rate of biodiversity loss, climate change and human interference with the nitrogen cycle) have already been exceeded. SOURCE: Rockström et al., 2009a. surface fresh water. . . . To harness electricity, control flooding, and impound fresh water, we have built over 45,000 large dams (the size of a four-story building or larger) and an additional 800,000 smaller dams around the world, changing flows on roughly 60 percent of the world’s rivers. Resource use has also contributed to changes in global nutrient cycles and altered climatic patterns, which in turn may accelerate hydrological cycles and increase the likelihood of extreme weather events, such as droughts, heavy precipitation, heat waves, hurricanes, typhoons, and cyclones. The human population has grown from 1.6 billion in 1900 to more than 7 billion in 2012, and is interacting and gathering at increasingly high rates and densities (urbanization, mass migrations such as the Hajj, sporting and cultural events, cruise ships, etc.). These changes are bringing people, plants, animals, and microbes together in otherwise improbable combinations and environments. As detailed in the World

WORKSHOP OVERVIEW 9 TABLE WO-1  Planetary Boundaries Pre- Proposed Current industrial Earth-System Process Parameters Boundary Status Value Climate change (i) Atmospheric carbon 350 387 280 dioxide concentration (parts per million by volume) (ii) Change in radiative 1 1.5 0 forcing (watts per metre squared) Rate of biodiversity loss Extinction rate (number of 10 100 0.1–1 species per million species per year) Nitrogen cycle (part of a Amount of N2 removed from 35 121 0 boundary with the phosphorus the atmosphere for human use cycle) (millions of tonnes per year) Phosphorus cycle (part of a Quantity of P flowing into the 11 8.5–9.5 –1 boundary with the nitrogen oceans (millions of tonnes cycle) per year) Stratospheric ozone depletion Concentration of ozone 276 283 290 (Dobson unit) Ocean acidification Global mean saturation state 2.75 2.90 3.44 of aragonite in surface sea water Global freshwater use Consumption of freshwater 4,000 2,600 415 by humans (km3 per year) Change in land use Percentage of global land 15 11.7 Low cover converted to cropland Atmospheric aerosol loading Overall particulate concentration in the To be determined atmosphere, on a regional basis Chemical pollution For example, amount emitted to, or To be determined concentration of, persistent organic pollutants, plastics, endocrine disrupters, heavy metals, and nuclear waste in the global environment, or the effects on the ecosystem and functioning of the Earth system NOTE: Boundaries for climate change, rate of biodiversity loss, and nitrogen cycle have been crossed. SOURCE: Rockström et al., 2009a,b.

10 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS Health Organization’s (WHO’s) 2005 Millennium Ecosystem Assessment, pro- tection from infectious disease is one of the many ecosystem services provided by the natural environment. Only a tiny fraction of the known microbial species cause disease in their associated human, animal, and plant hosts—in part, because microbes are often constrained geographically or seasonally by complex ecologi- cal relationships with their hosts, vectors, and surrounding environments. Human- induced changes to the physical environment can alter these natural constraints on infectious agent range and activity in often unpredictable ways. Infectious disease dynamics, according to the WHO, are influenced by “destruction of, or encroachment into, wildlife habitat[s through] logging and road construction; changes in the distribution and availability of surface waters [through dam construction, irrigation and stream diversion]; agricultural land use changes, including proliferation of both livestock and crops; uncontrolled urbanization; climate variability and change; migration; and international travel and trade” (WHO, 2005). The reasons for the emergence or reemergence of some diseases in response to environmental change are largely unknown, but may include · Altered habitat leading to changes in the number of vector breeding sites or reservoir host distribution, or exposure to new host species; · Niche invasions or transfer of interspecies hosts; · Biodiversity change, including loss of predator species and changes in host population density; · Human-­ nduced genetic changes in disease vectors or pathogens—such as i mosquito resistance to pesticides or the emergence of antibiotic-resistant bacteria; and · Environmental contamination by infectious disease agents, such as fecal contamination of source waters. In this century, disruptions to the natural environment will continue to in- crease with both population size and intensity of economic activity. Several trends identified in the National Intelligence Council report Global Trends 2030, highlighted below, are likely to play a prominent role in shaping the world in the next 15–20 years and may also contribute to conditions that are favorable for the emergence and spread of infectious diseases (National Intelligence Council, 2012). Key trends include · Increased urbanization and migration: The mass relocation of rural populations to urban areas is one of the defining demographic trends of the latter half of the twentieth century. In 2012, close to 50 percent of the world’s population lived in urban areas, compared to only 30 percent in 1950. By 2030 60 percent of the world’s population will live in urban areas. Growing urbanization will spur economic growth but could put new

WORKSHOP OVERVIEW 11 strains on food and water resources, and historically such growth has led to reductions in forest covers, adverse shifts in soil quality, alteration to ecosystems (including local extinctions), and changes in the availability and quality of freshwater. Migration and urbanization also provide new pathways for infectious disease exposure. Much of the rapid urbanization occurring today is taking place in urban or peri-urban slums with few services for providing clean water, sewage disposal, solid waste manage- ment, or quality housing. Additional health hazards include those posed by open sewers and people living in close association with animals. · Increased demand for resources such as food, water, and energy: As the world’s population increases from 7.1 billion in 2012 to an estimated 8.3 billion people in 2030, coupled with an expansion of the middle class by 1 billion people, demand for food, water, and energy will grow by ap- proximately 35, 40, and 50 percent, respectively. Increased use of natural resources will likely be accompanied by severe deterioration of global ecosystems and strain the ecosystem services provided by freshwater re- sources, including aquatic habitat, fish production, water for households, industry, and agriculture. · Climate change: Climate change will worsen the outlook for the avail- ability of critical resources and is expected to reinforce additional con- tributors to infectious disease emergence, including global trade and transportation, land use, and human migration. A National Intelligence Council analysis suggests that in the twenty-first century, the severity of existing weather patterns will intensify, with wet areas getting wetter and dry and arid areas becoming more so. Much of the decline in precipita- tion will occur in the Middle East and northern Africa as well as western Central Asia, southern Europe, southern Africa, and the U.S. Southwest. An examination of these trends and of how their effects may vary worldwide, across populations and regions, may reveal important implications for targeting and improving infectious disease surveillance, detection, and response efforts. Human Habitat Expansion and Deforestation Infectious disease is a kind of natural mortar binding one creature to another, one species to another, within the elaborate edifices we call ecosystems. —David Quammen, 2007 The advance of human civilization has brought people, plants, animals, and microbes together in otherwise improbable combinations and locations. Such bio- logical introductions were once rare occurrences, but human actions have all but abolished spatial and temporal barriers between species and ecosystems (Carlton,

12 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS 2004). The profound consequences of human-mediated biological introductions include emerging infectious diseases (EIDs): those caused by pathogens that have increased in incidence, or in geographical or host range; those that have altered capabilities for pathogenesis; those that have newly evolved; or those that have been discovered or newly recognized (Anderson et al., 2004; Daszak et al., 2000; IOM, 1992). More subtly, but no less importantly, introduced animals, plants, and mi- crobes can disrupt ecosystems in ways that increase the potential for infectious disease outbreaks. Such changes can be difficult to anticipate and even more daunting to prevent. The term invasive species is widely used to describe plants and animals that, when introduced to new environments, spread aggressively (Dybas, 2004). Given both the links and similarities between such invasions to those of pathogenic microbes, it should prove fruitful to view the origins of disease emergence through the larger lens of biological invasion, and to consider common strategies to prevent and mitigate threats posed by all kinds of invasive species. Novel associations of pathogens, hosts, vectors, and reservoir organisms are well known to precipitate the emergence of zoonoses,3 anthropozoonoses,4 and vector-borne diseases.5 Biological introductions have prompted the emergence of vector-borne plant diseases of considerable agricultural importance. An epidemic of Pierce’s disease of grapes in California followed the introduction of a highly competent vector for an endemic bacterium, Xylella fastidiosa, which had been associated with low levels of disease for over a century (Fletcher and Wayadande, 2002). Until recently, this pathogen was transmitted by the blue-green sharpshooter (Grapho- cephala atropuntata), but with the arrival of the glassy-winged sharpshooter (Homalodisca coagulata) in the late 1980s, Pierce’s disease emerged as a major threat to the state’s viticultural industries. Similarly, the introduction of an ef- ficient Asian aphid vector to the Americas prompted the regional emergence of citrus tristeza virus, which currently threatens California orange crops (Anderson et al., 2004). And, the introduction of a vector-borne bacterial disease—referred to as citrus greening disease—is decimating the citrus industry throughout the state of Florida (Harmon, 2013; USDA/APHIS, 2014). Given the potential for such circumstances to introduce vector-borne patho- gens to immunologically naïve hosts and vectors, it should not be surprising that the initial human occupation of remote ecosystems has resulted in vector-borne disease (VBD) emergence, The clearing and settlement of tropical forests has ex- posed woodcutters, farmers, and domestic animals to new VBDs (Murphy, 1998). Some species of sandfly that carry leishmaniases—a group of parasitic diseases 3  Diseases transmitted from animals to humans. 4  Transmitted from humans to other animals. 5  Transmitted from one infected host to another through the actions of an intermediate animal such as a biting insect, snail, or rodent; includes many zoonoses, anthropozoonoses, and plant pathogens.

WORKSHOP OVERVIEW 13 that cause significant death and disability in many countries—and which had long resided in forests and fed on wild animal blood—have adapted to humans as a food source and to their dwellings as a habitat (Walsh et al., 1993). At the same time that VBDs have emerged from formerly isolated locations, vector-borne pathogens have entered new territories along with their human, animal, and plant hosts (Murphy, 1998). Deforestation also creates new habitats for pathogens and vectors. In South America, for example, epidemic malaria has occurred in recently logged areas where mosquitoes now thrive (Walsh et al., 1993). The modern agricultural practice of planting large acreages with a single monogenic crop is also associ- ated with the emergence of multiple types of fungal diseases and vector-borne plant viruses (Gray and Banerjee, 1999). Conversely, the explosive growth of the white-tailed deer populations and their attendant ectoparasites, coincident with the reforestation of former farmland and the construction of residential proper- ties in semi-rural areas, has been associated with the emergence and spread of Lyme disease (IOM, 2003). As Murphy (1998) has observed, when ecosystems are altered, disease problems follow. Disruptions to the natural environment are expected to continue unabated throughout this century, accelerating with the size, urbanization, and economic activity of the human population. A recent analysis by the National Intelligence Council—referred to earlier in this chapter—suggests that several key global trends will further encourage the emergence and spread of infectious diseases over the course of the next two decades (National Intelligence Council, 2012). Examining potential impacts of these trends within specific populations and regions may inform the targeting and improvement of infectious disease surveil- lance, detection, and response efforts. Health Impacts of Environmental Change (T)he real determinants of disease mortality are the environment and the popula- tion, both of which are being “doctored” daily, for better or for worse, by gun and axe, and by fire and plow. —Aldo Leopold, 1933 Health and the environment are inextricably linked. This simple truth, which underpins the practice of public health, has been borne out countless times over the history of life on Earth. Before delving into the specific health effects of climate change, land use, and biodiversity loss, keynote speaker Jonathan Patz of the University of Wisconsin noted various ways that the health–environment relationship has been conceptualized—ranging from Aldo Leopold’s terse assess- ment of the consequences of anthropogenic environmental change (above), to the epidemiological triad model of disease, shown in Figure WO-4, to the Health in

14 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS FIGURE WO-4  The epidemiological triad. The familiar epidemiological triad concept (host–pathogen–environment), as illustrated in the famous diagram of Snieszko (1974), neatly illustrates the complex interplay of factors that result in disease at the individual and population levels. The presence of a pathogen is a necessary but not sufficient cause of a particular disease (IOM, 2008). SOURCE: Snieszko, 1974. All Policies6 approach to sustainable development endorsed by the WHO and in- corporated into several recent international environmental agreements (Dr. Patz’s contribution may be found on pages 328–359 in Appendix A). Patz then turned to the primary focus of his presentation: health risks associ- ated with climate change. In a recent review article (Patz and Hahn, 2013), he and coauthor Micah Hahn illustrated the state of climate change with the following conclusions from the Intergovernmental Panel on Climate Change (IPCC): · Average North American temperatures in the mid- to late twentieth cen- tury appear to have been warmer than during any similar period in the last five centuries and likely the highest in at least the past 1,300 years. · From 1906 to 2005, global average temperature rose by 0.74°C. 6  The Association of State and Territorial Health Officials (ASTHO) defines Health in All Policies as “a collaborative approach that integrates and articulates health considerations into policymaking across sectors, and at all levels, to improve the health of all communities and peo- ple.”   SOURCE: http://www.astho.org/Programs/Prevention/Implementing-the-National-Prevention- Strategy/HiAP-Toolkit (accessed August 6, 2014).

WORKSHOP OVERVIEW 15 · The rate of change of global temperature is faster now than in any period in the last 1,000 years. · Since 1961, sea level has risen on average by approximately 2 millimeters per year, and over the next 90 years will rise between 20 and 85 cm. Patz and Hahn (2013) also noted that higher temperatures evaporate soil moisture, increasing drought risk, and also allow air to hold more moisture, pro- ducing heavy rains; the IPCC predicts increased incidence of such ‘‘hydrologic extremes’’ by 2100. At the same time, the melting of the Arctic and Antarctic ice sheets is raising ocean levels and potentially altering their currents. These changes, in turn, are likely to affect weather patterns but in different ways in dif- ferent locations (Patz and Hahn, 2013). Patz went on to observe that increases in air temperature and sea level, along with hydrologic extremes, will influence a range of environmental health issues. These include the immediate health consequences of extreme heat, ground-level smog, and ozone pollution, as well as less-direct phenomena such as the recently noted correlation between warm temperatures and extreme rainfall, and rates of interpersonal violence and intergroup conflict (Hsiang et al., 2013). Warmer summers are also associated with the risk of food insecurity (Battisti and Naylor, 2009). “We have almost a billion people at risk of hunger today,” Patz reported. “Malnutrition risks could double by mid-century.” Further focusing on infectious diseases, Patz offered the following examples of diseases showing evidence of influence by the suite of environmental shifts collectively known as climate change: temperature and VBDs; hydrologic ex- tremes and disease risk; and climate and land use synergies. Temperature and Vector-Borne Diseases Patz noted that diseases transmitted by cold-blooded vectors, such as arthropods,7 are especially temperature sensitive. Three characteristics of such vectors appear to account for this relationship: their geographic range; their rates of development, survival, and reproduction (as well as those of pathogens that they carry); and biting rates of infected vectors or the prevalence of infection in reservoir or vector populations (which affects the likelihood of transmission resulting from contact with a human) (Patz and Hahn, 2013). Malaria is a prominent example of a VBD with temperature-dependent trans- mission dynamics, Patz continued. The malarial parasite’s extrinsic incubation period—how long it takes to cross the stomach lining of its mosquito vector, reach the salivary gland, and develop to an infective stage—is strongly tempera- ture dependent. “The warmer the temperature the faster that mosquito becomes infectious,” he explained. “You have to have appropriate temperatures or you 7  Invertebrate animals of the phylum Arthropoda, such as insects, spiders, or crustaceans.

16 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS cannot have the tropical disease malaria.” He described the work by Pascual and colleagues who examined increasing malaria incidence in the relatively cool East African highlands, and its possible—and controversial—link to climate change (Pascual et al., 2006). The authors found evidence for a significant warming trend that was subsequently magnified in terms of mosquito population dynamics by at least one order of magnitude. “Our results emphasize the importance of con- sidering not just the statistical significance of climate trend, but their biological implications with dynamical models,” they wrote. Hydrologic Extremes and Disease Risk As the Earth warms, droughts and flooding rains are expected to become more frequent, raising the risk for a variety of infectious diseases (IOM, 2008). Heavy precipitation events—rainfall, snowfall, and storms—can inundate ag- ricultural fields, and overwhelm sewers and sewage treatment plants, allowing fecal pathogens to contaminate surface, drinking, and recreational water sup- plies (Patz and Hahn, 2013). Currently, combined sewer overflows in the United States amount to 1.2 trillion gallons per year—the amount of water that passes over Niagara Falls in 18 days, Patz reported (Whitman, 2000) (this issue was also considered by workshop speaker Joan Rose, of Michigan State University, whose presentation is described in the section “Anthropogenic Factors Driving Disease Emergence” on page 29). Using a suite of models of precipitation intensity and emissions scenarios, Patz and coworkers determined that sewer overflows into Lake Michigan from southern Wisconsin could increase by 50 to 120 percent by the mid-twenty-first century (Patz et al., 2008). Subsequent work by Sandra McLellan of the Uni- versity of Wisconsin-Milwaukee revealed high concentrations of E. coli in Lake Michigan near Milwaukee following storm-water–induced sewage overflows in that city (McLellan et al., 2007). “When it rains, you have contamination,” Patz insisted. “If it is going to be raining harder with climate change that is a public health concern.” By contrast, outbreaks of West Nile virus (WNV)—a mosquito-borne in- fection that can cause fever and severe neurological complications, and which emerged in the Western Hemisphere in the late 1990s—have been associated with heat waves and drought, Patz observed. Several factors contributed to WNV emergence in the Western Hemisphere, including the ubiquity of international transportation. The role of climate appears to involve the virus’ principal vector, Culex spp., which is well adapted to “dirty, concentrated urban environments,” Patz explained. “This is why drought conditions in the summertime can actually favor that mosquito that doesn’t get flushed out of the storm drains.” Accordingly, he observed that during the summer of 2012, when more than 1,000 temperature records were broken in the United States, “It was a banner year for West Nile virus.”

WORKSHOP OVERVIEW 17 Reisen and coworkers (2006) experimentally determined that the strain of WNV that emerged in New York City during a record heat wave in July 1999 required warmer temperatures for efficient transmission than its South African counterpart. They further noted that “the greatest WNV transmissions during the epidemic summers of 2002–2004 in the United States were linked to above- average temperatures” (Reisen et al., 2006). In addition to regional outbreaks, “hot spots” for WNV transmission were also found to occur within a relatively the small area of the city of Chicago, and may be correlated with the effectiveness of local water drainage, Patz reported (Loss et al., 2009). Identifying such micro- level environmental differences in arboviral transmission will help in predicting future outbreaks, he concluded. Climate and Land Use Synergy As the previously noted study of malaria demonstrates, small changes in ambient temperature can produce significant biological effects, often in combi- nation with additional environmental variables (Pascual et al., 2006). Patz noted that, “when we think about climate change, we really cannot view it in isolation. We need to look at synergistic issues on the ground. Things happen locally.” Similarly, he added, local factors may dampen the effects of climate change. To illustrate this point, Patz described another malaria study, in which a temperature differential introduced by deforestation was found to significantly increase repro- ductive fitness of the mosquito vector (Afrane et al., 2006). Indeed, the transformation of wild lands through human enterprise is pro- ceeding exponentially around the globe and—as Andrew Dobson, of Princeton University, argued in his keynote address to the workshop (see the section “Un- derstanding Infectious Disease Dynamics” on page 20)—may represent an even more consequential source of environmental upheaval than climate change. As we alter the landscape, Patz queried, “Are we giving up some ecosystem services and intact functioning ecosystems that could be useful for human health?” To explore this question on a local scale, Patz’s group compared the behavior of the primary mosquito vector of malaria, Anopheles darlingi, between forested and deforested sites along a road in the Peruvian Amazon (Vittor et al., 2006). “There were lots of mosquitoes in the forest, but just not the main one that carries malaria,” Patz explained. “So this is a biodiversity story: the bad actor mosquito was in the disturbed landscape.” The researchers also determined that deforesta- tion provoked ecological changes—especially in the aquatic environment where Anopheles darlingi breeds—that favored the species’ presence (Vittor et al., 2009). The causes of this altered biodiversity remain to be determined, he said, “But you see an altered mosquito biodiversity from this land-use change, and you also have artificial breading sites from road culverts, fish ponds, and a higher abundance of Anopheles darlingi, leading to a high risk of malaria.”

18 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS Patz observed, however, there is little site-specific data linking actual hu- man cases of malaria to deforestation. To address this question, Patz’s group analyzed malaria reports obtained from Brazilian health districts together with satellite imagery to determine the relationship between deforestation and malaria incidence in a single county. His group calculated that for every 4 percent loss in forest cover, malaria incidence increased by 48 percent (Olson et al., 2010). Similarly, he reported that graduate student Micah Hahn, in collaboration with speakers Steve Luby, of Stanford University, and Peter Daszak, of the EcoHealth Alliance (whose presentations are summarized in the section, “Approaches to Identify and Address Factors Contributing to Disease Emergence” on page 76), recently examined the relationship between land use and spillover (bat-to-human transmission) of the Nipah virus in Bangladesh. The investigators discovered that viral spillover tends to occur more frequently in forested areas, a local phenom- enon limited to certain villages within a general area known as the “Nipah belt” (Hahn et al., 2013). Addressing the Health Consequences of Environmental Changes The multifactorial nature of global environmental change means that the health effects of its components are interdependent, and therefore need to be considered together (Rockström et al., 2009a,b). “The good news is that if the risks are all interconnected so too will be the [intervention] opportunities,” Patz declared. Both urban and rural settings offer abundant design opportunities to reduce humankind’s environmental impact, he continued. Urban adaptations could include the use of porous surfaces, in order to reduce runoff and thereby minimize or mitigate the risks associated with waterborne diseases, he noted. He also described a system to convert potentially infectious human and animal waste to biogas fuel, recently constructed in rural Uganda, which provides both economic and health benefits. During the discussion that followed Patz’s presentation, Forum chair David Relman, of Stanford University, raised the possibility that “natural experiments,” in environments where the effects of climate change were found to be minimal, might reveal mitigating factors that could be exploited elsewhere. Patz agreed, and noted two examples of such observations that shade-grown coffee plantations fared far better than open-grown plantings during Hurricane Mitch;8 and that minimizing asphalt “heat islands” in cities could reduce the impact of heat waves. Several participants advised framing the discussion of the health effects of environmental change in ways that emphasize the benefits of environmental stew- ardship, rather than warn against future catastrophe, which they dismissed as (at 8  Hurricane Mitch was the most powerful hurricane and the most destructive of the 1998 Atlantic hurricane season, with maximum sustained winds of 180 mph (285 km/h). The storm was the thir- teenth tropical storm, ninth hurricane, and third major hurricane of the season.  SOURCE: http:// en.wikipedia.org/wiki/Hurricane_Mitch (accessed April 7, 2014).

WORKSHOP OVERVIEW 19 best) ineffective. Patz agreed, and observed that many steps that could be taken to improve human health, such as redesigning cities to emphasize human-powered and public transportation, would also address climate change. For example, he said, “We did a study showing that switching short car trips to bicycle trips would save $8 billion in avoiding mortality and health costs every year just for our re- gion” (Grabow et al., 2012). A convincing business case for addressing the health effects of global envi- ronmental change needs to be made, advised Forum member Jeffrey Duchin, of the Seattle and King County Department of Public Health. “There are plenty of examples in the history of medicine and science of problems that were recognized as big issues from the scientific and human health perspectives that did not get addressed until someone realized that it affected the bottom line,” he observed. For example, he said, the threat of antimicrobial resistance (another example of a “tragedy of the commons”) was largely ignored until the infectious disease community convinced hospitals that they would save money by taking preventive measures against resistance. Patz suggested that an important tool for making such a case is the health impact assessment (HIA), a systematic process to evaluate the potential health effects of a plan, project, or policy prior to its implementation (CDC, 2013c). HIAs can demonstrate to policy makers the health benefits their communities can expect to gain from specific environmental protections and can expose un- intended consequences of environmental development projects that might seem, on balance, beneficial to growing economies, he explained. “Health needs to be on the table, and there are trade-offs,” Patz stated. “That is step one. Step two is that there are better ways to develop” than to pursue Western-style fossil fuel–dependent growth. “You can’t tell a country ‘Don’t cut that tree, and don’t burn those fossil fuels,’” he continued. “We can tell them there are ecosystem services. There are alternative transportation options . . . [and] an opportunity to have a better city if you forego the path we took for a newer path.” “Economic growth and technological progress should be partners and friends of the ecology and the environmental movement,” added Forum member Lonnie King, of Ohio State University. Although seemingly at odds in the current market- place of ideas, both objectives could be united by a value proposition that defines investment in the environment as a means to a society’s long-term well-being, he asserted. On the other hand, observed Forum member Jesse Goodman, of the U.S. Food and Drug Administration,9 even economic trade-offs that we can all agree could be win-win long term, considering the big picture, may not result in needed policy changes if there is a disconnect between who reaps immediate economic rewards (such as in fossil fuels) and who pays for long term broader effects and costs (e.g., to the environment). “Right now, with respect to fossil fuels and 9   Dr. Goodman is now at Georgetown University.

20 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS global warming, the entities that are making money are not then economically accountable for the environmental costs accrued by the world as a whole.” Such disconnects will continue unless policies, including incentives, such as a carbon tax—begin to broadly link benefits to costs,” he stated. “Unfortunately, we don’t have an international governance system that can deal with that challenge,” Patz replied. “That is the toughest problem, I think.” Returning to the local level, several participants expressed frustration at the lack of evidence-based recommendations available to help policy makers under- stand and address the health effects of climate change that they are confronting in their communities. For example, during last summer’s severe WNV outbreak in Dallas, Texas, some neighborhoods were affected far worse than others, though all suffered drought and record temperatures, recalled Beth Bell of the Centers for Disease Control and Prevention (CDC). “Policy makers are asking me, why is this, and what do we do about it? My answers from the community perspective are pretty limited,” she said. To develop such guidance, Forum member Carole Heilman, of the National Institute of Allergy and Infectious Diseases (NIAID), suggested that such local variations should be studied in detail, perhaps by ecolo- gists working alongside epidemiologists during outbreak investigations. “Where you have to start building is locally,” Heilman insisted. “They are the people who make decisions in terms of what our laws really are,” she continued. “There are probably areas that we can change attitudes . . . [including those of] important people that will be involved and invest in changing the world.” Understanding Infectious Disease Dynamics Identifying and interpreting how multifactorial, interacting environmental forces influence the emergence and spread of infectious diseases is essential to reducing the harm they cause. Keynote speaker Andrew Dobson, of Princeton University, an ecologist studying infectious disease dynamics, offered a provoca- tive view of this effort by attempting to debunk the following six misconceptions about emerging pathogens, as he described them (Dr. Dobson’s contribution may be found on pages 193–212 in Appendix A). Misconception One: Disease Has No Effect on National Economies “One of the most cited papers in economics . . . explains how everything about economies of different countries is simply a product of governance,” Dobson reported, referring to the work of Acemoglu and coauthors (Acemoglu et al., 2001). Posing the question, “What are the fundamental causes of the large differences in income per capita across countries?,” these economists assert that disease merely shaped governmental institutions established by nations in their colonies—institutions that persist to this day and that strongly influence each former colony’s relative economic success.

WORKSHOP OVERVIEW 21 This analysis statistically removes the significant contemporary effects of diseases that infected early settlers, such as malaria, schistosomiasis, and hook- worm, Dobson declared. Dobson and coworkers conducted their own version of this analysis, in which they estimated the relative effects of vector-borne and parasitic diseases and income on each other, and found a significant inverse as- sociation between residual levels of disease and per-capita income (Bonds et al., 2013). “The diseases still causing the biggest economic damage are things like malaria,” he observed. “So we have to keep worrying about those if we are wor- ried about the world’s economy, and indeed I would say we should be worried about those certainly as much as governments.” Their analysis also suggested that the economic effects of vector-borne and parasitic diseases are to some ex- tent buffered by local biodiversity—a strong argument for protecting it, Dobson concluded. Misconception Two: Climate Will Drive Disease Emergence Climate change will influence infectious disease dynamics, Dobson said, but its significance may be difficult to discern among the multiple, interacting forces underlying global environmental change. As Patz also noted, changes in land use—prompted by rapid growth in the human population—are dramatically transforming the global environment. According to the Millennium Ecosystem Assessment, a comprehensive scientific appraisal of the state of global ecosys- tems and ecosystem services released by the United Nations in 2005 (United Nations Environmental Programme, 2005), the influence of land use as a force for environmental change over the next century will at least equal that of climate change in tropical and temperate regions, as illustrated in Figure WO-5. “Climate change will have a huge effect on the tiny proportion of birds (and all other species) that live up in the Arctic. Land use change will have a huge ef- fect on the huge proportion of birds that live near the equator, and that is going to be true for all biodiversity,” Dobson predicted—including pathogens and humans. As a result, he explained, signals of climate change effects on infectious diseases will be difficult to detect in the tropics, as has occurred in the case of malaria (as Patz also noted). This controversy illustrates the importance of recognizing the heterogeneous nature of global environmental change, Dobson concluded. Misconception Three: Maps Can Predict Pathogen Emergence Dobson took issue with the landmark publication by Jones et al. (2008), suggesting that it purports to identify global hot spots for infectious disease emer- gence, but instead maps global disease research centers. “What happens if you take the first half of the data [used to plot these hot spots], and try and predict the second half of the data?,” he wondered. “No one has done that.” If they did, he predicted, one might find such phenomena as high skill for prediction of sites for

22 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS FIGURE WO-5  Patterns of change in land cover due to land use and climate change by 2100. Patterns are given for the environmentally proactive “Adapting Mosaic” scenario as well as the environmentally reactive “Order from Strengh” scenario. (A) Adapting Mo- saic, 2100. The “Adapting Mosaic” scenario sees the rise of local ecosystem management strategies, and the strengthening of local institutions. There is also great variation among nations and regions in styles of governance, including management of ecosystem services. Some regions explore actively adaptive management, investigating alternatives through experimentation. Others employ bureaucratically rigid methods to optimize ecosystem performance. Eventually, the focus on local governance leads to failures in managing the global commons. Problems like climate change, marine fisheries, and pollution grow worse and global environmental problems intensify. Using good regional solutions and discarding poor ones eventually improves approaches to a variety of social and environ- mental problems, ranging from urban poverty to agricultural water pollution. As more knowledge is collected from successes and failures, provision of many services improves. (B) Order from Strength, 2100. This scenario represents a regionalized and fragmented world, emphasizing primarily regional markets, and paying little attention to common goods. The role of government expands. Regionalization exacerbates global inequality. Treaties on global climate change, international fisheries, and the trade in endangered spe- cies are only weakly and haphazardly implemented, resulting in degradation of the global commons. Ecosystem services become more vulnerable, fragile, and variable in Order from Strength. For example, parks and reserves exist within fixed boundaries, but climate changes around them, leading to the unintended extirpation of many species. Conditions for crops are often suboptimal. As a result, there are frequent shortages of food and water, particularly in poor regions. Low levels of trade tend to restrict the number of invasions by exotic species; however, ecosystems are less resilient, and invaders are therefore more often successful when they arrive. SOURCE: Jetz et al., 2007.

WORKSHOP OVERVIEW 23 emergence of drug-resistant pathogens as these will consistently occur in areas with modern hospitals and high levels of drug use. They will be very limited in their ability to predict hot spots for emergence of novel pathogens. “Maps are only as powerful as the data and analysis that goes into their construction,” he warned. “It is essential to clearly state the underlying analysis that creates a map, rather than assume powerful mapping techniques have done this for you.” In reply, Daszak noted that Dobson may have referred to the wrong map in his formal presentation—a map of raw data—rather than the predictive hot spot maps that correct for reporting bias by including a measure of global research ef- fort that “specifically accounts for the location of disease identification centers!” Daszak went on to observe that his research group included a specific measure of reporting bias in their General Linear Models that produced the hot spot maps. 10 Daszak reminded the Forum members that these maps were not intended to invite fine comparisons between specific locations, but rather to reveal broad trends and associations including that the majority of pandemics, such as wildlife zoonoses, have emerged in the tropics. Daszak provided a detailed account of the theory and process of disease mapping in Jones et al. (2008) in his presentation, which is summarized in the section “Approaches to Identify and Address Factors Con- tributing to Disease Emergence” on page 76. Misconception Four: Virus Hunting Is an Effective Strategy for Detecting Emerging Diseases Virus hunters who head for the tropics in search of the next emerging patho- gen are, in Dobson’s opinion, wasting time and resources for two key reasons re- lated to infectious disease dynamics. First, he observed, “Wherever you go, there is a huge amount of undiscovered bacterial and viral tissue. We do not have to go off to the swamps of Africa to find it.” To illustrate his point, Dobson described his group’s intensive study of three estuarine ecosystems, which proved to be rife with microbes (Hechinger et al., 2011). “There is much parasitic biomass mov- ing around in those salt marshes—and I would posit, in any other ecosystem—as there is free-living biomass. It is just in tiny little particles, and it turns over really quickly,” he said. “Parasites are probably the dark matter that holds the whole ecosystem together.” Their results also suggest that an organism’s parasite load scales with its tro- phic level (its position in the food chain),11 and that species abundance scales with 10  “This work involved gathering information on around 15,000 authors of papers from Journal of Infectious Diseases, then gridding each author out spatially with a specific latitude and longitude—a huge amount of work that Dr. Dobson may have overlooked in reading the paper.” 11  Primary producers such as photosynthetic plants occupy the first trophic level; herbivores that feed on the plants form the second trophic level; carnivores feeding on herbivores occupy the third trophic level, and so on. SOURCE: http://www.biology-online.org/dictionary/Trophic_Level (ac- cessed August 6, 2014).

24 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS body size, Dobson explained (Hechinger et al., 2011). Guided by these scaling laws, the abundance of viruses and bacteria in a given ecosystem can be readily estimated based on an understanding of feeding patterns. As a result, Dobson concluded, “Undiscovered viruses are abundant everywhere . . . [and] we need to think more about the dynamics of emergence than the romance of fishing for viruses with computer chips.” Dobson’s second criticism of virus hunting derives from an understanding of two dynamic features of emerging pathogens: basic reproductive number (R0) is the number of cases one case generates on average over the course of its infec- tious period in an otherwise uninfected population (Fraser et al., 2009). Because SARS and smallpox have relatively low R0s and produce symptoms quickly, it was possible to control those diseases by isolating infectious people, he explained. Influenza, with its higher R0 and lag between infectious and symptomatic stages, and HIV/AIDS, with an extended asymptomatic period, are difficult to impossible to eradicate, he said. “Would virus hunters have detected HIV? I think it would have gone straight past them,” Dobson asserted. “Very long incubation period— so you are likely to dismiss it as harmless. Cost of extensive lab trials—you are never going to get money in the current climate for that. And HIV is still the emerging pathogen that has had a bigger impact than all the others put together.” Daszak also took issue with this point and noted that the frequency and impacts of pandemics are growing, so discovering new approaches for proactive infectious disease identification might be a critical strategy that should be taken seriously. “I concede that finding a new virus does not necessarily mean we can stop it.” However, Daszak continued, the majority of emerging diseases originate from mammals in the tropics, so that is where efforts should be concentrated. “If we are going to sit here and wait for them to come to our backyard, as you sug- gest, then are we too late? Shouldn’t we get there ahead of the curve, go to places where diseases are likely to emerge, and try and at least characterize the diversity of fauna viruses out there so we can do something about it?” Relman observed that while the emerging diseases shown on Daszak’s maps constitute overt cases of pathogenicity (parasites receiving benefits by damaging their hosts), Dobson’s analyses incorporated more subtle and ongoing forms of parasitism, which in some cases are merely assumed to occur based on micro- bial taxonomy. These two models of parasitism differ not only in terms of the magnitude of damage to the host, but also in the duration of the host–parasite relationship, he added. “How does that play into the way in which you devise a model, the ways in which you try to estimate impact on a network when you think about a wide range of both magnitude of damage and timescale over which it happens?” he asked Dobson. All parasites cause energetic damage, Dobson responded. “Any form of para- sitism, even if it is a tiny microbe or a virus, is causing some energetic change in its host [at] some cost. It may be incredibly small, but aggregated across all the viruses and all the worms it will build up.” Moreover, he added, the statistical

WORKSHOP OVERVIEW 25 distribution of parasites across the host population is such that the few hosts at the top of the food chain receive the bulk of damage caused by parasites, which in turn damages the parasites. Relman agreed, but speculated that, when all selec- tive factors—not just energetics—are taken into account, some parasites may not actually prove harmful to their hosts. Misconception Five: Emerging Pathogens Will Evolve to Higher Virulence What happens when an infectious disease spreads rapidly and no one tries to stop it? Does it necessarily gather virulence like a ball rolling down a hill gath- ers momentum? Dobson described a model system for exploring this question: a bacterial pathogen of birds, Mycoplasma gallisepticum, which, in 1993 in the northeastern United States, jumped from domestic poultry into house finches, in which it causes a readily identifiable eye infection. The disease spread rapidly through the regional finch population, causing dramatic population declines but little local extinction, he reported—so the disease’s main impact was to reduce the size of the birds’ typical social groups. Upon reaching less abundant finch populations in the north and Midwest, the rate of disease spread slowed, but in- creased again when it reached the more densely populated western United States (Hochachka et al., 2013). After Mycoplasma gallisepticum emerged in house finches on the East coast, it became increasingly virulent, Dobson reported—but as it spread across the country its virulence declined (Hawley et al., 2013). He and coworkers then rep- licated this natural experiment in the laboratory, demonstrating that high rates of contact between groups favors a relatively stable evolution of virulence; they also developed a predictive mathematical model based on their results. “We can use this system to actually look at the dynamics of the interaction between the pathogen and the host immune systems,” he continued. “We can take samples from the bird’s eyes, look at what the bacteria population is doing from day to day, and we can look at what the immune system does in response to that. So we can make models of the immune system that look at the dynamics of the interaction between the pathogen and the host immunity.” Misconception Six: Immunology Is Science and Ecology Is Natural History As the virulence evolution model demonstrates, an ecological view of the immune system offers both insight and predictive capacity. To provide further evidence of the advantages of this approach, Dobson depicted the vertebrate im- mune system as a network of predator/prey relationships known as a food web (see Figure WO-6). Dobson’s group develops mathematical models to characterize food webs and identify the dominant interactions among them. To demonstrate how this works, he offered the example of the Serengeti, a complex system they found to

26 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS be dominated by a few major interactions, particularly that between wildebeest and rinderpest virus. Based upon mathematical descriptions of these relationships, Dobson and colleagues were able to construct a predictive model of interspecies dynamics (Holdo et al., 2009). Using the same mathematical tools, he said, “You can write the immune system down as two equations for each of the different dominant terms in their interaction.” As compared with mainstream, descriptive, immunological studies—which Dobson characterized as “natural history”—the mathematical approach favored by ecologists affords “a much deeper understand- ing of how the immune system works,” he asserted. “If we really want to understand emerging diseases we need to move beyond high-tech natural history description of novel components to develop mathemati- cal models that capture the essential dynamics of immunity, and then examine how those things vary as we move from the mammals to the birds to the bats,” Dobson concluded. This approach could, among other things, reveal why bats are reservoirs of several emergent pathogens, including the SARS, Nipah, and Hendra viruses (Dobson, 2005), he suggested. More generally, he later noted, lit- tle is known about the energetics of the immune system; what energetic costs does fever impose on host and parasite, for example? Answering that question would require thinking about the immune system in purely thermodynamic terms, he observed—an example of “risky science” that, in his opinion, was worth doing. Heilman, of NIAID, noted that while the agency is responsible for funding basic science that can improve human health, such as Dobson’s approach to immunology, the National Institutes of Health are also involved in identifying and developing interventions for individual diseases. “Can you explain to me how your ecological network or food network could develop targets for human intervention?” she asked. Dobson agreed that identifying disease-specific targets is a worthwhile activ- ity beyond the reach of his approach. However, he continued, target-based inter- ventions act within metabolic networks, which are essentially food webs, through which the knock-on effects of the intervention can be anticipated. Mathematical epidemiology has, for example, shown how the average age of first infection for rubella might change as we vaccinate, he observed. “If you just identify targets and try and hit them you are going to get side effects that might be much more damaging than not having intervened in the first place,” he concluded. “You cannot predict those until you have some sort of food web model of how those things interact.” “You reduce some very complicated systems, like immune function, to some very simple mathematically defined models,” Duchin said to Dobson. “How do we develop models that are better at predicting, or how would you suggest we approach the idea of modeling the determinants of the emergence of disease, rather than just describing it better?” “The type of models I am interested in are not really designed for predic- tion, they are designed for understanding,” Dobson replied—models like the

FIGURE WO-6  These two figures illustrate computer realizations of the network structure of (a) the fauna and (vertebrate) fauna of Yellowstone National Park (Barmore, 2003; Frank and McNaughton, 1992) and (b) the vertebrate immune system (based on Cox and Liew, 1992). The cen- tral point of this exercise is to illustrate that food webs and the vertebrate immune system have a network structure that can be examined using ecological food web models. The figures were realized using Network 3D Version 1.0.0 Copyright Microsoft Corporation; Microsoft Research and PEaCE Lab, C# OpenGL Framework Copyright © 2006 devDept, Berkeley, California. SOURCE: Images courtesy of Andrew Dobson. 27

28 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS immunological “food web,” developed from the simplest assumptions of how a system works, which reveal its essential dynamics. “If those dynamics are very complicated, even with the simplest possible model, then prediction is going to be hard,” he observed—or worse than that, dangerous, because unlike the structural models he favors, they cannot be held up against reality and corrected, except in hindsight. “I would rather understand the processes, understand why the bat’s immune system is different from other mammals, why can it have things in it that suddenly become pathologic. That has to be some function of the way the physiology of bats work versus the physiology of the other mammals.” Similarly, with regard to emerging diseases, Dobson said, it is important to characterize pathogen diversity, including the various ways pathogens interact immunologi- cally with different host species, and to attempt to develop a predictive framework for those interactions. Where pathogens are going to come from, he said, “is the least interesting of those questions.” Forum member Jacqueline Fletcher, of Oklahoma State University, pursued the notion, raised earlier by Relman, that the category of parasites includes in- digenous microbes that are harmless or beneficial to their hosts. How are such interactions factored into models of emerging diseases, she inquired? What might shift such microbes to become pathogens—a new host? Environmental change? “I think we’ll never know what proportion of viruses or bacteria will be epi- demic, because to know that we would have to take every virus and test it in every host species that it goes into,” Dobson replied. Moreover, he said, a single host is likely to respond differently to a given pathogen under different environmental conditions, such as if the host is malnourished or stressed. Nipah virus underwent such a transition as a result of changes in land use, which caused it to infect pigs and humans in addition to its longstanding host, bats, noted audience participant Dr. Joe Dudley of SAIC. Viruses do not gain par- ticular advantage in “jumping” host species, he observed; spillover is a product of global environmental change. “In the tropics, you have more people, you have more vectors, you have more viruses, and you have more disruptions, and more people making more people more quickly.” Upon introduction to a host, pathogens are generally surrounded by com- munities of diverse indigenous microbes, noted Forum member Margaret McFall- Ngai. Can these complex relationships be factored into models, she wondered? They can be captured in essence and corrected through comparison with relevant data, Dobson said. However, he added, “The microbiome is fascinating, but I just see it as something that kicks up [the immune response].” At the conclusion of his talk, Dobson posed this rhetorical question: should we be worried, about emerging disease? “Yes, particularly if you . . . don’t believe in evolution,” he remarked. “Yes times six if you believe . . . any of the above misconceptions. No, I don’t think you should be worried, because I think the field has produced many bright young people, many of whom would agree completely with [my view of] the above misconceptions. And no, we should not worry if we

WORKSHOP OVERVIEW 29 change the way we fund things. But if we continue to fund things the way we are, be very worried, and perhaps start praying.” Anthropogenic Factors Driving Disease Emergence The global environmental impact of human activity results from the collec- tive effects of interacting processes, including travel, trade, migration, urbaniza- tion, conflict, land development, water use, and fossil fuel combustion—each of which represents a potential influence on infectious disease dynamics, but none of which is likely to act alone. A series of workshop presentations examined the effects of anthropogenic factors on infectious disease establishment, adaptation, and spread, and through case studies, revealed multiple forces at work in shaping disease patterns at the local scale. Travel and Trade Perhaps no aspect of the Great Acceleration is better described by that phrase than the recent, rapid expansion of the human capacity for mobility, a hallmark of our species. Figure WO-7 offers three perspectives on this phenomenon and its intersection with population growth. A central feature of global environmental change, increased human mobility and migration have both caused and resulted from environmental influences (such as deforestation, drought, land use, climate change) and sociopolitical upheaval (urbanization, globalization, and conflict) (IOM, 2010). “There are millions of people engaged in [an] international journey every day,” observed speaker Martin Cetron, of the CDC (Dr. Cetron’s contributions may be found on pages 165–170 and 170–181 in Appendix A). He noted that more than 1 billion individual international border crossings take place each year, among them those of an estimated 900 million tourists (Castles and Miller, 2009). The explosive growth of travel and trade over the last century, having all but eliminated previously existing spatial and temporal barriers between the world’s species and ecosystems, now drives the global distribution of microbial pathogens and the organisms that harbor them (Carlton, 2004; IOM, 2003, 2010). Of particular interest is the practice of medical tourism, discussed in Box WO-1. Human migration has long been associated with the spread of disease. The practice of quarantine originated in fourteenth century Venice, and was intended to prevent the spread of the Black Death into the city (and continent) by isolating passengers and products on arriving ships for 40 days before they were permitted to come ashore, Cetron explained. In the United States, cholera outbreaks among immigrants in the nineteenth century led to the stigmatization of immigrants as bearers of disease and the creation of a national quarantine program. As has oc- curred throughout history, immigrants were thought to be disease vectors, rather than victims of disease, he observed—a notion that ignores complex interactions

30 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS FIGURE WO-7  (A) Travel over four male generations of the same family. Each map shows in a simplified manner the individuals “life-time tracks” in a widening spatial con- text, with the linear scale increasing by a factor of 10 between each generation. (B) Speed of global travel and population growth. (C) International plane arrivals. Global aviation network. A geographical representation of the civil aviation traffic among the 500 largest international airports in >100 different countries is shown. Each line represents a direct connection between airports. SOURCES: (A) Cliff and Haggett, 2004; (B) Murphy and Nathanson, 1994; (C) Hufnagel et al., 2004.

WORKSHOP OVERVIEW 31 BOX WO-1 Medical Tourism and Infectious Disease International travel for medical treatment has undergone rapid expansion, par- ticularly among residents of developed countries, who receive medical procedures in low- or middle-income countries at often vastly lower cost, and more quickly, than they would in their home countries. Common procedures include dental work, arthroplasty, certain surgeries (cataract, bariatric, cosmetic, and cardiac proce- dures), reproductive care, and tissue and organ transplants. Popular destinations for medical tourists include the Caribbean, China, Europe, India, Latin America, Mexico, the Middle East, Pakistan, Singapore, and Thailand. Although limited data exist to describe medical tourism on the basis of volume, destinations, services, or procedures received, as many as an estimated 4 million patients have received treatment outside their home countries each year. Medical tourists (as well as all travelers who receive medical care away from home) are vulnerable to both procedure- and travel-related infections. They also risk introducing pathogens and resistance determinants to new host populations while abroad, and upon their return home. Malaria, dengue, and other infections are endemic in many countries with high volumes of medical tourism. These coun- tries may also have high background rates of tuberculosis, antibiotic resistance, hepatitis B and C, and HIV. Blood and blood products used in hospitals certified by Joint Commission International (http://www.jointcommissioninternational.org/ About-JCI) are screened for common blood-borne pathogens, but not necessarily for region-specific pathogens such as dengue and West Nile viruses. SOURCE: Chen and Wilson, 2013. between human migration and infectious disease emergence, as discussed be- low (Figure WO-8) (see the sections “Migration,” “Urbanization,” “Conflict and Complex Emergencies,” and “Road Development” on pages 38, 39, 43, and 51, respectively). A more straightforward relationship exists between the global movement of animals and animal products and the emergence of zoonoses, which are estimated to comprise 60 percent of newly identified infectious diseases (Jones et al., 2008). This topic was addressed by speaker Nina Marano, of the CDC’s Division of Global Migration and Quarantine, which regulates the importation of live animals and animal products into the United States (Dr. Marano’s contribution may be found on pages 282–296 in Appendix A). In the course of defining the scope of the CDC’s mandate in this area—summarized in Box WO-2—Marano described specific zoonotic disease risks associated with international trade that the agency attempts to mitigate through regulation.

32 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS FIGURE WO-8  They come arm in arm—American seaports must close their gates to all three. SOURCE: Judge Magazine, September 17, 1892. Introducing Plant Pathogens Global pathogen movement—enabled by international travel and trade, as well as by extreme weather—has also had a profound effect on plant health, and thereby on food security. Along with anthropogenic introduction, winds, and weather—including extreme weather events12—are associated with the intro- duction, establishment, and spread of fungal diseases (Anderson et al., 2004). Because many fungal pathogens are soil associated, wind, weather, or soil dis- turbances by humans and animals can release spore-associated dusts into the air. Once airborne, spores may be dispersed over great distances—often hundreds or thousands of miles—to new geographic areas and potential new host environ- ments (Figure WO-9) (Brown and Hovmøller, 2002). This topic was addressed by the presentation of Caitilyn Allen of the Univer- sity of Wisconsin. Interactions among novel plant, pest, and pathogen species are a frequent and influential consequence of human activities. The effects of such 12  Includesweather phenomena that are at the extremes of the historical distribution, especially severe or unseasonable weather (e.g., extreme heat or cold, tropical cyclones, tornadoes). SOURCE: http://www.ncdc.noaa.gov/climate-information/extreme-events (accessed August 6, 2014).

WORKSHOP OVERVIEW 33 introductions often interact with, and are magnified by, the effects of climate change, as shown in Figure WO-10. Pathogen introductions are a key factor in disease emergence in crop plants, which often are grown in monoculture. These circumstances—which fueled the Irish Potato Famine in the mid-nineteenth century and the 1970 epidemic of southern corn leaf blight in the United States—now threaten banana (plantain), a major food crop in East Africa, Allen noted. Irish Potato Famine One of the most tragic outcomes of a weather-induced fungal disease out- break was the Irish Potato Famine during the nineteenth century in which a sustained pattern of cool, rainy weather between 1845 and 1847 facilitated the emergence and spread of potato late blight (Phytophthora infestans) (Money, 2006). Resulting yield losses of this staple crop were catastrophic, leading to the starvation and death of over 1 million people and forcing the migration of more than 1 million more (Vurro et al., 2010). Southern Corn Leaf Blight When combined with reduced genetic diversity in agronomically important crops, weather can contribute to a “perfect storm” for a devastating agricultural disease epidemic (Rosenzweig et al., 2001). Southern corn leaf blight (SCLB), a spore-dispersed maize disease caused by the fungus Helminthosporium maydis (also known as Cochliobolus heterostrophus), swept through the United States between 1970 and 1971. Unusually warm, moist weather, coupled with a wholly susceptible host crop, provided the ideal conditions for the outbreak and spread of disease (Rosenzweig et al., 2001). Over the course of the 1970–1971 growing season, the SCLB epidemic spread from the tip of Florida to Alberta province in Canada, destroying a significant proportion of susceptible maize plants in its path (Ullstrup, 1972). Yield reductions were most severe in the Southern states with many farms experiencing total crop loss. Average yield loss in the Corn Belt states was 20 to 30 percent, with some parts of Illinois and Indiana reporting yield losses of 50 to 100 percent (Ullstrup, 1972). In the 1970 season alone, the SCLB epidemic led to the loss of 710 million bushels of corn—valued at more than $1 billion at the time [or about $5.6 billion in 2009 dollars] (Tatum, 1971; Vurro et al., 2010). Banana Xanthomonas Wilt (BXW) Sub-Saharan Africa grows about one-third of the world’s bananas, which are consumed locally and supply between one- and two-thirds of daily calories in Uganda, Rwanda, and Burundi, Allen stated. As such, they not only represent a dietary staple, but a source of cash income and a driver of local economies. In

34 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS BOX WO-2 CDC Regulatory Authority for Importation of Animals and Animal Products In her workshop presentation, Marano noted the following categories of im- ports regulated by the CDC’s Division of Global Migration and Quarantine, and the rationale behind these regulations. 1. Dogs and cats Due to the risk of importing the canine strain of rabies, which has been elimi- nated in the United States but is still endemic in many countries, a valid rabies vaccination certificate is required at a U.S. port for admission of a dog unless the owner shows that dogs have been in a rabies-free country for 6 months before arrival. The CDC publishes a list of countries each year in which no indigenous cases of the disease have been reported. Nevertheless, Marano noted, dogs imported from such “rabies-free” countries may still pose a risk, as demonstrated by the re- cent reintroduction of terrestrial rabies—via infected bats—in Greece and Taiwan. As a result, she said, the CDC proposes to change the designation of rabies-free countries to include only those that do not have bat viruses. A general certificate of health is not required for entry of pet cats into the United States, although some airlines or states may require them. However, pet cats are subject to inspection at ports of entry and may be denied entry into the United States if they have symptomatic evidence of an infectious disease that can be transmitted to humans. If a cat appears to be ill, further examination by a licensed veterinarian at the owner’s expense might be required at the port of entry. 2. Turtles, tortoises, and terrapins Due to the risk of salmonellosis for young children who may put small living or nonliving objects—including turtles—in their mouths, importation by one person of more than six turtles with shells less than 4 inches in long, or viable eggs, is not permitted except for scientific, educational, or exhibition purposes. The Food and Drug Administration (FDA) imposed restrictions on domestic tur- tle sales, but producers found ways to skirt these laws (e.g., by giving away turtles with the purchase of habitats). As a result, nearly 400 children contracted salmo- nellosis in eight multistate outbreaks, of whom about one-third were hospitalized. 3. Nonhuman primates Importers must register with the CDC and certify that the animals will be used for scientific or educational purposes, or for exhibition. The animals are isolated and quarantined for at least 31 days; disease control measures are implemented as appropriate; and any suspected zoonoses reported. These regulations were established after an outbreak of Marburg and Ebola viruses in a nonhuman primate facility in Reston, Virginia (chronicled in Richard Preston’s bestseller, The Hot Zone). Nonhuman primates share several infectious diseases and disease agents with humans, including tuberculosis, viral hemor- rhagic fever, herpes B virus, hepatitis A and B viruses, monkeypox, simian immu- nodeficiency virus (SIV), simian foamy virus (SFV), yellow fever, and meliodosis (Burkholderia pseudomallei). Improved detection and control of disease outbreaks among nonhuman pri-

WORKSHOP OVERVIEW 35 mates under the CDC’s regulations resulted in a reduction in animal mortality from an average of 15 percent to 1 percent, thereby improving the quality of medical research studies. 4. Infectious biological agents, infectious substances, and vectors A permit is required to import infectious biological agents, infectious sub- stances, or vectors, and only for scientific, educational, or exhibition purposes. The definition of vector is constantly being updated, as occurred when SARS emerged, and the difficult search for its reservoir host ultimately revealed it to be bats. The recently emerged Middle East respiratory syndrome (MERS)a has been linked with bats, as well as with camels, Marano noted. 5. African rodents and other animals that may carry the monkeypox virus Neither live nor dead rodents obtained directly or indirectly from Africa may be imported, nor may any products derived from such rodents. These restrictions followed a 2003 outbreak of monkeypox linked to the importation of infected Af- rican Gambian pouched rats, which subsequently infected prairie dogs, which in turn infected humans. At the same time, the FDA banned the sale and interstate distribution of both African rodents and prairie dogs; the interstate ban was lifted in 2008. While the CDC ban stemmed the flow of African rodents into the United States, since its imposition, imports of rodents from Europe, Canada, and South America have skyrocketed, Marano reported. “You can put your finger in the dike, but with the dynamic and fluid pet trade . . . we have to struggle to keep up with the changes,” she observed. 6. Persons, carriers, things If any arriving carrier or article or thing on board the carrier is believed to be infected or contaminated with a communicable disease, it may be subject to deten- tion, disinfection, disinfestation, fumigation, or other related measures. This regulation covers a range of potentially risky imports, including civets (which, though not the reservoir for SARS, carry a high viral load and therefore pose some risk for human infection), goat hair products from Haiti (associated with cutaneous anthrax), and bushmeat (the likely origin of the first human HIV infection, and a known source of Ebola virus, SIV, and SFV, among other infectious diseases). A pilot project under way in collaboration with the EcoHealth Alliance, the Wildlife Conservation Society, and Columbia University to screen confiscated bushmeat for zoonotic pathogens with PCR detected SFV, herpesvirus, polyoma- virus, and coronavirus samples from seized bushmeat shipments (Smith et al., 2012). a  The Middle East respiratory syndrome coronavirus (MERS-CoV) was first reported to cause human infection in September 2012, and by September 20, 2013, was reported to have caused a total of 130 cases, of which 58 (45 percent) were fatal. All cases have been directly or indirectly linked through travel to or residence in four countries: Saudi Arabia, Qatar, Jordan, and the United Arab Emirates (UAE). SOURCE: http://www.cdc.gov/mmwr/preview/ mmwrhtml/mm6238a4.htm (accessed August 6, 2014). SOURCES: Marano presentation, 2013; http://www.cdc.gov/animalimportation/cats.html; http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6238a4.htm (accessed August 6, 2014).

36 FIGURE WO-9  Selected dispersal events of fungal pathogens. Red and blue arrows indicate invasions of new territories (first year recorded in parentheses). Red arrows indicate dispersal that probably occurred by direct movements of airborne spores (I, II, II, and IV). Blue arrows indicate pathogens that were probably transported to the new territory in infected plant material or by people and spread thereafter as airborne spores (V, VI, VII, and VIII). Orange circles indicate the worldwide spread of black Sigatoka disease of banana; the first outbreak on each con- tinent is marked (X). Green arrows indicate periodic migrations of airborne spores in extinction–recolonization cycles (X, XI, XII, XIII, XIV). SOURCE: Brown and Hovmøller (2002); Background world map © C. Lukinbeal, Southern Connecticut State University, New Haven Connecticut.

WORKSHOP OVERVIEW 37 FIGURE WO-10  Global change impacts on plant health. Global change is composed of the interactions of various drivers (climate change, increased trade, land use change, pollution, urbanization). All these factors will have an impact on plant health, through direct effects on host–pathogen interactions, and via indirect effects on host migration, genetic diversity, and phenology, as well as on disease distribution, insect pests, vectors, and landscape structure. There is a feedback from plant health to global change. To be successful in the face of global change, ecosystem management will have to consider the complexity of interactions depicted in this diagram. SOURCE: Pautasso et al., 2012. 2001, banana trees in east Africa began showing signs of the emerging disease now called banana Xanthomonas wilt (BXW): yellowed dying leaves, early fruit ripening, wilted male flowers, and discolored, inedible fruits. Transmitted by pollinating insects, BXW is caused by the bacterium Xan- thomonas campestris pv. museacearum, a member of the bacterial family that produces xanthan gum, a common food additive, cosmetic stabilizer, and in- dustrial lubricant. Researchers have since learned that this pathogen jumped from ensete, a native African plant, to banana, which is native to Southeast Asia (Studholme et al., 2010). Epidemic BXW has caused the loss of more than half of all banana produc- tion capacity in the Great Lakes region of Africa, and with them, the livelihoods and food security of many residents, who must now pay approximately 40 percent more for this important foodstuff.

38 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS Migration Human migration in this age of mobility differs significantly from the histori- cal unidirectional flow of people from a place of origin to a single, final destina- tion, Cetron observed (Castles and Miller, 2009). Today, he said, migration tends to be a circulatory process: a complex journey that may be composed of many stops of varied length (and thereby, many opportunities for interchange), some of which return the migrant to his or her place of origin. Internal migration— movement within national borders—accounts for the majority of such journeys, of which an estimated 744 million take place each year, in addition to tourism. International migration is also significant, involving approximately 3 percent of the global population, who together would comprise the world’s fifth largest country, he reported. Using the United Nations’ strict definition of “migrant” as a person liv- ing outside their country of birth for at least 12 months, Cetron noted that the majority of migrants are leaving the Middle East, Eastern Europe, South Asia, East Asia, and Japan for destinations in North America, Western Europe, and the Middle East. These movements are largely driven by economic and demographic disparities, he observed—which are projected to shift somewhat in the coming decades (Munz, 2013). Population growth in sub-Saharan Africa and India is projected to exceed 200 percent in the twenty-first century and create a work- force that will dominate the structure of the international migrant populations, he reported. At the same time, the populations of Latin America, North America, the United States, and Canada—and even China—will be relatively aged, and therefore in need of the labor that African and Indian immigrants could provide. Human migration can influence infectious disease dynamics through a va- riety of mechanisms, according to speaker Chris Beyrer, of Johns Hopkins Uni- versity (Dr. Beyrer’s contribution may be found on pages 146–154 in Appendix A). People seeking transient employment, moving from rural to urban settings, or those fleeing political strife or natural disaster, may be exposed to pathogens as a result of populations mixing or gathering en masse, often while ingesting contaminated food or water, he noted. Mobile populations also frequently lack preventive measures against infectious diseases (e.g., water filters, bed nets to prevent malaria, or condoms to prevent sexually transmitted diseases) and have limited access to care facilities or health care workers, resulting in delayed or denied treatment. Beyrer offered some examples to illustrate these conditions. In South Africa, migrating miners—of which there are many, and for whom extramarital rela- tionships are common—are nearly twice as likely to acquire HIV as nonminers (Beyrer et al., 2008). In China, where the largest human migration in history has already shifted at least 120 million people from rural to urban settings, a recent ecological analysis revealed that as the proportion of immigrants rises in urban

WORKSHOP OVERVIEW 39 populations, so does the rate of sexually transmitted diseases (Tucker et al., 2005). Recent forced migrations in Zimbabwe caused thousands of people to flee to neighboring countries, where many were considered illegal immigrants; as such, they were unlikely to receive treatment for infectious diseases, or to be recognized in disease surveillance efforts, he observed. See the section “Conflict and Complex Emergencies” on page 43 for further discussion of the role of politi- cal conflict in disease emergence. Urbanization As Beyrer noted in the case of China, increasing capacity for mobility un- derlies the urbanization of human populations. Albert Ko, of Yale University, introduced his workshop presentation with an overview of this global demo- graphic transition, illustrated in Figure WO-11. As of 2007, and for the first time in history, more people live in cities than in rural regions, and by 2037, he stated, “More than 50 percent of the world’s population will be living in the urban centers of less developed countries, most of which are situated in tropical environments.” This shift has been especially dramatic in Brazil, where nearly 90 percent of people currently reside in cities, Ko reported. There, as in much of the develop- ing world, the transition from a rural agrarian society to an urban one was ac- companied by increasing social inequity, which now divides the population into widely separated groups of “haves” and “have nots.” Most of the latter groups live in slums, defined by the United Nations as housing with insecure tenure, poor structural quality, overcrowding, and inadequate access to safe water, sanitation, and infrastructure (United Nations Human Settlements Programme, 2003). The United Nations estimates that 1 billion people—nearly half the world’s urban population—live in slums. Ko observed that changing disease patterns affect residents of slums and shantytowns worldwide. Table WO-2 lists several characteristics of urbanization and urban poverty he associated with specific infectious disease risks. Leptospiro- sis, long recognized as a rural disease of particular concern for subsistence farm- ers and their livestock, has begun to emerge in both affluent and impoverished urban settings under a variety of circumstances associated with globalization, he reported. In the slums of Salvador, Brazil, a city of more than 2.5 million people on the country’s northeast coast, Ko and colleagues tracked annual epidemics of leptospirosis for more than 15 years, beginning in 1996 (Ko et al., 1999). Case study: Leptospirosis in the slums of Brazil  Leptospirosis, an infectious disease caused by bacteria of the genus Leptospira, occurs worldwide, but it is most prevalent in the tropics and subtropics. The pathogen, which comprises

40 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS FIGURE WO-11  World urban and rural poplation for developed and developing regions (percent of total). In 2007 the percentage of the planet’s population living in urban areas crossed the 50 percent threshold. Initially a developed market phenomenon, the focus of urbanization has switched to developing nations as they rapidly industrialize. After 2020 more people will also live in cities than rural areas in developing nations, and by 2037 those cities will contain half the world’s total population. SOURCE: Figure: Credit Suisse, 2012; Data: Population Division of Department of Eco- nomic and Social Affairs of the United Nations Secretariat, Credit Suisse. 9 species and >200 serovars,13 is present in water contaminated by urine from infected animals—often rats—and can be transmitted to humans through skin lesions or through mucous membranes. Leptospirosis typically is a self-limiting and often unnoticed illness, but a percentage (15 to 19 percent) of untreated cases progress to develop severe life-threatening manifestations such as pulmonary hemorrhagic syndrome and acute renal failure. In developing country settings, case fatality from leptospirosis is often greater than 10 to 15 percent. 13  Serotypeor serovar refers to distinct variations within a species of bacteria or viruses or among immune cells of different individuals. These microorganisms, viruses, or cells are classified together based on their cell surface antigens, allowing the epidemiologic classification of organisms to the subspecies level. A group of serovars with common antigens is called a serogroup.  SOURCE: http:// en.wikipedia.org/wiki/Serotype (accessed April 8, 2014).

WORKSHOP OVERVIEW 41 TABLE WO-2  Infectious Diseases Influenced by Urbanization and Urban Poverty In Poor Urban Settings… …Increases Risk For: …changing ecosystem, breakdown of control dengue in Latin America. programs… …expansion of peri-urban slums and visceral leishmaniasis. deforestation… …overcrowding and human movement… meningococcal B and C outbreaks; tuberculosis among commuters from shantytowns. …migration; increased access to diagnosis and pseudo-epidemics of leprosy in Brazil. screening… …increased yet inadequate access to health drug-resistant tuberculosis. services… SOURCE: Ko presentation, 2013. In Salvador, a city in Northeast Brazil, as well as in the large urban centers throughout the country, outbreaks of leptospirosis occur annually within the same slum communities and involve a single serovar of the pathogen, for which domestic rats serve as a reservoir (Ko et al., 1999). Rainfall is an important driver of leptospirosis in urban slums, with large outbreaks occurring during seasonal periods of heavy rainfall. Epidemics of leptospirosis are well recognized to occur after large disaster events, such as hurricanes, monsoons, and typhoons. However, Ko and colleagues found that in slum communities there was a direct association such that “for every 1 centimeter of weekly rain fall, a week or two weeks later there would be a 5 percent increase in case counts,” he explained. “So, small amounts of rainfall, and not only extreme events, contribute to risk. And there is also a 7 to 14 day lag in the effect of rainfall in cases, indicating that exposures to the pathogen among slum residents occur during or shortly after these rainfall events,” he added. Decade-long cohort studies in the crowded peri-urban slum community of Pau da Lima—where 14,000 people occupy 0.5 square kilometers—allowed Ko’s group to examine environmental determinants and outcomes of leptospiro- sis in a slum environment (Reis et al., 2008). They found that men—especially young men—had the highest attack rates for the disease, but Ko’s group has yet to determine why young men have a significantly increased (>10-fold) risk for acquiring severe life-threatening manifestations of leptospirosis acquisition than adult women and children. These investigators also observed that households at the lowest elevations in this hilly area were at greatest risk for disease, for a number of possible reasons: these were the most destitute of the slum’s residents, and their poorly drained homes tended to accumulate pathogen-laced mud when

42 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS flooded. Clearly, he observed, there is a significant social gradient of risk even within this highly impoverished community. Ko’s group is working with the Brazilian Ministry of Health to prevent dis- ease among slum dwellers through a range of interventions, including early warn- ing and response; health and education; and targeted rodent control. He noted, however, that “the majority of rodent control campaigns were actually targeting the richest neighborhoods where all the politicians were calling to get rid of the rats.” In response, he and coworkers developed maps that clearly identified the peri-urban regions at highest risk for disease. They also convinced community leaders to extend closed sewage projects beyond wealthy neighborhoods. Ko reported that after these interventions were implemented slum dwellers experi- enced a four-fold decrease in leptospirosis. Ko and colleagues are now conduct- ing observational studies to determine the significance of sewer improvements in lessening disease risk. Lessons learned  With an estimated 1 billion additional people expected to join slum populations within the next two decades, we must anticipate disease risks such as those identified in the Pau da Lima community and prepare for them, Ko insisted. The “sanitation revolution” that occurred in Victorian London, as in much of the developed world in the 1800s, led to important health improvements for the population. Although imperative, much of the developing world does not have plans in place, has not made investments or does not have the capacity to mount such a revolution to address the needs of their urban slum populations. Furthermore, the paradigm of the sanitation revolution in the 1980s may not adequately address the complexities of contemporary slum communities, he ob- served. Ko asserted that new paradigms need to be constructed which incorporate interdisciplinary approaches to these inherently complex health problems, based on an understanding and linkage of ecological and social drivers for all infectious diseases—something we have yet to achieve for marginalized urban slum popula- tions, he said (Riley et al., 2007). Ko noted the great strides that have been made in the past century toward advancing human rights and social cohesion through both policy and research. He concluded that addressing the health risks faced by the urban poor is more than just a matter of generating political will; it will require, in addition, a rec- ognition of and response to the specific role that social justice influences human health (Dye, 2008). Urbanization of animals  A less discussed, but ecologically important aspect of urbanization concerns wildlife species—such as raccoons, coyotes, and white- tailed deer—that have acclimated well to urban environments and are found there in large numbers. In making the transition from diverse ecosystems in which hu- mans play a relatively small role, to less-diverse, human-controlled environments, these species undergo a “huge shift in ecology,” noted speaker Sonia Altizer, of

WORKSHOP OVERVIEW 43 the University of Georgia (see also the sections “Climate Shifts, Animal Migra- tions, and Infectious Disease Dynamics” on page 57 and “Ecophysiology of Host–Pathogen Interactions” on page 67) (Dr. Altizer’s contributions may be found on pages 111–129 and 129–146 in Appendix A). Altizer works with colleagues at the University of Georgia who study one such animal—the white ibis—that naturally forages on aquatic animals and ver- tebrates in the Florida Everglades, but is now “starting to hang out in urban parks following people around, eating Cheetos and popcorn as handouts,” she said. This change in diet, combined with more sedentary behavior that tends to go with it, could have important effects on the ibis’ gut microbiome, she observed, and thereby, its susceptibility to infectious diseases. At the same time, these birds are exposed to Salmonella and influenza viruses through their frequent contact with peri-domestic species (S. Hernandez, unpublished). How these circumstances influence the dynamics of these diseases in wild ibis remains to be determined, she said. Conflict and Complex Emergencies Following his more general introduction to the ways in which human mobil- ity and migration may directly or indirectly influence infectious disease dynam- ics, Beyrer focused on two multifaceted phenomena in which human migration and mobility play a central role: conflict and complex humanitarian emergencies, which he defined as a humanitarian challenge, such as a natural disaster, com- bined with social conflict or political upheaval. Both circumstances are unfortu- nately common, especially in the context of environmental change, he observed. To present a typical picture of the health consequences of civil conflict, Beyrer described the 2002 political crisis in Côte d’Ivoire, which resulted in the displacement of 25 to 55 percent of that country’s adult population and 68 to 95 percent of its health care workers. Thereafter, the incidence of sexually transmit- ted infections increased dramatically—even by what were doubtless incomplete measures, given the few health care workers left to compile this assessment (Betsi et al., 2006). More recently, similar devastation has plagued Syria, where more than 6 million people had been displaced by civil war at the time of the workshop, Cetron noted. More than 2 million of these migrants had fled the country—the largest such exodus since the 1994 Rwandan genocide, according to the United Nations (BBC, 2013). The destruction of the Syrian health care system had led to increased numbers of infectious disease outbreaks in the region, he added. These are discussed in greater depth in the section, “Approaches to Identify and Address Factors Contributing to Disease Emergence" on page 76.

44 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS Natural disasters14 often represent tipping points in political crises, and frequently lead to complex humanitarian emergencies, Beyrer observed. Human rights are often at risk when natural disasters strike, he noted. A 2008 report from the Brookings Institution warned that disaster victims often face unequal access to assistance, discrimination in aid provision, enforced relocation, sexual and gender-based violence, loss of documentation, and child recruitment into fighting forces, among other challenges (Brookings-Bern Project on Internal Displace- ment, 2008). “The longer displacement lasts, the greater the risk of human rights violations,” Beyrer added. “Discrimination and violations of economic, social, and cultural rights tend to become more systematic over time.” Case study: Cyclone Nargis and its aftermath in Burma  Most, if not all, of the human rights challenges outlined above occurred in Burma after the largest storm in the country’s history, which—in 2008—killed an estimated 146,000 peo- ple, displaced 2.4 million more, and destroyed about 700,000 homes in Burma, Beyrer said. Cyclone Nargis washed over about 5,000 square kilometers, altering the geography of the Ayeyarwady Delta, and destroying 60 percent of the coun- try’s rice crop, he continued. This devastation occurred as the country’s corrupt leader, General Than Shwe, was fighting for political survival in the face of an increasingly popular op- position. “What was happening in the lead up to this storm was not preparations for the storm, but preparations for a referendum on a military-backed constitu- tion,” Beyrer recalled. The ill-prepared dictatorship was, uncharacteristically, all but absent in the storm’s aftermath—and far worse, blocked access to the country to a host of western nations ready to offer assistance. “The Burmese government was asking for direct donations, and they got some of them from their allies, but they insisted that everything go through them,” he said. Beyrer recounted his own attempts to gain admittance to the country to assist with recovery, only to be denied because he was deemed “a humanitarian doctor,” and therefore, a threat to the dictatorship. Amazingly, no major infectious disease outbreaks occurred in Burma fol- lowing Cyclone Nargis, Beyrer reported; nevertheless, the population suffered epidemic posttraumatic stress disorder, widespread depression, and impoverish- ment. The causal loop diagram depicted in Figure WO-12 illustrates how climate change, international and national governance, and conflict intersected to produce this crisis—from which Burma has yet to recover, Beyrer concluded. Stability bias  In addition to the immediate repercussions of migration, conflict, and civil disruption for human health in general and infectious disease in par- ticular, these circumstances often compromise our ability to understand, track, 14  Such as the death, devastation, social, and economic disruptions caused by Typhoon Hainan in the Philippines in November 2013.

WORKSHOP OVERVIEW 45 FIGURE WO-12 Causal loop diagram illustrating the relationship between climate change, international and national governance, and conflict in Myanmar in the aftermath of Cyclone Nargis in 2008. SOURCE: American University, 2011. respond, and mitigate infectious disease threats, Beyrer observed. He has coined a term for this dilemma: “stability bias,” which he defines as “the systematic undersampling of populations and health threats in context of conflict and insta- bility in favor of more stable settings where health research can more easily be conducted” (Beyrer et al., 2007). To illustrate this concept, Beyrer described the fate of HIV/AIDS and malaria research in the Democratic Republic of Congo (DRC), once a key location for studies of the emergence of HIV (Beyrer et al., 2007). The number of new HIV/ AIDS studies conducted in the DRC peaked between 1986 and 1988, he said, then declined rapidly and were halted by the government of Mbuto Sese Seko in 1994; much the same was true for studies of malaria. “Political conflict can really turn off our ability to understand what is happening in these contexts,” Beyrer concluded. “That is really an important thing to be mindful of when we think about these interactions.” Water Contamination Changes in temperature, humidity, precipitation, and water salinity have been shown to influence the quality of water used for drinking, recreation, and commercial uses. Temperature increases of just a few degrees can produce rapid growth in several types of bacteria that cause diarrheal diseases, including Sal- monella and Vibrio species. In addition, an increase in water temperature coupled with eutrophication has been known to promote dormant strains of cholera,

46 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS sheltered by marine phytoplankton and zooplankton, to become infectious. Out- breaks of cholera, Cryptosporidium, and Giardia have also been associated with periods of heavy rainfall and flooding. Storm water runoff from heavy precipita- tion events may also increase fecal bacterial counts in coastal waters and nutrient loading. This may, in turn, lead to increases in the range and frequency of harmful algal blooms (red tides), which pose a threat of food poisoning to humans when consumed by fish and shellfish, and when coupled with increased sea-surface temperatures, the production of potent toxins by marine microorganisms (IOM, 2009). Waterborne diseases have emerged throughout world history as a result of complex interactions between human and animal pathogens that are influenced by land and water use, and by climate, according to speaker Joan Rose, of Michi- gan State University (Dr. Rose’s contribution may be found on pages 375–389 in Appendix A). “Water quantity is well described worldwide, but water quality, which we equate to health, is not,” she observed. The importance of safe wa- ter has been expressed in the United Nations Millennium Development Goals (United Nations Millennium Development Goal 7).15 However, new Sustainable Development Goals are focusing on water quality and sustainable water security. To achieve universal access to clean water and basic sanitation, and ensure ef- ficient allocation through integrated water-resource management, various goals have been suggested: · Restrict global runoff. · Limit withdrawals from river basins. · Contribute to health targets (access, drinking water free of E. coli). As Rose noted, however, the infrastructure necessary for ensuring access to safe drinking water is lacking in much of the world. Home water treatment is in theory a viable alternative, she added, but “it’s not about whether we can get the technology into the hands of the people, it’s whether they’re going to use it.” It has recently been demonstrated that, unless compliance rates for home water treatment in a community are near 100 percent, high rates of diarrhea will persist due to the extreme contagiousness of many waterborne pathogens (Enger et al., 2013). Clearly, meeting the goal of sustainable water security will require both innovation and insight into human behavior, Rose observed. 15  The eight Millennium Development Goals (MDGs)—which range from halving extreme pov- erty rates to halting the spread of HIV/AIDS and providing universal primary education, all by the target date of 2015—form a blueprint agreed to by all the world’s countries and leading development institutions. They have galvanized unprecedented efforts to meet the needs of the world’s poorest. The United Nations is also working with governments, civil society, and other partners to build on the momentum generated by the MDGs and carry on with an ambitious post-2015 development agenda.  SOURCE: http://www.un.org/millenniumgoals/environ.shtml (accessed August 6, 2014).

WORKSHOP OVERVIEW 47 Threats to water quality in the United States  Rose remarked that before using water or developing water resources, people ask the simple yet difficult question, “How safe is the water?” While the effect of global environmental change on wa- terborne disease risks at all geographic levels remain largely to be determined, it is known that human fecal contamination and zoonotic pathogens present major threats to the biological safety of water globally, and that failing wastewater fa- cilities, combined sewer overflows,16 and agricultural runoff contribute to water contamination in both economically developing and developed countries, she stated. In the United States—the focus of Rose’s presentation—an estimated 30 to 40 percent of the millions of existing septic systems are failing, she noted. Following the introduction of water filtration, chlorination, and sewage treat- ment in the first decade of the twentieth century, death rates for typhoid fever, an important waterborne disease, dropped rapidly in the United States, Rose reported. In the succeeding decades, fewer community waterborne disease out- breaks occurred. Yet, she added, as typhoid declined, other waterborne diseases emerged—such as giardiasis, which was first reported in the 1960s. Today, an estimated 12 to 19 million cases of waterborne illness occur each year among customers of community water systems in the United States, she said. They are caused by a wide variety of pathogens, many of which are generally excreted from hosts in large numbers; resist treatment; and may persist in the environment. As a result, Rose observed, “We cannot just throw chlorine in there, we have to know how we can kill some of these more resistant pathogens.” These treatment-resistant pathogens include microbes that are highly infec- tious at low doses, and those that can be transmitted by inhalation such as the amoeba Naegleria fowleri, recently discovered in two Louisiana water systems, which causes fatal meningoencephalitis, she explained. Organisms such as tiny single-stranded cycloviruses—animal viruses found in significant numbers in sewage, and recently associated with acute central nervous system infections— may escape standard disinfection and filtration, she added (Tan et al., 2013). The emergence of these and other waterborne diseases raises an important set of intersecting questions, Rose observed, including 16  Combined sewer systems are sewers that are designed to collect rainwater runoff, domestic sew- age, and industrial wastewater in the same pipe. Most of the time, combined sewer systems transport all of their wastewater to a sewage treatment plant, where it is treated and then discharged to a water body. During periods of heavy rainfall or snowmelt, however, the wastewater volume in a combined sewer system can exceed the capacity of the sewer system or treatment plant. For this reason, com- bined sewer systems are designed to overflow occasionally and discharge excess wastewater directly to nearby streams, rivers, or other water bodies. These overflows, called combined sewer overflows, contain not only storm water, but also untreated human and industrial waste, toxic materials, and debris. They are a major water pollution concern for the approximately 772 cities in the United States that have combined sewer systems. SOURCE: http://cfpub.epa.gov/npdes/home.cfm?program_id=5 (accessed August 6, 2014).

48 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS · How and why is water quality changing? · What are the sources of emerging waterborne pathogens, and how does their emergence relate to ecosystem health? · What measures must be taken to restore and protect water systems over the long term? To examine these issues, Rose and coworkers use two model frameworks: pathogen-specific quantitative microbial risk assessments (QMRAs), to evaluate water management strategies (Coulliette et al., 2012; Enger et al., 2013), and an integrated systems model (see Figure WO-13) to inform more general decision making. FIGURE WO-13  Coupled human and natural systems (CHANS) framework. The socio- economic system is composed of four interrelated subsystems: human activities, stressors from socioeconomic activities, human well-being, and environmental policies. The lake system is described through two subsystems: water quality indicators and the ecosystem responses. SOURCE: Adapted from Mavrommati et al., 2013.

WORKSHOP OVERVIEW 49 Linking water quality to health  Using historical data, Rose and coworkers are attempting to examine interactions between ecological and socioeconomic sys- tems as they affect water quality—and thereby, human health and well-being—in the Great Lakes region. The earliest source was a 1913 bacteriological study of samples taken from 1,000 locations that identified sewage discharges as a signifi- cant source of pollution, and which recommended both wastewater and drinking water treatment for the region. Focusing on the small but important watershed of Lake St. Clair, which con- nects the upper and lower Great Lakes and provides drinking water for 4.5 million people, these investigators compared changes in precipitation, lake levels, land use, human population, and household income with trends in water quality over the past century. Wastewater treatment did not significantly eliminate waterborne disease in the watershed until after 1940, Rose stated, and water quality remained inconsistent until reforms mandated by the Clean Water Act were implemented in the 1980s. The resulting improvement in water quality soon reversed, and has since deteriorated to pre-1950s levels, she reported. To extend their historical study, the researchers obtained cores of lake sedi- ments in two locations—Anchor Bay, which is relatively undeveloped, and the heavily urbanized Clinton River—and are examining them for fecal pollution indicators, antibiotic-resistant microbes, and several markers of eutrophication. 17 Fecal pollution indicators for Anchor Bay increased steeply between 1875 and 1900, when there was heavy logging activity in that area, but remained stable thereafter; by contrast, Clinton River did not reach similar levels of pollution until after 1950, but a steep rise since the 1980s nearly doubled that level. Rose suspects that combined sewer overflows—from sewers installed in increasing numbers between the 1950s and 1980s—played a key role in raising fecal pollu- tion rates in Clinton River. With the data they have gathered so far, Rose and coworkers developed a complex causal loop diagram—a more detailed and specific version of the integrated systems model shown in Figure WO-14—to illustrate the interacting forces contributing to fecal pollution in Lake St. Clair. It can be reduced to two main pathways, she explained: one derived from human water use and waste production; the other, from nonpoint sources (e.g., agricultural runoff) related to land use. The model also incorporates socioeco- nomic variables such as human well-being and income in order to inform deci- sions on land use and infrastructure. For example, she said, “What if climate gets wetter? We may show that the intensity of the storms is playing a major role . . . as opposed to the water infrastructure. Where are we going to put our dollars?” 17  Eutrophication is an excessive richness of nutrients in a lake or other body of water, frequently due to runoff from the land, which causes a dense growth of plant life and death of animal life from lack of oxygen. SOURCE: http://www.oxforddictionaries.com/us/definition/american_english/ eutrophication (accessed August 6, 2014).

50 FIGURE WO-14  Causal loop diagram representing two pathways. Pathway 1 is human use of water (residential water demand) and, conse- quently, the production of pollutant loads; Pathway 2 is nonpoint source pollution related to land use (tourism). SOURCE: Mavrommati et al., 2013.

WORKSHOP OVERVIEW 51 Road Development Both ecological and social drivers of infectious disease transmission are in- fluenced by road development, observed speaker Joseph Eisenberg, of the Univer- sity of Michigan (Dr. Eisenberg’s contributions may be found on pages 213–229, 230–250, and 251–266 in Appendix A). The introduction of primary roads into remote areas inevitably alters both human interactions and ecosystems and, by facilitating movement, changes population structure, he said (see Figure WO-15). These effects are magnified by the subsequent construction of secondary roads in a process that tends to unfold over the course of decades, as increasing numbers of villages within the region gain access and exposure to roadways, he added. To learn how road development interacts with the dynamics of diarrheal disease and with the transmission of antibiotic-resistant enteric pathogens, Eisenberg and coworkers conducted epidemiological studies in an area of coastal Ecuador at the time that its first roads were being constructed (Eisenberg et al., 2006, 2011). Roads as catalysts for diarrheal disease dispersal While past studies have focused on the influence of road construction on the emergence and spread of sexually transmitted diseases, which are largely shaped by social processes, and VBDs, which are driven by ecological factors, Eisenberg’s research has focused on the complex transmission patterns of enteric pathogens, which are affected by both social and ecological forces. An estimated 1 billion people lack access to safe water and adequate sanitation, placing them at high risk for diarrheal dis- eases, Eisenberg observed. While mortality from these diseases has been declin- ing worldwide, he said, the risk for disease remains—and threatens to increase with the spread of antibiotic-resistant pathogens. This is the case in their study region in northern Ecuador, where roads have led to the rapid development of new settlements with substandard water and sanitation. “Enteric pathogens can survive in the environment in water, food, and differ- ent media, and also have the propensity to transmit person to person,” Eisenberg FIGURE WO-15  Causal diagram linking proximity of the road to increases in infection and diarrheal disease. SOURCE: Eisenberg et al., 2006.

52 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS pointed out. A wide range of organisms can cause diarrheal diseases, and a given enteric pathogen may exploit a variety of transmission pathways. Consequently, as he and coauthors have noted, diarrheal diseases are influenced by many inter- acting and interdependent risk factors (Eisenberg et al., 2012). To explore how social and environmental changes associated with road construction influence the epidemiology of diarrheal diseases, Eisenberg and coworkers compared enteric pathogen infection rates from case-control studies conducted in 21 villages across their study region in northern Ecuador with in- fection rates in the regions’ major population center, Borbón, as well as between those communities that reside close to the road and those that reside far from the road (Eisenberg et al., 2006). “Prior to the road, these people had lived quite independently, surviving on different kinds of industry that the river transport system supported,” he noted. This changed, however, when interest arose in ex- tracting hardwood and other resources from the area, and a road was constructed connecting Borbón to the coast and also up in to the Andes. In the ensuing 10–15 years, he said, secondary roads began to link formerly remote villages to Borbón. The researchers selected the villages they surveyed to provide a cross-section of remoteness from Borbón, their closest access to the primary road (Eisenberg et al., 2006). They visited each village three times over the course of 2 years, during which they tested each person who reported diarrhea for pathogenic E. coli, rotavirus, and Giardia, and collected information about his or her social network. Using two different definitions of remoteness (distance and cost/time), they found that diarrheal disease in general, as well as for infection by all three pathogens, was more prevalent in villages closer to Borbón. This trend was weak- est for Giardia and strongest for E. coli, which one might expect based on the contrasting transmission dynamics of the two pathogens, Eisenberg noted; Giar- dia is shed over a longer period by its hosts, persists longer in the environment, and has a lower infectious dose than E. coli. As a result, he explained, “E. coli is going to be cut out, the transmission potential is going to be cut off much more easily in a village that has better sanitation and hygiene than Giardia.” Giardia is therefore better able than pathogenic E. coli to maintain transmission within the more sanitary environment of remote villages, the researchers concluded (Eisenberg et al., 2006). Pathways to antibiotic resistance  The evolution and spread of antibiotic resis- tance occurs over multiple scales, Eisenberg noted. These range from individual usage, to community-level and regional spread of antibiotics and resistant patho- gens through water systems and the soil, as well as through host migration. To examine how each of these processes may be influenced by road construction, he and coworkers analyzed data on antibiotic use and the prevalence of antibiotic- resistant E. coli collected as part of the previously described surveys of diarrheal disease in northern Ecuador (Eisenberg et al., 2006, 2011). As with the risk for diarrheal disease in general, they found villages that were close to the road were

WORKSHOP OVERVIEW 53 more likely to have a significantly higher prevalence of antibiotic resistance as compared with more remote villages. Interestingly, the researchers were then able to determine that this pattern could not have been caused by differences in antibiotic use between the close and remote villages (e.g., an individual-level influence on antibiotic resistance). Rather, a more complex explanation emerged: under conditions of low antibiotic usage, differences in transmission rates across villages have very little effect on relative risk for antibiotic resistance, Eisenberg stated. Instead, he said, antibiotic resistance is driven by the higher rate of introduction of resistant bacteria in the close communities than in the far communities as a result of increased migration and movement into close villages; conversely, when antibiotic use is high, resis- tance is driven by transmission rates. The varying levels of antibiotic resistance across their study area appeared to conform to this model of community-level influence (Eisenberg et al., 2011). Eisenberg and coworkers then considered how the various effects of road proximity on disease could explain community-level influences on disease preva- lence. This same approach might be applied as a model system to predict the transmission dynamics of antibiotic resistance. First, increased contact with the outside world in villages close to the road could increase the rate of introduc- tion of antibiotic-resistant pathogens, Eisenberg noted. Second, their research suggested that close villages were less socially cohesive—that is, they had less dense social networks—than those far from the road; they further associated de- creased social cohesion in the close villages with reduced sanitation and hygiene. Thus despite the fact that physical closeness among remote village residents (as measured by distance between households) raised the risk of disease, this effect appeared to be overcome by the concomitant advantages of social closeness, as illustrated in Figure WO-16. These studies taken together suggest that road construction leads to social and ecological changes that influence disease dynamics across a region. These direct and indirect effects of disease dynamics are mediated through local social structures. Eisenberg noted that his team has also determined that climate and hydrological processes related to primary and secondary road construction influ- ence the spread of antibiotic resistance in their study region and influence the patterns of diarrheal disease in their study region, as illustrated in Figure WO-17.) Climate Change As Patz previously observed (see the section “Health Impacts of Environ- mental Change” on page 13), the major features of climate change—warming and hydrologic extremes—can profoundly influence infectious disease dynamics. Yet, it is difficult to identify specific effects of climate change on infectious disease risk among human, animal, and plant communities, and to define how climate change interacts with other drivers of global environmental change (e.g., land

54 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS FIGURE WO-16  Postulated conceptual model: Effects of social relationships on disease outcomes, Esmeraldas, Ecuador, 2007. Risks and protective effects are mediated through a number of social processes. NOTE: OR = odds ratio. SOURCE: Adapted from Zelner et al., 2012. use) to alter the spread and transmission of infectious diseases. Several speak- ers described their efforts to undertake this challenge through their studies of a variety of species and ecosystems. Changing disease patterns in the Arctic  “The Arctic is unique in many re- spects,” observed speaker Alan Parkinson, of the CDC (Dr. Parkinson’s contribu- tion may be found on pages 310–327 in Appendix A). While definitions of “the Arctic” vary, Parkinson noted, he includes all of Alaska and northern Canada to about 60 degrees north in this region, together with parts of northern Quebec, Labrador, Greenland, the Faroe Islands, Iceland, Norway, Finland, Sweden, and the northern regions of the Russian Federation. This area covers approximately one-eighth of the Earth’s surface, but its residents number only about 4 million, of whom about half live in the Russian Federation, and 10 percent are of indigenous ancestry, he stated. Investigations in the Arctic provide an opportunity to study climate-sensitive infectious diseases in isolated human and wildlife populations, according to Parkinson. The warming trend that has occurred worldwide over the twentieth century has been amplified in the Arctic by more than three-fold over the past half century, he said. As a result, the average extent of sea ice has diminished by as much as 20 percent—a decline that has quickened as the growing area of open

WORKSHOP OVERVIEW 55 FIGURE WO-17  An ecological perspective. 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). The presence of a road or roads causes environmental changes (social and ecological). These changes occur differentially across the landscape of villages, affecting social structure (spread of microorganisms differentially through water sanitation and hygiene pathways); movement and migration patterns at multiple scales; and climate and hydrological processes. Regional patterns of environmental change will vary over time. SOURCE: Eisenberg et al., 2011.

56 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS ocean surface warms. “This will accelerate further and will probably result in a total loss of sea ice in the summer projected for later this century,” he reported. Warming is also extending vegetation zones and animal ranges northward, in- creasing the number of species—including those of their associated parasites—in the Arctic, he observed (Revich et al., 2012). At the same time, marine species that are dependent on sea ice are declining in numbers, threatening the already vulnerable indigenous populations that depend on marine animals for food. Indigenous peoples of the Arctic have significant health disparities compared with nonindigenous populations, Parkinson stated. Many of their communities lack adequate water and sanitation infrastructure. In Alaska, 22 percent of rural homes lack in-home water and sewage service, limiting residential use of water for hygiene to as little as 1.6 gallons per person per day, raising the risk of “water- washed” diseases such as skin and eye infections, he reported. A study conducted in rural Alaskan villages found that higher respiratory and skin infection rates were associated with a lack of in-home water service (Hennessy et al., 2008). In villages like these, sewage is often disposed of in a pit lagoon that is vulner- able to flooding—that is occurring with increasing frequency in recent years, he added. “With the thawing of the permafrost we have flooding, shoreline erosion, storm surges, [and] loss of protective sea ice,” he said. “Many communities are facing relocation because village housing, water, sanitation, food storage, [and] structures are being undermined.” Parkinson also noted that many residents of the Arctic depend on hunting for their food supply—preserving meat by drying, smoking, fermentation, and freezing (where permafrost exists)—all processes that are vulnerable to the ef- fects of climate change and are associated with the risk for zoonotic diseases such as trichinellosis, toxoplasmosis, and brucellosis (Hueffer et al., 2013). He observed that large and increasingly frequent (and formerly rare) walrus die-offs have occurred in northwest Alaska coincident with sea ice loss, and warned that animals that scavenge the carcasses could transmit Trichinella. Climate change may also influence the density and distribution of animal hosts of arthropod vectors, resulting in an increase in human illness or a shift in geographical range of disease, Parkinson stated. Milder winters and earlier spring onset have been associated with a shift in the range of tick-borne encephalitis in northwestern Russia (Tokarevich et al., 2011). After a period of unusually high temperatures in 2006, Northern Sweden experienced an outbreak of hemorrhagic fever caused by Puumala virus when large numbers of voles, which carry the virus, sheltered in homes due to a lack of snow cover (Pettersson et al., 2008). “I think we can learn a lot from outbreaks about climate change and infec- tious disease emergence,” Parkinson concluded. He urged improvements in both surveillance and disease diagnostics (for animals as well as humans) to advance this goal, which is especially important to pursue given the recent openings of the Northwest and Northeast Passages to commercial shipping. Regional public health institutes and laboratories collaborating in a program called International

WORKSHOP OVERVIEW 57 Circumpolar Surveillance (Parkinson et al., 2008) have begun to meet this chal- lenge by monitoring certain infectious diseases and their relationship to climate change, and are bringing the One Health paradigm to the Arctic, he reported. Climate shifts, animal migrations, and infectious disease dynamics  While the influence of climate change on pathogen dynamics is readily apparent in Arctic wildlife and in some marine ecosystems, detecting such effects in human popula- tions inhabiting temperate regions has proven difficult, noted Altizer. “The wealth of nations; the better infrastructure; and the better health care, surveillance, and control aimed at human pathogens in developed countries, might actually be masking the climate signals,” she explained. Accordingly, the effects of climate change on disease dynamics are more apparent among animals in the wild, Altizer observed (Altizer et al., 2013). For example, she said, in the Arctic, the parasitic worms of musk oxen are develop- ing faster in the tundra due to warmer summers and longer growing seasons (Kutz et al., 2013). More prevalent lungworm infections are reducing musk ox populations, and thereby, food security for residents who rely on this animal for food. At the same time, coral reefs in the Caribbean have become more disease susceptible due to outbreaks associated with warmer water temperatures (Harvell et al., 2009). In addition to affecting pathogens and vector behavior, climate change indi- rectly influences disease dynamics by altering animal migration patterns, Altizer stated (Altizer et al., 2011, 2013). Migration can lower disease risk in some ani- mal populations, she explained, through several mechanisms: · by allowing animals to periodically escape habitats where parasitic in- fectious stages have accumulated (thereby starving parasites left behind before the migrants return); · by imposing stress during strenuous journeys that serves to cull infected— and therefore weakened—animals from the populations; and, in some cases · by physically separating more vulnerable juveniles from infected adults (Altizer et al., 2011). However, she noted, animal migrations have been altered, and in some cases have disappeared altogether, as a result of human activities including habitat destruc- tion and climate change (Wilcove and Wikelski, 2008). Using the monarch butterfly and its protozoan parasite, Ophryocystis elektro- scirrha, as a model system, Altizer and coworkers have explored the connections between animal migration and infectious disease risk (see figure in Box WO-3). They determined that monarchs in eastern and western North America, which migrate long distances to wintering grounds in Mexico, suffer lower rates of in- fection than nonmigratory monarch populations that breed year-round in southern

58 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS BOX WO-3 Lessons from a Model System: Monarch Migration Drives Large-Scale Variation in Parasite Prevalence During the past 10 years, Altizer and colleagues 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 that breed year-round in southern Florida (C), Hawai’i, the Caribbean Islands, and Central and South America (Ackery and Vane-Wright, Florida and other locations. Importantly, year-round breeding in monarchs is enabled by mild winters, as well as by the availability of exotic food sources. Sedentary (nonmigratory, winter breeding) monarchs have recently become more common in North America along the south Atlantic and Gulf coasts, and Altizer noted that monarchs at these locations face greater infection risk than their mi- gratory counterparts. In addition to these effects on disease dynamics, migration can also affect the evolution of pathogen virulence and host resistance in this system, Altizer noted,

WORKSHOP OVERVIEW 59 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 migrations 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 demonstrated that monarchs infected with O. elektroscirrha flew shorter distances and with reduced flight speeds, and field studies showed parasite prevalence decreased as monarchs moved southward during their fall migrations (Bartel et al., 2010; Bradley and Altizer, 2005), consistent with the idea of migratory culling. Parasite prevalence was also highest among butterflies sampled at the end of the breeding season than for those that reached their over- wintering sites in Mexico (bottom-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 (Altizer, 2001; de Roode and Altizer, 2010), suggesting that longer migration distances cull monarchs carry- ing 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. SOURCES: Images reproduced from Altizer et al., 2000, 2011; Bartel et al., 2010. Text re- produced from Altizer, 2011. because the most virulent strains of O. elektroscirrha are found among nonmi- grating monarch populations, and not among those that migrate long distances (de Roode and Altizer, 2009). “Migration is essentially a sieve that’s removing infected animals, especially those that harbor the most virulent pathogen strains, from the population,” Altizer concluded. Because many animal migrations are likely to be compromised by climate change and other human activities, she urged greater effort to understand the effects of these losses on infectious disease dynamics.

60 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS FIGURE WO-18  Major taxonomic groups of pathogens causing plant emerging infec- tious diseases: (a) viruses, fungi, and bacteria cause the most emerging infectious diseases in plants; (b) introduction of pathogens cause the most plant emerging infectious diseases. SOURCE: Anderson et al., 2004. Climate effects on plant pathogens  As the ultimate source of all food, plants determine the health of all living things; thus the knock-on effects of anthropo- genic changes that increase risk for plant diseases are potentially widespread and profound (Flood, 2010; Strange and Scott, 2005; Wheeler and von Braun, 2013). An estimated 16 percent of all crops are lost to disease each year, according to speaker Caitilyn Allen, of the University of Wisconsin; however, if a particular pathogen encounters optimal weather conditions (typically higher temperatures and rainfall), or naïve or abundant host plants, the result may be devastating (Oerke, 2006). A “disease triangle,” composed of favorable environment, suscep- tible host plant, and virulent pathogen, underlies such epidemics, she explained. Human activities influence all of these factors, she added. In particular, anthropo- genic factors have contributed to the global warming of the Earth that, in turn, has increased the frequency of flooding rains. Allen went on to observe that humans also move pathogens around the world through trade and travel, and, vast areas of crops are grown as monocultures.18 As Figure WO-18 illustrates, infectious disease emergence in plants is strongly influenced by weather, climate change, and pathogen introductions to new host plants, as discussed in Figure WO-19. Concerning the effects of climate change on plant pathogens, speaker Marco Pautasso, from the Centre d’Ecologie Fonctionnelle et Evolutive of France’s Centre National de la Re- cherche Scientifique,19 has written that plant health is predicted to suffer under climate change due to mechanisms that range from climate- and weather-induced stress to increased pathogen virulence and transmission rates (Dr. Pautasso’s contribution may be found on pages 359–374 in Appendix A) (Pautasso et al., 18  The cultivation or growth of a single crop or organism especially on agricultural or forest land. 19  Dr. Pautasso is now with the European Food Safety Authority.

WORKSHOP OVERVIEW 61 FIGURE WO-19  Coffee rust and climate change. (A) Defoliation in a coffee plantation, Coimbra, Minas Gerais, Brazil; (B) Leaf symptoms on abaxial surface (bar = 0.5 cm); (C) Detail of suprastomatal uredinial pustules coalescing over lower leaf surface (bar = 0.5 cm); (D) Uredinium showing arrangement of spores (bar = 20 µm); (E) Urediniospores— showing the thickened, heavily-ornamented or verrucose upper wall containing carotenoid lipid guttules imparting the yellow-orange color (bar = 10 µm). SOURCE: Carvalho et al., 2011. 2012). As Allen pointed out, however, climate change has yet to be identified as the sole cause of any plant disease outbreak (Bebber et al., 2013). Allen noted that the emergence of coffee rust in Latin America—described in Figure WO- 19—offers an opportunity to characterize the influence of climate change on a plant disease epidemic. Next to oil, coffee—with an annual crop worth some $80 billion—is the most valuable commodity traded by developing countries. Coffee is grown in more than 50 tropical countries, mainly in large plantations. An understory tree, coffee thrives in shade, but lower-quality sun-tolerant varieties predominate the market,

62 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS because they are more profitable. Coffee is native to Ethiopia, but for centuries has been planted and consumed throughout the world; coffee rust (Hemileia vastatrix), a wind-blown fungal infection, has followed every move. Coffee rust (Figure WO-19) is an obligate parasite that causes rapid defoliation and eventu- ally kills its host. Its spores are short-lived, but given access to the large-scale monocultures typical of most coffee plantations, can readily produce an epidemic. Quarantines kept coffee rust out of Latin America until 1970. The use of fungicides kept this fungal pathogen in check until a major outbreak occurred in Colombia in 2008. This epidemic was preceded, and possibly triggered, by 2 years of atypically warm, wet weather. In the intervening years more outbreaks have followed in Latin America, some of them in locations where coffee rust had never been detected before. Now, according to Allen, “We are in the middle of a major rust epidemic on coffee [plantations] in all of Latin America,” with declines in yield of approximately 20 percent resulting in losses of over half a billion dol- lars to date. Unfortunately, she added, because the pathogen overwinters in dead leaves, even larger losses are expected next year. This disaster begs the question: if coffee rust emerged in Latin America in 1970, and the pathogen is no more virulent (as researchers have determined), why did it take decades to spark epidemic disease? Weather seems to have been the culprit, Allen asserted, and it drove the disease to higher elevations than it had previously reached. As one researcher observed, “Rust was the explosive, but climate change was the detonator” of this epidemic. It will be years before we can say whether this is the case, Allen remarked. She added, however, that “this is a place where it is worth looking if we are trying to find climate change footprints in plant disease development.” Models also provide a way to examine the consequences of climate change for plant disease (see the section “Characterizing the Effects of Environmental Change on Infectious Disease Dynamics” on page 63 for further discussion of ecological models of infectious diseases). Pautasso offered several examples of such models, pointing out their strengths and weaknesses. Most climate change models fail to incorporate the role of plants in carbon release, and thereby, a crucial feedback cascade that could be triggered by warming temperatures or extreme weather events, he observed. Uncertainty regarding the effects of cli- mate change on precipitation levels in specific locations diminishes the predic- tive capacity of some models, he added. “When we develop scenarios for plant diseases with climate change, it is important to know whether we will just have increasing temperature and decreasing precipitation, or whether both temperature and precipitation will increase,” he said, because most plant species and many pathogens will respond differently to these contrasting regimes. Models predict- ing future plant diseases also should take into account efforts to reduce carbon emissions—and thereby, the ongoing effects of climate change—through the use of plant-based fuels produced in large-scale monocultures.

WORKSHOP OVERVIEW 63 Another potential unintended consequence of attempts to mitigate the effects of climate change could result from so-called assisted migration: human-mediated movement of plant species threatened by warming temperatures poleward, or to higher elevations, in order to protect them from extinction (McLachlan et al., 2007; Mueller and Hellmann, 2008; Vitt et al., 2010). These efforts seldom take into account the concomitant risk for pathogen introductions to novel environ- ments, Pautasso noted. Such risks need to be examined through both empirical approaches and modeling studies—both of which are relatively scarce in com- parison to the numerous review articles on plant disease and climate change, he observed (Pautasso et al., 2012). Characterizing the Effects of Environmental Change on Infectious Disease Dynamics The previously described case studies illustrate the daunting challenges involved in measuring the effect of individual anthropogenic factors on disease dynamics, host–microbe interactions, and in understanding the interplay of mul- tiple factors that influence these relationships. These direct and indirect forces are not only challenging to disentangle, but they are highly localized in their individual and collective effects. These conditions must be taken into account in efforts to explain—let alone predict—the overall impact of environmental change on disease transmission patterns, Relman observed. Conceptual frameworks, models, and maps provide ways to organize the wealth of information required to characterize the complex ecological relation- ships that shape the dynamics of infectious diseases across a range of spatial and temporal scales. Several speakers described the design, refinement, and applica- tion of such approaches to analyze existing information, to identify knowledge gaps and research goals, and as a foundation for prediction. Frameworks and Models: Epidemics as Networks As illustrated in work presented by Dobson on food webs, and by Eisenberg on social relationships within villages, networks provide a means to portray in- terconnections between components of a system. Epidemiologists use networks to illustrate the spread of human and animal pathogens, but according to Pautasso (2013), plant pathologists have made limited use of this tool. In his presentation, however, Pautasso described the application of network epidemiology to address emergent tree pathogens (see next section). He also noted that in addition to ana- lyzing the spread of disease through large populations, networks could be used to examine heterogeneity in contacts among members of subgroups (e.g., within social or ecological communities such as schools, workplaces, farms, or plant nurseries), which has been demonstrated to shape the course of epidemics, and could therefore inform their control (Jeger et al., 2007; Pautasso and Jeger, 2014).

64 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS Exotic Tree Pathogens: Assessing Impact and Options for Response Tree diseases, along with other plant diseases caused by exotic pathogens, have increased in number and severity over the course of the Great Accelera- tion, and in particular, with the expansion of global trade (Pautasso et al., 2012; Santini et al., 2013). Spatial models depicting the presence and absence of emerg- ing diseases among susceptible host species can inform strategic responses to these threats, Pautasso observed; he described two such efforts, directed against Phytophthora ramorum (the cause of Sudden Oak Death in North America, and Sudden Larch Death in the United Kingdom), and Hymenoscyphus pseudoalbidus (the cause of ash dieback in Europe; Queloz et al., 2011). Since it was first described in 2001 (Werres et al., 2001), P. ramorum, a generalist oomycete, has infected and killed a wide range of both wild and or- namental host plants in North America and Europe, including oak, camellia, and rhododendron (Pautasso, 2013). Molecular evidence suggests that this pathogen was spread efficiently through global plant trade networks. Before 2009, the disease affected relatively few trees in Britain; since then, its spread to Japanese larch, which is widely planted, has affected thousands of hectares of tree planta- tions, Pautasso said. To assess the potential impact of this pathogen in the United Kingdom, researchers built a spacially explicit simulation model of epidemic development that incorporates data on the distribution of susceptible host plants as well as plant trade networks (see Figure WO-20). Investigating pathogen invasion routes and identifying environmental vari- ables associated with disease severity can help set priorities for monitoring and predict the likely further development of the epidemic (Pautasso, 2013). A further development of this model has accurately predicted increased risk for disease in southern and western Scotland, Pautasso reported. As Pautasso observed, Ash dieback likely spread, via the plant trade, to Po- land from East Asia in the early 1990s, and thence throughout Europe; it was first reported in the United Kingdom in 2012 (Gross et al., 2014). The highly lethal fungal disease threatens the existence of the common ash (Fraxinus excelsior), a keystone tree species throughout temperate Europe, and its associated biodiver- sity; it could potentially affect several Fraxinus species in North America as well (Pautasso et al., 2013).20 Determining the origin of the pathogen is important in order to prevent further introduction of the pathogen to new environments and 20  Asian Fraxinus spp. appear to be resistant to the pathogen, as they are likely to have coevolved with it.

FIGURE WO-20  Example of the spatially explicit simulation model of P. ramorum dispersal in England and Wales developed by Tom Harwood (Harwood et al., 2009). The model integrates data on the distribution of main susceptible hosts and a realistic reconstruction of the plant trade involving ornamental plants susceptible to P. ramorum. 65 SOURCE: Image courtesy of Tom Harwood, CSIRO, Australia.

66 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS FIGURE WO-21  Four basic scenarios for the further development of ash dieback in Eu- rope, based on levels of pathogen dispersal and host susceptibility. If both are consistently high, host disappearance may take place. SOURCE: Pautasso et al., 2013. of new strains of the pathogen worldwide, as well as to inform the search for biological controls and candidate species and cultivars for resistance breeding. 21 Because of its recent emergence—and in contrast to P. ramorum—relatively little is known about environmental factors that might influence ash dieback severity following infection. What is known is that the pathogen is dispersed through wind-blown spores and also on infected ash saplings, which can be asymptomatic. Pautasso and coauthors (2013) propose four basic scenarios for the further development of ash dieback in Europe, based on future levels of pathogen dispersal and host susceptibility, as shown in Figure WO-21. Luckily, some resistant ash trees have been observed in Denmark, Lithu- ania, and Sweden (McKinney et al., 2014). If pathogen dispersal is limited, high host susceptibility may matter less, but this scenario is made less realistic by long-distance dispersal due to trade in infected ash saplings, such as to the 21  As Allen noted in discussion, breeding for disease tolerance or resistance has long been con- sidered the best way to manage plant disease threats. “If you have good disease resistance in your crops—not an option available to human doctors—then you don’t have to worry about the disease,” she observed. “You don’t have to spray. You can grow with impunity the crop of interest—until the pathogen evolves . . . the ability to overcome that resistance, which happens all the time, unfortu- nately.” This approach has been used with mixed success to save disease-threatened tree species such as the American chestnut (Castanea dentata) against chestnut blight and elm trees (Ulmus spp.) against Dutch elm disease (Pautasso et al., 2013).

WORKSHOP OVERVIEW 67 United Kingdom and Ireland. Should common ash susceptibility be limited in some regions (e.g., those with hot and dry summers typical of the Mediterranean climate), the impact of the dieback would be less devastating. Each scenario can be considered for various regions and countries, as well as for the whole distribu- tion of common ash. Further dimensions to be considered are the conduciveness of the environment to disease, other pests such as emerald ash borer (which has been reported to be spreading westward from the Moscow region of Russia), as well as human actions to prevent further worsening of the dieback. Ecophysiology of host–pathogen interactions  In a recent review article, Altizer and coauthors (2013) pointed out that integrating knowledge from ecophysiologi- cal responses of organisms to temperature variation with modeling approaches is needed to better predict how different host–pathogen relationships will respond to climate warming. This approach combines established relations between metabo- lism, ambient temperature, and body size, with epidemiological modeling to pre- dict how general classes of pathogens (e.g., directly transmitted or vector-borne) and hosts (ectotherms or endotherms) will change with increasing temperatures (see Figure WO-22). “Building from this foundation, the next step is to extend such general mod- els to specific pathogens of concern for human health, food supply, or wildlife conservation, which will require empirical parameterization, with attention to the on-the-ground conditions,” the authors wrote. “Modeling efforts should be inte- grated with experiments to test model predictions under realistic conditions, and with retrospective studies to detect the ‘fingerprint’ of climate-induced changes in infection.” Because detecting signals of climate change in many human dis- eases remains problematic, the authors emphasize the importance of long-term ecological studies to examine past distributions of pathogens, important hosts, and disease severity. Case study: Weather-based risk for coccidioidomycosis (valley fever)  Coc- cidioidomycosis, also known as valley fever, is a lung infection of humans or animals by the fungal pathogens Coccidioides immitis and C. posadasii (Nguyen et al., 2013). These fungi occur in desert soils in the Western Hemisphere. In the United States, two-thirds of all cases occur in the “valley fever corridor,” that includes Phoenix and Tucson, Arizona, and areas along the 150-mile stretch of highway that connects the two cities, according to speaker John Galgiani, of the University of Arizona (Dr. Galgiani’s contribution may be found on pages 266– 282 in Appendix A). While most people who inhale these fungal spores suffer a mild illness and develop resistance to reinfection, a few—perhaps 500 per year in Arizona—manifest life-threatening pneumonia or serious, potentially chronic, infections outside the lungs, resulting in about 160 deaths per year. Among di- agnosed and reported cases of coccidioidomycosis in Arizona, an estimated 75 percent of people lost at least 2 weeks of work and 40 percent were hospitalized,

68 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS FIGURE WO-22  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 warm- ing. 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 exceed 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 acclima- tion. (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) relative to baseline (blue). For example, a shift from 2- to 1-year cycles of transmission has occurred for the muskox lungworm (Kutz et al., 2009). This outcome could generate sporadic dis- ease 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. SOURCE: Altizer et al., 2013.

WORKSHOP OVERVIEW 69 he reported. “If you add in economic impact from lost work, all of the outpatient management of this disease, it is easily probably a couple of hundred million dollars a year for Arizona,” he said. Colleagues of Dr. Galgiani were prompted to create models to describe the relationship between seasonal precipitation and the incidence of coccidioido- mycosis after hypothesizing that an annual increase in cases after an intense dust storm struck Phoenix in 2011 was not a consequence of the storm itself, but of weather patterns that raised the risk for both dust storms and disease, he said. Tamerius and Comrie (2011) had created a time series of predicted exposure to Coccidioides spores based on laboratory-confirmed cases in two Arizona counties over a 12-year period. Their analysis suggested that spores released during the late summer and fall persist in the environment and remain infectious for several months, and that the size of the spore “bloom” influences human exposure in the winter and spring. They also determined that exposures end abruptly in mid-summer, coincident with the local rainy season, which may suppress aerosolization of the spores. Their model-predicted cycle of “grow and blow,” in which precipitation first raises spore levels, then restricts spore distribution—conformed to local observations, as shown in Figure WO-23. FIGURE WO-23  Modeled versus observed August–March (1995–2013) cocci exposure in Maricopa County, Arizona.   * From Tamerius and Comrie, 2011. SOURCE: Galgiani presentation, 2013.

70 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS To refine this model, Galgiani’s colleagues hope to sample spore levels in the air and soil and link those measurements to precipitation and exposure levels. Be- cause spore distribution is suspected to be patchy, locating soils with high spore levels and protecting them from disturbance could reduce the risk of exposure, he noted. Environmental mechanism(s) linking precipitation and spore expo- sure levels remain to be determined, but they hint at the possibility that rodents serve as a reservoir for this disease—and therefore merit exploration, he stated. “Similar to the trophic cascade hypothesis associated with variable outbreaks of plague and hantavirus, high precipitation during the preceding winter may result in an increase in rodent populations,” Tamerius and Comrie (2011) suggest. “This mechanism potentially increases the density of rodent carcasses the following fall, which have been hypothesized to be suitable environments for fungal growth due to their high nutrient content.” Ko observed that environmental methods to predict risk for infectious diseases, such as those proposed by Galgiani’s colleagues, must employ both a robust detec- tion method and a rational sampling scheme, and must be proven to be epidemio- logically valid. While admitting that detection of Coccidioides spores remained problematic, Galgiani proposed strain typing as a means to link spore levels in the environment to infection rates. He also noted a World War II remediation effort that confirmed one of the model’s underlying assumptions: precipitation during the “blow” period reduces exposure to aerosolized spores. During wartime, he said, wetting and oiling airfields and other exposed soil in California (another hot spot for coccidioidomycosis) was found to reduce infections (as measured by skin tests) by more than half among troops training at those locations. Mapping Disease Occurrence and Risk Maps that portray the extent and magnitude of infectious diseases can sup- port public health decision making, efforts to monitor the success or failure of interventions, and evaluations of specific disease-driving factors in space and time (Hay et al., 2013a,b). Global maps of infectious diseases are of potential use to several audiences, including international funding agencies, public health officials responsible for vaccine distribution, ministries of health with inadequate reporting capacity, and travelers in general, according to speaker Jane Messina, of the University of Oxford, England (Dr. Messina’s contribution may be found on pages 297–310 in Appendix A). In her workshop presentation, she described how she and coworkers in the Spatial Ecology and Epidemiology Group create such maps, and discussed ways to improve existing disease maps and to map more infectious diseases. She then used the example of dengue to illustrate the process and utility of global infectious disease mapping. Global mapping of diseases Of 355 diseases identified in the Global Infec- tious Disease and Epidemiology Network (GIDEON), 174 have been deemed

WORKSHOP OVERVIEW 71 “mappable” by Hay and coauthors (Hay et al., 2013b), who report that only seven diseases—all vector-borne—have been mapped comprehensively: coltiviruses (Old World), dengue, Lassa fever, Mayaro, monkeypox, and two forms of malaria (P. falciparum and P. vivax) (Hay et al., 2013a). Among mappable diseases, 80 (~46 percent) are caused by vector-borne pathogens, Messina stated. The mapping process follows the decision pathway illustrated in Fig- ure WO-24, which assigns diseases to one of five categories based on its dis- tribution (because it would be pointless to map ubiquitous diseases) and the availability of information on its ecology, reservoirs and vectors, and prevalence, Messina explained. If only occurrence data are available, as is the case for most VBDs, maps are limited to displaying the possible niches where the disease could exist, she noted; a statistical method known as boosted regression trees (BRTs) is used to construct such maps. If prevalence data are available, more advanced methods, such as model-based geostatistics (MBGs), can be used to map disease endemicity (Hay et al., 2013a). Understanding the current distribution of a disease is the first step toward predicting its response to global environmental change, Messina noted. Descrip- tions of disease distribution necessarily include uncertainty, which should be incorporated into calculations and made apparent in maps through such devices as confidence intervals associated with probabilities of occurrence, she noted. This is particularly the case for many VBDs, she added, which means construct- ing maps based on the assumption that disease observations fully represent the range of environments—the ecological niche—in which the pathogen can exist. This approach is known as “niche modeling.” However, she continued, even in the best case, “We don’t necessarily know all of the places in the world where the disease is absent—all we have is where it has been reported.” While agreeing with Dobson’s earlier observation that a map is only as good as the data upon which it is based, Messina described two methods to optimize the statistical processing of this data: · Cross-validation, which compares a subset of observations with a model generated with the remaining data; and · Ensembling, which involves iterative modeling to generate mean predic- tions and confidence intervals based on hundreds of simulations. Maps are not static, she said; they can always be improved with better data, with information shaped by evidence consensus and expert opinion, and with more discerning analytical and statistical methods. Case study: Global mapping of dengue  More than one-third of the world’s population lives in areas at risk for dengue, a mosquito-borne viral disease that is a leading cause of illness and death in the tropics and subtropics. As many as 100 million people are infected yearly; most experience high fever and severe

72 IGURE WO-24  A schematic of the disease classification process. The classification system results in diseases being categorized into one of five options: (1) do not map; (2) map observed occurrence; (3) map maximum potential range of reservoir or vectors; (4) niche/occurrence mapping with BRT; and (5) MGB-based endemicity maps. NOTE: BRT = boosted regression tree; MBG = model-based geostatistics. SOURCE: Hay et al., 2013a.

WORKSHOP OVERVIEW 73 headache, among other discomforts. An estimated 500,000 people are hospital- ized annually with a severe form of the disease, dengue hemorrhagic fever, which can be lethal. There are no specific protections from dengue, as an effective vac- cine has yet to be developed, nor are there specific treatments for its symptoms (CDC, 2013b; WHO, 2013a). Due to its wide distribution and rapidly growing global incidence, dengue has been called a “disease of the future,” according to Messina. Figure WO-25A illustrates the group’s map of the endemic status of dengue, based on informa- tion from multiple sources. Endemicity is depicted using a 200-point scale from certain absence (green) to certain endemicity (red). To create the occurrence map shown in Figure WO-25B, Messina and coworkers conducted a systematic search and treatment of evidence of transmission from nearly 9,000 unique reports in the formal literature and from ProMED and HealthMap. Construction of both maps was hampered by a lack of data from Africa; estimating the level of underreport- ing of dengue on that continent is a focus of future research, she said. Using their map of dengue’s endemic status (Figure WO-25A–C), which Messina described as “the first step in our niche modeling process,” she and FIGURE WO-25  Infectious disease global risk modeling framework: (a) evidence con- sensus; (b) disease occurrence locations; (c) pseudo-data; (d) spatial covariates; (e) average risk map produced by BRT ensemble. The dashed arrow represents the iterative nature of the procedure, whereby the new information provided by (e) informs future surveillance efforts, which then enables updating and improvement of both (a) and (b). SOURCE: Figure courtesy of Jane Messina.

74 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS coworkers added geospecific information on several key environmental vari- ables known to influence risk for dengue, including precipitation, temperature, socioeconomic status of local populations, and urbanization. The resulting risk map, shown in Figure WO-25, can be paired with cohort studies to infer unap- parent and apparent infections per pixel, and also paired with population surveys to estimate total numbers of infections on a national and global basis, she said. Uncertainty, she noted, “was propagated throughout the entire process,” and is reflected in their map’s depictions of national-level risk, as well as in confidence ranges for their estimates of infections per year at both national and global levels, she explained. To modify the current map to reflect global risk for dengue in 2020, 2050, and 2080—as the group has been charged to do by the European Commission— Messina and coworkers will incorporate information on several environmental trends, she said. These include the growth of urbanization in the tropics, warming temperatures, and increased travel and trade originating from endemic areas, all of which could facilitate dengue transmission. On the other hand, socioeconomic development in the tropics may mitigate the risk for disease, Messina noted. As for determining whether climate change has influenced global risk for dengue, she observed that advancements in dengue detection and reporting over the course of the Great Acceleration make such comparisons difficult, and that the apparent growth in disease incidence may not accurately reflect actual trends in its transmission. Modeling Anthropogenic Effects on Disease Transmission To consider the effects of global environmental change on the dynamics of VBDs is to address the following unanswered questions, posed by speaker Uriel Kitron, of Emory University (whose talk was a collaborative effort with Charles King from Case Western Reserve University and Dan Colley from the University of Georgia): · What are the impacts of environmental changes and variation on vector and reservoir populations, and on exposure? Are these relationships uni- versal, and if not, how do they vary locally? · How can we address heterogeneity of scale—in both time and space—of these impacts? · Can environmental changes be used to forecast changes in vector and host populations and the risk of outbreaks or spread of disease? · How do we apply environmental/climate data and models to the study of transmission and disease management? While it is possible to identify hot spots for disease transmission based on current knowledge of disease dynamics (e.g., as demonstrated by Messina’s

WORKSHOP OVERVIEW 75 mapping of current risk for dengue), it is difficult to predict how transmission may be affected by interventions or by other future anthropogenic activities, Kitron insisted. Moreover, he added, “We cannot assume that what happens globally is relevant to the local conditions and vice versa. We have to do both, and we have to work on many scales.” To begin to approach this challenge, he and coworkers have examined VBDs that persist under changing environmental conditions and/or intensive intervention: malaria and schistosomiasis, which have resisted eradication in known, often rural, hot spots, where they frequently infect the same person. Schistosomiasis is caused by trematode flatworms of the genus Schistosoma. Larval forms of the parasites, released by freshwater snails, penetrate the skin of people in the water. The larvae develop into adult schistosomes, which live in the blood vessels. The females release eggs, some of which are passed out of the body in the urine or feces; others remain in body tissues, where they cause an immune reaction. Urogenital schistosomiasis progressively damages the bladder, ureters, kidneys, and reproductive organs. Intestinal schistosomiasis causes progressive enlargement of the liver and spleen, intestinal damage, and hypertension of the abdominal blood vessels. Nearly 800 million people are at risk of schistosomiasis, which ranks second only to malaria among the parasitic diseases with regard to the number of people infected and those at risk (Steinmann et al., 2006; WHO, 2013b). In the area Kitron’s group conducts their research, on the south coast of Kenya, 10.7 percent of the population had malaria, 26.0 percent had schistosomi- asis, 21.4 percent had hookworm, and 9.3 percent had filariasis in 2009–2011, he reported. The rates for malaria in this area were higher than originally believed, and appear to be on the rise, he noted. Despite the availability of a treatment in- tervention for these diseases, it was apparent that asymptomatic cases were going untreated, and the transmission cycle was maintained. In the case of malaria, in addition to treatment, a transmission-interrupt- ing intervention—the introduction and wide adoption of insecticide-treated bed nets—was so effective as to be “transformative,” Kitron observed. A precipitous decline in malaria cases followed their introduction (Mutuku et al., 2011, and many other studies throughout sub-Saharan Africa). However, he continued, malaria rates increased as the bed nets wore out (Mutuku et al., 2013)—but this was not the only reason. The pathogen also adapted to transmission by a more versatile mosquito vector: a species that feeds earlier in the evening and later in the morning, and had also developed resistance to insecticides and adapted to urban habitats such as swimming pools (Impoinvil et al., 2008). Even so, he concluded, urbanization largely disrupts malaria transmission—in contrast to that of dengue—so the disease is on the decline. “Malaria will stay with us for a long time, but dengue will eclipse it both in numbers and distribution,” he predicted. It is difficult to make similar predictions about schistosomiasis, Kitron ob- served, deeming it “a very hard disease to model” due to its focal distribution

76 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS pattern and the age structure and mobility of the infected population. Among the villages they study, there are obvious hot spots for schistosomiasis and obvious superspreaders: young infected boys who swim in multiple water holes, provide a reservoir for the parasite, thereby spreading infection among themselves and others (Clennon et al., 2006). “We do have good treatment against schistosomia- sis, one that works very well in reducing infection intensity.” However, he added, “It’s only partially curative, since there is no residual effect, you treat but do not interrupt transmission, leading to people become[ing] re-infected.” Moreover, he added, targeting hot spots for treatment has proven to have limited effectiveness, because they are ephemeral—shifting locations when, for example, drought dries up known, infected, ponds (Clennon et al., 2007). “After 5 years of not finding snails and finding very little transmission in the known places, we still have about the same prevalence of infection,” Kitron said. “Intensity is down somewhat, but not as much as we expected. Even with our very intensive study of an area where transmission is relatively straightforward, we still are missing something big . . . we can’t really explain why it hasn’t gone down much more than it did.” Epidemiological models of transmission indicated that without drastic reductions in transmission, infection prevalence remains at low levels from which it can bounce back. “None of our models really have shown us even the potential strategy to get rid of the disease completely,” he admitted; only by eliminating the parasite in snails and people simultaneously is the disease likely to be controlled. Given the mobility of super-spreading boys— who often migrate to distant villages for months at a time—coupled with the difficulties in reducing snail populations, schistosomiasis control in this setting will require intense surveillance and detection of new hot spots as they emerge. “The time frame is long,” he concluded. “There is no quick fix.” The problem of shifting hot spots is not limited to schistosomiasis, Kitron observed—similar dynamics have been observed with dengue in Iquitos, Peru (LaCon et al., 2014), WNV in Dallas and Chicago, and Lyme disease in the United States and Europe, for example. Nevertheless, when asked about the overall prospects for managing VBDs, Kitron claimed to be optimistic. While researchers cannot model the long-term impact of current interventions, they are getting better at making connections between interventions and disease dynamics, he said—and are getting better in making short-term predictions, and even more long-term forecasts, albeit more limited ones, in some situations. Approaches to Identify and Address Factors Contributing to Disease Emergence Presentations in the final session of the workshop offered diverse examples of efforts to address risk factors for infectious disease emergence—as characterized in earlier sessions—through strategic prevention, surveillance, intervention, and response. While predicting and heading off a potential pandemic is the ultimate

WORKSHOP OVERVIEW 77 goal of such efforts, several workshop participants emphasized the value of prediction as a means of generating hypotheses that in turn spark the invention of novel diagnostic tools and interventions to reduce the burden of infectious diseases. Strategies to Predict and Anticipate the Emergence of Novel Pathogens Predicting where and when the next pandemic will strike, and what pathogen will cause it, is a primary goal of the EcoHealth Alliance, directed by Forum member Peter Daszak (Dr. Daszak’s contribution may be found on pages 182–193 in Appendix A). In his workshop presentation, he addressed the following “big questions for pandemic prevention,” and in so doing, extended his earlier response to the critique of pandemic prediction presented by Dobson in his keynote address (see the section “Understanding Infectious Disease Dynamics” on page 20): · Are EIDs really on the rise? · Can we allocate resources more strategically to combat them? · Are there predictable patterns to disease emergence? · Where will the next pandemic originate? · How likely will a new EID be to spread out of a region? · Which wildlife do pandemics originate in? · Can we identify potential pandemic pathogens before they emerge? · Can we stop them emerging? Infectious disease emergence is frequently described as occurring in stages (Lloyd-Smith et al., 2009; Morse et al., 2012; Wolfe et al., 2007). Figure WO-26 illustrates pandemic development as a three-stage process: 1. An early stage characterized by environmental disruptions that allow wildlife to make contact with livestock or people, resulting in spillover and small outbreaks; 2. A subsequent increase in number and size of disease outbreaks featuring short, “stuttering” chains of human-to-human transmission; and finally, 3. Pandemic disease, as has occurred with HIV, WNV, and SARS. “I believe that predicting the last stage is actually fairly straightforward,” Daszak stated, noting that several models can successfully predict disease spread based on epidemiological information obtained in the early stages of an outbreak. It is harder to make such predictions based on the dynamics of stuttering chains of transmission, but it can be done with very complex mathematical models, he con- tinued; however, predicting a pandemic’s progress from the very early stages of emergence, the first spillover of a new pathogen, remains a significant challenge.

78 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS FIGURE WO-26  Emergence of pandemic zoonotic disease. Stage 1 is a preemergence state, in which naturally occurring microbes are transmitted between their animal res- ervoirs. Disturbances to the ecology of these populations (e.g., due to changes in land use) change the dynamics of microbial transmission and can lead to a heightened risk of pathogen spillover to other nonhuman wildlife or livestock hosts (but not people). Stage 2 is localized emergence, either through self-limiting spillover events (green peaks and troughs, representing the rise and fall in numbers of infected people with time) or large- scale spillover (red peaks, representing spikes in the number of infected people with time), that leads to person-to-person transmission for a few pathogen generations. In stage 3, some spillover events might lead to indefinitely sustained person-to-person outbreaks, international or global spread, and the emergence of a true pandemic. The size, spread, and potential effect of events increase from stage 1 to stage 3, but the frequency falls so that full stage 3 pandemics are quite rare. By dissecting this process and analyzing the interactions of the underlying drivers with the risk of spillover and spread, development of a more structured approach to pandemic prevention is possible. The ultimate goal of successful pandemic prevention is to move the control point to stage 1. SOURCE: Morse et al., 2012.

WORKSHOP OVERVIEW 79 As a first step toward improving early detection of potential pandemics, Daszak and coworkers set out to map emergence “hot spots”—places across the globe where infectious diseases are likeliest to emerge (Jones et al., 2008). Their map, based on a comprehensive review of all infectious diseases reported between 1960 and 2008, was critiqued by Dobson in his presentation and briefly discussed afterward by Daszak. In his presentation, Daszak summarized the efforts his group made to correct various biases in their initial map, most importantly geo- graphic (favoring wealthy countries) and chronological (increasing effort over time) biases in emerging disease research. After these corrections were made, their calculations demonstrated that, on average, five new zoonoses emerge each year, and that zoonoses are also increasing as a proportion of emerging disease events. What factors are driving these changes? Further analysis of their data re- vealed human activity as a major driving force for disease emergence, and al- lowed the researchers to produce a predictive map (see Figure WO-27) that, according to Daszak, “tells us right now where the next pandemic is most likely to come from,” and therefore could serve as a general guide to allocating global efforts for pandemic prevention. Can we then predict how likely it is that an outbreak will spread across the globe? Reasonably well, Daszak demonstrated, by using the hot-spot map com- bined with the number of passengers on planes flying into and out of the outbreak area, and by taking into account that outbreaks tend to be more accurately reported in wealthier countries. Using the recent (2009) H1N1 influenza pandemic as an example, he and coworkers were able to use this method to “reverse predict” the strain’s spread out of Mexico, where it emerged, throughout the world (Hosseini et al., 2010). These refinements also enabled global mapping of vulnerability to zoonotic EIDs in general, and also to vector-borne EIDs (Figure WO-28). If pandemic prevention efforts focus on wildlife in hot spots, which species should they target? “Most zoonoses are mammalian in origin,” Daszak stated. Because detailed data on the diversity and number of viruses found on mammals is available, as well as information on the number of viruses animals share with humans, he and coworkers were able to identify those mammals likeliest to share viruses with humans: primates, rodents, and bats (Olival et al., in preparation). Major challenges  While Daszak and coworkers have made significant progress toward answering the aforementioned “big questions,” he identified three major obstacles to predicting emergence that remain to be overcome: · We do not know the number of unknown viruses; · We do not know how human contact with wildlife varies across a land- scape, nation, or planet; and, · We do not know how likely a new virus will be to infect people.

80 FIGURE WO-27  Global emerging disease “hot spots.” NOTE: Update of model found in Jones et al., 2008, using driver datasets as of 2009 and events as of 2010. SOURCE: Daszak presentation, 2013 (adapted and updated from Jones et al., 2008).

WORKSHOP OVERVIEW 81 FIGURE WO-28 Global vulnerability from (A) zoonotic EIDS and (B) vector-borne EIDS. SOURCE: Daszak presentation, 2013. In pursuit of the first of these unknowns, Daszak and coworkers made use of a standard statistical method for gauging biodiversity in order to estimate the total number of unknown mammalian viruses. They based their calculations on comprehensive viral discovery in 2,000 Pteropus giganteus bats22 from Bangla- 22  Commonly referred to as a fruit bat.

82 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS desh (Anthony et al., 2013). This bat species has become notorious as a carrier of the Nipah virus (discussed in the section “Local-Scale Interventions to Reduce Pandemic Risk” on page 84). Having determined that this species hosts about 58 unknown viruses, and assuming other mammalian species host a similar number of unknown viruses, the researchers calculated that mammals as a group possess about 320,000 unknown viruses. They further estimated that all of these viruses could be identified at a cost of about $6.8 billion (or 85 percent of them could be identified for about $1.4 billion)—cheap compared to the cost of a pandemic such as SARS, which took a $10 to $50 billion toll on the global economy, he noted. To learn how people make contact with wildlife, Daszak and coworkers are sampling viral diversity in sites representing pristine forest, fragmented habitats, and rural–urban centers in Borneo, Brazil, and Uganda—all hot spots for disease emergence. The researchers are examining known reservoir species in these areas for viruses with pandemic potential; they also use questionnaires to learn about residents’ contact with local wildlife. Determining how likely is it that a newly discovered virus will infect people is a major challenge, Daszak observed. “I think this is where there is a big black box around emerging disease. This is where we really need to think creatively and cleverly,” he said. “It is something that is going to involve sequences and proteins and cultures and really good virology.” Halting an already emerging pandemic represents an even more daunting challenge, Daszak noted, especially if it involves an unknown pathogen. Noting that nearly half of past EID events are attributable to land use, he suggested that businesses involved in environmentally disruptive projects—such as mining and logging—associated with risk for infectious disease outbreaks might be per- suaded to change their practices if there were a demonstrated financial advantage to do so. “There is a huge economic impact to the extractive industry,” he stated. “Ten billion to $40 billion in potential liability if they get blamed for emerging disease.” Health impact assessments that demonstrate such costs could reduce destructive land use, and thereby, infectious disease emergence. Similarly, from the consumer side, people are likely to reject products as- sociated (fairly or not) with the risk of infectious disease, Daszak observed. EcoHealth Alliance has developed a smart phone application, PetWatch, to help people choose exotic pets that do not come from disease hot spots, and that other- wise bear a relatively low risk of spreading infectious disease. The EcoHealth Al- liance has also launched a media campaign to discourage illegal trade in wildlife. A global perspective  In considering how resources should be allocated to pre- vent disease emergence and spread, Daszak and coworkers noted a fundamental problem: the term emerging has no universally accepted, empirical definition. It is “rarely backed by quantitative analysis and is often used subjectively,” they wrote. “This can lead to over allocation of resources to diseases that are incor- rectly labeled ‘emerging’—such as salmonellosis in Europe—and insufficient

WORKSHOP OVERVIEW 83 allocation of resources to diseases for which evidence of an increasing or high sustained impact is strong” (Funk et al., 2013). Daszak emphasized that a coordinated, global effort to address the threat of emerging diseases is crucial. “From the U.S. point of view . . . this is a biosecurity argument … [and] also an international development argument,” he stated. He endorsed the approach taken by the U.S. Agency for International Development (USAID) toward emerging pandemic threats, which emphasizes building capacity for infectious disease surveillance in developing countries in which emergence hot spots are located. “That will help solve the pandemic problem, but also help with building basic capacity for malaria, cholera, and all the other big killers of the tropics,” he observed. Daszak also decried the lack of international coordination evinced in the response to the recent emergence of Middle East respiratory syndrome (MERS; see footnote to Box WO-2). “We have brilliant models where we can predict the spread rapidly when a new disease emerges,” but, he observed, the global public health community is dealing with MERS “extremely poorly because of political and cultural differences between Saudi Arabia, other countries in the Middle East, and us here [in the United States], and everybody else. Until we truly are acting as one species, we have a big problem,” he warned. This occurred despite the involvement of nongovernmental and multinational organizations, including the Food and Agriculture Organization of the United Nations (FAO), the World Organization for Animal Health (OIE), the WHO, coupled with the revisions to the International Health Regulations (IHR). The revised IHR were designed to improve response to early outbreaks of emerging diseases, he noted with disap- pointment. “One has to ask the question: is the IHR really doing anything specifi- cally for MERS now?” he wondered. “I think that is an overly pessimistic look at progress of global governance,” Cetron responded. He credited the revised IHR with improving the global re- sponse to MERS, as well as to an outbreak of H7N9 influenza A that occurred in China in April 2013, as compared with the SARS epidemic in 2003 that catalyzed the IHR revisions. “There is not going to be one great universal global governance system that will hold everybody accountable to the same degree,” he remarked. “What success looks like may not be fewer outbreaks, but it may be more out- breaks of smaller size with more rapid intervention,” he added—and the world has made progress toward that goal under the formal disease reporting structure imposed by the IHR. “It is not perfect, but it is a lot better than where we were [when SARS emerged] in 2003,” he concluded. Daszak agreed with that assessment, noting that the ability for third parties to report outbreaks has increased reporting accuracy for infectious diseases. “One of the things that we will probably see over the next 10 years or so is trying to deal with the underlying drivers of emerging disease,” he observed—an area in which global governance could enable swift and effective interventions to reduce pandemic threats. Such an effort would also benefit from involvement by the

84 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS private sector, and in particular, multinational corporations involved in large-scale agriculture, logging, and mining, he noted. Local-Scale Interventions to Reduce Pandemic Risk To study emerging diseases means looking carefully at pathogens that cur- rently do not cause a high disease burden in humans, but which have the potential for uncontrollable human-to-human transmission, observed speaker Steve Luby, of Stanford University. These include zoonotic pathogens such as the Nipah and H5N1 influenza viruses (Lloyd-Smith et al., 2009; Wolfe et al., 2007). These are diseases that today cause “stuttering” chains of transmission in humans, but with very slight adaptation, could become important new human pathogens, he said. In response to such threats—and, as he described it, at the risk of taking what might be described as an “under-theorized approach” without “proper disciplin- ary credentials”—Luby’s team applies the following seven-step framework to designing and testing interventions against diseases with pandemic potential: 1. Identify the risks 2. Understand the reasons for the risk 3. Develop interventions that address the underlying reasons 4. Pilot interventions 5. Iteratively revise 6. Scale up 7. Evaluate His presentation focused on employing this framework to reduce the risk of human-to-human transmission of Nipah virus and H5N1 influenza in Bangladesh. Case study: Interventions against Nipah virus  Since diagnostics for Nipah became available, outbreaks have been regularly identified in Bangladesh, with an average case fatality rate of nearly 80 percent, Luby reported. Not only is Nipah lethal, but some people infected through exposure to the Pteropus bats that serve as the virus’ reservoir have been shown to have transmitted Nipah to other people (Luby and Gurley, 2012). According to their framework, Luby and coworkers first investigated how hu- mans became infected with the Nipah virus. Case-control studies in Bangladesh revealed that people who reported drinking raw date palm sap were significantly more likely to be cases than controls. This led to the suspicion that consumption of raw date palm sap may be an important route for human exposure to Nipah. The researchers were then able to make a clear connection between bats, sap, and humans, when they discovered that bats drink at the sap collection points on date palm trees, fouling the sap with saliva (and often, with feces and urine as well) which contain Nipah virus.

WORKSHOP OVERVIEW 85 FIGURE WO-29  Protective skirts for palm sap collection made from (A) jute; (B) doin- cha (Sesbania rostrata); (C) bamboo; and (D) polyethylene. SOURCE: Khan et al., 2012 (frames A, B, and D); Luby presentation 2013. Anthropologists learned from sap harvesters that collecting and selling the highly popular fresh palm sap, which may also be boiled to make molasses (a process that inactivates the virus), is often highly profitable. Luby and cowork- ers designed two types of interventions to address this risk: a campaign to warn people not to drink raw date palm sap, and several versions of protective “skirts,” that sap harvesters could manufacture themselves, to keep bats and other animals out of the sap as it is being collected (see Figure WO-29) (Khan et al., 2012).

86 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS Disappointingly, a pilot study to test whether sap harvesters would use the skirts, once informed of Nipah risk and encouraged by increased demand for cleaner sap, found that only about one-quarter of the sap collectors used the skirts during the first year, and fewer than 10 percent continued for a second year (Nahar et al., 2013). On the other hand, preliminary results of the carefully crafted “just say no to raw sap” media campaign suggested that overall raw sap consumption declined by more than half among both men and the women in villages exposed to this message, Luby stated. This result appears quite variable from village to village, however, so he and coworkers plan to launch a two-tiered message campaign—“just say no” and “skirts make sap safer”—in the hope that this dual messaging effort will reduce outbreaks that are still occurring. Rather than convince sap collectors to use skirts, the key to reducing infec- tion risk “is convincing the date palm sap drinkers that they want to drink sap from a protected tree,” Luby observed. “If people ask for that—and this is a local activity, so they can see whether it is done or not—I think that demand will drive and support the behavior change.” Case study: Interventions against H5N1 influenza  Bangladesh is a crowded country, in which some 150 million people (the approximate population of the East Coast of the United States) inhabit an area the size of Iowa—along with 183 million chickens and 37 million ducks, according to Luby. More than half of all Bangladeshi households raise their own poultry, keeping them indoors at night to prevent theft; birds are also sold live and slaughtered in open markets. All of these conditions raise the risk that H5N1 influenza, a strain endemic to Bangladesh and associated with a 60 percent case mortality in humans who have become infected through contact with birds, will emerge as a human-transmitted pandemic disease, Luby warned. If an outbreak of H5N1 in poultry occurs si- multaneously with one of human influenza—also common in Bangladesh—there is a risk that a person co-infected with both strains could serve as an incubator for a novel, human-transmitted form of H5N1. Such a pathogen, he said, would constitute “a severe global risk.” People at highest risk for such simultaneous infections are (1) those who raise poultry without regard to biosecurity, as is typical for backyard livestock operations, (2) those who slaughter infected birds, (3) those who work in live bird markets, and (4) those who provide care for avian influenza patients. There are so many risks in Bangladesh that it is difficult to choose one on which to focus, Luby observed—but ending the raising of poultry by individual households is not one of them, because poultry provides significant nutrition and income in this extremely impoverished country (Sultana et al., 2012). Instead, the researchers attempted to design a message campaign to improve slaughtering practices (e.g., not slaughtering sick birds for consumption or sale); this was determined by follow-up anthropological study to be ineffective. Villagers exposed to messages

WORKSHOP OVERVIEW 87 could recall them and thought they were truthful, he said, but their practices remain largely unchanged. Another intervention targeted family care-giving practices, which are im- portant even within hospitals (Blum et al., 2009). Family members, rather than nurses, provide physical care and desire close physical contact with hospital patients, especially if they are dying, Luby explained. As a result, hospitals in Bangladesh are crowded; moreover, hand-washing stations tend to be reserved for staff, in part because soap is an expensive commodity in this extremely poor country. So, while hand washing is a highly effective way to control disease transmission, the researchers were forced to focus on other risks more amenable to reduction, such as convincing people not to share beds or face-to-face contact with sick relatives (Gurley et al., 2013). However, Luby added, the team recently launched pilot studies of hand-washing interventions using low-cost soap and alcohol-based gel. “It looks like there is reasonable uptake, but we are interested in continuing to iterate around this to collect data,” he said. “We still have many steps to go.” Sound interventions to reduce pandemic risk are feasible, Luby concluded. “They are not simple,” he noted. “They require an appreciation of local con- straints. They require an iterative scientific process and long-term engagement with multidisciplinary teams.” Lessons learned  Many cycles of developing and testing interventions against Nipah and influenza in Bangladesh revealed the critical importance of collabora- tion between scientists and government, Luby observed. He noted that consid- erable time and evidence were required to convince government officials that people should be discouraged from the culturally established practice of drinking raw date palm sap. Yet, once persuaded, he continued, these officials also stuck by that message, not wanting to dilute it with the notion that skirt use made raw sap safe; thus even more data were needed to convince them that “just say no” was not sufficiently effective. “I think a big part of working towards change is generating evidence that policy makers find persuasive,” Luby concluded. “[Through] engagement with government, focusing on where the data sends you, we can make meaningful contributions.” Such decisions resonate far beyond villages in Bangladesh, he added. “I think it is important to recognize that the problem of reducing the risk of emerging disease is not fundamentally a problem of Bangladesh. It is a prob- lem of humanity.” Modeling Emerging Infectious Diseases According to speaker Neil Ferguson, of Imperial College, London, math- ematical models address several stages of infectious disease emergence. Dur- ing preemergence, models can assess risk and improve preparedness, he said;

88 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS post-emergence, models enable the rapid assessment of epidemiological data and pathogen genetics, predict short-term risk, and inform control policy optimiza- tion. In his presentation to the workshop he described his work on two of these applications: estimating risk associated with viral pathogens that have achieved “stuttering” transmission in humans, and the rapid assessment of post-emergence transmission dynamics at the outset of the H1N1 pandemic in 2009 and after the recent emergence of MERS. Estimating risk for emergence  As noted by Daszak and Luby, the preemergence stage of pandemic development is characterized by sporadic human-to-human transmission, described as “stuttering chains of transmission” or “viral chatter” (Antia et al., 2003; Lloyd-Smith et al., 2009). During this stage, mutations ac- cumulate in viral pathogens that eventually allow them to shift from animal-to- human to human-to-human transmission, but there is no selection pressure for viruses to adapt to human hosts until they are readily transmissible among them, Ferguson noted. This process has been modeled many times, he said, but it would be ideal to link this transition to data that reveals the specific changes that enable human-to-human transmission in order to understand the “evolutionary hurdles” a virus would have to cross, and the likelihood that it could do so. Ferguson and coworkers have approached this challenge using genome-wide association studies, a technique that allows researchers to pinpoint the minimal set of genetic changes that would permit a zoonotic virus to infect humans, and with that knowledge, to determine the probability that these changes would occur (Aguas and Ferguson, 2013; Russell et al., 2012). “If we know something about the mutational barrier a virus needs to cross, the number of end point mutants that need to accumulate, we can use mathematical modeling to say what the prob- ability [is that] this will occur,” Ferguson said. “You have to make an awful lot of assumptions, but effectively you are building a model of . . . pathogenesis within a person.” From there, he continued, you can estimate how many people need to get infected over a given time period in order for the virus to have a significant chance of emerging. This process is highly speculative, because it includes as- sumptions about the fitness of the viral intermediates. Nevertheless, he observed, “It gives you some ballpark estimate.” In the case where a virus has already begun to infect humans, such as the swine influenza variant H3N2v, which has caused limited human cases since it was first reported in 2011, one can use epidemiological data to assess whether this virus is more transmissible in humans than other swine strains, and whether it can generate sustained epidemics in humans, Ferguson stated. In collaboration with colleagues at the CDC, his group was able to estimate the relative transmissibility of the variant virus in humans from limited information (they knew the source of infection in detected cases, but lacked examples of complete and representa- tive chains of transmission). “If you imagine a virus is really quite transmissible then most of the cases you will start to see, which you pick up randomly, will

WORKSHOP OVERVIEW 89 have not had any exposure to swine,” he explained. “But if the virus is really not transmissible at all from person to person, then all of the cases can be associated to swine.” Their results suggest that H3N2v is more transmissible from person to person than its predecessor (H3N2v is a variant of the H3N2 influenza virus that infected 321 people in the United States in 2011 and 2012), but insufficiently transmissible to cause a sustained epidemic, he stated (Cauchemez et al., 2013a). Rapid assessment of 2009 H1N1 and MERS  “Perhaps the busiest time in the last few years was during the H1N1 pandemic in 2009 where we undertook with collaborators around the world particularly Mexico and the World Health Orga- nization and later with the Centers for Disease Control and Prevention a number of rapid assessments,” Ferguson recalled. Early on, the researchers focused on four key questions: 1. How severe is the illness? 2. How far has it spread? 3. How fast is it spreading? 4. What can be done? Using the “case curve” from Mexico City (showing the number of cases identified each day as the epidemic unfolded, and known to be biased toward severe cases) and case reports from surveillance of travelers at the U.S. and European borders, the illness was quickly determined to be more transmissible than seasonal flu, but less so than the strains that caused any of the three landmark influenza pandemics (in 1918, 1957, and 1968), he reported. Helpful and reassuring as this information doubtless was, it was not suffi- ciently accurate to inform the sorts of policy decisions that are needed to control a pandemic in today’s world, Ferguson observed. Because their estimates were based on reported disease among people who sought health care for their symp- toms, they likely reflected a fraction of the people infected with H1N1. “We did not really know what proportion of an iceberg those reported cases were,” he stated, so his group is pursuing analytical approaches that would allow them to impute the number of true cases from those reported. He noted that track- ing cases indirectly—through data mining methods such as those employed by Google Flu Trends, HealthMap, and ProMED—could be of limited use in these circumstances. However, such informal data sources are far from definitive, and thus cannot serve as disease-specific surveillance systems, he added. Similar challenges are now facing Ferguson and coworkers as they attempt to understand—and thereby help to control—the emerging viral disease, MERS- CoV. Since the first human cases of this β-coronavirus were reported in 2012, there have been more than 228 cases of MERS, he reported. Marano previously noted that bats and camels remain suspected—but not confirmed—reservoirs for MERS-CoV.

90 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS Ferguson observed that two possible scenarios could account for the current epidemiological data on MERS emergence: reported cases may have largely been transmitted to humans by animals, in which case the virus remains incapable of sustained human transmission; or, human-to-human transmission is already suf- ficiently facile to support epidemic disease (Cauchemez et al., 2013b,c). Distin- guishing between these alternatives is difficult due to the poor quality of available epidemiological data, he said. As they did early on following the emergence of H1N1, the researchers are combining multiple analyses in hopes of resolving a general picture of MERS emergence, rather than a definitive history. One method they have used to do this is to use the number of cases exported out of the affected area in the Middle East as an indicator of the number of cases actually occurring there. With that number (four cases), along with passenger flow information—as- suming that visitors and locals bear the same risk of infection—it is possible to calculate how many people in the region must have been infected. “You come up with an answer of around a thousand [cases to date], with big confidence inter- vals,” he reported—a figure similar to one the group obtained using a phylody- namic approach, which estimates viral spread according to sequence divergence. Neither of these estimates is sufficiently robust to distinguish between the two scenarios he posed, Ferguson stated—although evidence appears to favor significant human-to-human transmission of MERS. Its overall impact remains to be determined, because the relatively few observed cases are almost among the most severe, he observed; however, more accurate fatality rates can be estimated by comparing fatality rates for the first- and second-detected infections in case clusters. He also noted that data from case clusters can be applied to estimate dis- ease transmissibility. Fortunately, in the case of MERS, all case clusters detected to date have been limited, which demonstrates that disease control measures effectively stop transmission once imposed within a case cluster. These results, viewed in concert with estimates of transmissibility based on the case curve itself, and on phylodynamic analysis of viral diversity, depict MERS as it currently ex- ists as a “slow epidemic.” Better data, better models During subsequent discussion, workshop partici- pants considered how several assumptions made in the models Ferguson de- scribed might be strengthened. The lack of epidemiological data on MERS is a source of particular concern. Ferguson noted that as a result, in part, of increased attention from researchers, health officials in Saudi Arabia and other affected countries are more consistently performing follow-up investigations of cases, and increasingly detecting milder cases. “The data collected just in the last few weeks very much validates what I presented,” he reported. “The evidence is that clearly, if you look harder, you are going to detect more cases. If you are picking up index cases through some sort of random severe disease surveillance system, or doctors testing, then those submitted are likely to be the more akin to the ‘iceberg’ [of total cases].”

WORKSHOP OVERVIEW 91 Similarly, knowledge of exposure risks for travelers, as compared with the general population in endemic areas, will improve estimates of disease based on cases among travelers, Ferguson stated—but this information is difficult to obtain, and even more difficult to interpret. “If you assume there is human-to-human transmission, it is entirely likely that there is a certain degree of contact discon- nect between travelers in hotels and the local population,” he explained. “This sort of estimate would underestimate the scale of the epidemic in Saudi Arabia. Con- versely, though, if there is not much human-to-human transmission, and what we are really talking about is exposure to animal reservoir, then tourist-type activities might actually increase your exposure. You would need to do some very difficult studies to resolve that.” However, he added, highlighting the need to resolve such issues is an important function of disease models. “Modeling is often about hypothesis generation,” he observed: it is not just giving possible interpretations of data, but also feedback to people collecting the data that suggests new studies. As was discussed in several contexts during this workshop, differences among the various definitions of a disease “case” present a significant challenge to understanding disease transmission dynamics—and this applies as well to models of emerging infections. While increasing work is being done to improve the analysis of surveillance data from multiple sources, Ferguson said, ultimately researchers must recognize that there is not a fixed case definition, and be aware of its implications. “We always recognize we are seeing a partial picture,” he observed. “When we do have clinical data, we use it to try and categorize cases. Often, though, we are left with what we have. Modeling is never a substitute for data.” Investigating the Influence of Population Shifts on Disease Dynamics Disease models typically involve algorithms that estimate dynamic param- eters, such as transmission or recovery rates, from information such as the num- ber of infected and susceptible people and their degree of contact. The influence of population dynamics—demography, mobility, and migration—is generally missing from such models, according to speaker Nita Bharti, of Penn State and Stanford Universities (Dr. Bharti’s contribution may be found on pages 154–165 in Appendix A). Failing to recognize that these factors are not static hinders our understanding of disease dynamics, as well as our ability to predict or change them, she observed. Rather, what is needed is a “merged understanding of popu- lations and diseases.” Studies of HIV-1 clusters along African road networks (Tatem et al., 2012) and influenza transmission through commuter flows in the United States (Viboud et al., 2006) demonstrate that human mobility and migration impact spatial pat- terns of disease transmission at different scales, Bharti noted. This point—and its consequences for disease control—was further borne out in her work on measles

92 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS transmission dynamics in Niger, which she described in her presentation to the workshop. Case study: Measles in Niger  Annual epidemic measles regularly kill as many as 150,000 children each year in Niger, despite the availability of inexpensive measles vaccine, Bharti reported. To understand the persistence of this disease despite efforts to control it, she and colleagues from Niger’s Ministry of Health and Doctors Without Borders investigated the mechanisms underlying these cy- clical outbreaks in three major cities: Maradi, Niamey, and Zinder. The seasonal pattern of measles outbreaks in Niger proved to be unusual in two respects. First, Bharti noted, rather than coinciding with the school term, as is typical, measles strikes Niger during the dry season, and declines significantly with the onset of the rainy season. Secondly, transmission is not correlated with rainfall, as would be expected if transmission rates were determined purely by environmental conditions (Ferrari et al., 2010). Recognizing that labor migration is common in this region, which is economically dependent on agriculture, the researchers attempted to examine the influence of migration patterns on disease dynamics. First, the researchers tested the hypothesis that population density fluctuates seasonally in the three cities, with large numbers of migrants living in low-density agricultural areas during the rainy season and returning to high-density urban areas during the dry season (Faulkingham and Thorbahn, 1975; Rain, 1999). After considering several different sources of data on population density and location, they chose to use satellite imagery composites of visible, anthropogenic light at night (Bharti et al., 2011; Elvidge et al., 1997; Pozzi et al., 2003). While cumbersome, this analysis had several advantages, Bharti explained: the images are captured daily and available within 48 hours; they are of increasingly high resolution (to 1 kilometer during her study period); they are publicly available; and, they have been collected and archived for several decades, so baselines can be established. While not appropriate for measuring migration in all situations (particularly in small towns and villages), the researchers determined that these images are sufficiently sensitive to reveal population changes in cities such as those that constituted their study area. “We see that there is a strong decrease in brightness during the rainy sea- son in each of these three cities,” Bharti said, “but this is a retrospective study. We could not go back in time and count the people that were there.” Thus the researchers attempted to validate their result through close study of the spatial progression of measles within Niamey, during an especially large outbreak in 2003–2004, when cases were recorded daily in each of three city subdivisions known as communes. “If population density was driving the spatial progression of measles through the city of Niamey, then the measles cases and the brightness values should show similar patterns within each commune,” she explained. This turned out to be the case: two communes had high brightness, which peaked at the

WORKSHOP OVERVIEW 93 same time as the measles cases, she reported; the third, less-bright commune had fewer cases. Additional modeling confirmed that with the correctly timed increase and decrease in population size, the researchers could predict the trajectory and peak of measles outbreaks in Niamey (Bharti et al., 2011). Bharti then described how she and coworkers applied their findings to im- prove the effectiveness of measles vaccination efforts in Niamey. Following the 2003–2004 epidemic, Niger’s Ministry of Health conducted a campaign, which proved only to be marginally effective, so they wanted to know why (Dubray et al., 2006). “We thought that if the variation in the vaccine coverage was due to the seasonal movement of the population, then it should be correlated with . . . changes in brightness that we think are indicating the change in population,” she said. They were able to show this was essentially the case. “Conventional wisdom in measles vaccination strategy tells us that we should vaccinate during the troughs of infection, to get ahead of disease and eliminate some of the nonlinearities in transmission,” she noted. Their results, however, suggest that for catch-up campaigns (not reactive interventions) vaccinating during the urban phase of the migratory cycle would be better for increasing regional vaccine coverage, even if it missed the infection trough. This timing is also advantageous, because it is easier to recruit people to be vaccinated when they are not scattered across a rural landscape. “We can take advantage of these rural–urban migration patterns and vaccinate people when they are coming to the cities,” Bharti observed. A clear understanding of population dynamics not only helped to explain the mechanism underlying the dynamics of measles transmission in their study area, but also to inform better disease control, Bharti concluded. She noted that a similar approach could prove valuable in other contexts, such as to investigate the contribution of rainy-season migration to malaria transmission dynamics, and the effects of drought on migration patterns, which in turn could affect the transmission of several infectious diseases. Integrating and Applying Information on Demography, Health, and Migration Prominent among the effects of the Great Acceleration in the United States has been a wave of immigration, rivaling that of the early twentieth century. Today, approximately 12 percent of the U.S. population—more than 40 million people—are foreign-born, that is, born outside the country to noncitizen parents, according to Cetron, of the CDC. Foreign-born residents of the United States represent 105 countries, with China, India, Mexico, the Philippines, and Viet- nam combined accounting for half of this population (Table WO-3). In 2010, he reported, about 1 million immigrants entered the United States, of whom more than 70,000 were refugees, along with 36 million nonimmigrant visitors and 127 million border-crossing commuters.

94 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS TABLE WO-3  Top 15 Source Countries with Largest Populations in the United States as Percent of Foreign Born, 2008 % of U.S. Foreign-Born Country Number of Foreign Born Population Mexico 11,467,856 30.3 China 1,899,643 5.0 Philippines 1,652,430 4.4 India 1,535,038 4.1 Vietnam 1,110,878 2.9 El Salvador 1,078,337 2.9 Korea 1,019,757 2.7 Cuba 958,548 2.5 Canada 824,354 2.2 Dominican Republic 762,511 2.0 Guatemala 718,993 1.9 Jamaica 622,431 1.6 Germany 635,065 1.7 Colombia 596,104 1.6 Haiti 522,678 1.4 Other 12,385,534 32.8 Total of Top 15 Birth Countries: 25,404,623 67.2 Total U.S. Foreign-Born Population: 37,790,157 12.4 SOURCE: American Community Survey, 2006–2008. Given the significant role of mobility and migration as a driver of infectious disease emergence and spread—along with deep global disparities in health care—it is important to identify pathogens that are likely to be crossing our bor- ders along with visitors and immigrants, Cetron said. For example, he noted, tu- berculosis (TB) rates in the United States—including multidrug-resistant disease (MDR-TB)—are 10 times higher among foreign-born residents than among the general population. Until 2007, very little had been done by the CDC to address this health disparity. In that year, the CDC changed its screening policies to in- crease the effectiveness of both detection and treatment of TB among immigrants, he noted. As a result, TB rates are declining among foreign-born U.S. residents for the first time in several decades. Disease screening programs for immigrants provide opportunities for surveil- lance of a range of emerging infectious diseases, some of which may be most effectively addressed through intervention in their countries of origin (see Fig- ure WO-30). “Most of these are events that should never happen, such as vaccine- preventable diseases,” Cetron observed. Simply administering anti-malarial or anti-parasitic medications to refugees prior to their arrival in the United States has proved highly effective from both medical and cost standpoints, he stated (Stauffer et al., 2008; Swanson et al., 2012). In Africa, the CDC’s Refugee Health program in Kakuma and Dadaab refugee camps in Kenya conducts a wide range

WORKSHOP OVERVIEW 95 FIGURE WO-30  More than 60,000 children were vaccinated against measles and polio in the Za’atari refugee camp in Jordan during a coordinated and targeted campaign in April 2013. SOURCE: Cetron presentation, 2013; UK Department for International Development. Photo credit: Peter Millett/British Embassy Jordan. of public health activities, including TB screening and treatment, syphilis testing, influenza surveillance, measles vaccination, and field investigations of outbreaks. More importantly, this program is building health infrastructure for the ongoing detection, treatment, prevention, and control of infectious diseases, he said. The CDC also provides interventions to control outbreaks of measles and prevent the spread of other infectious diseases among Syrian refugees of civil war living in camps in neighboring countries. During April 2013, more than 60,000 Syrian children in refugee camps were vaccinated against measles and polio. Introducing BioMosaic  The United States will soon accept Syrian women and children refugees, who are likely to benefit—along with their communities and local health care services—from a new tool developed by the CDC called Bio- Mosaic, according to Cetron. A recent fact sheet from the agency describes it as follows (CDC, 2013a): BioMosaic is a big data fusion and visualization project that integrates demogra- phy (human, animal, and environmental), health (disease profiles and emerging threats) and migration data into a common platform. The platform is linked by GIS coordinates that allows geospatial and temporal visualizations over mobile

96 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS and web interfaces. It has numerous applications for retrospective and prospec- tive analyses enabling high-level risk assessment and pandemic threat forecast- ing to enhance detection, response, and prevention. BioMosaic has aided international responses to recent global health threats such as H1N1 pandemic influenza, cholera following the Haiti earthquake, H7N9 influenza, and MERS-CoV. When Syrian refugees enter the United States, Bio- Mosaic maps will keep track of their locations at a county level, enabling health officials to target interactions, ensure access to treatment for TB, and continue screening for other health issues as appropriate, Cetron predicted. “We think that this is an opportunity to change the paradigm” of immigrant screening, he observed. Rather than a means of exclusion, information available through BioMosaic provides an opportunity to conduct infectious disease surveillance and outbreak response, and to target interventions for prevention and treatment. Ultimately, the CDC and its BioMosaic collaborators, the BioDiaspora proj- ect of the University of Toronto23 and HealthMap of Harvard University,24 antici- pate that BioMosaic will integrate data from a range of sources on human health and migration, insect and vector distribution, animal movements and speciation, and environmental data such as temperature, rainfall, and anthropogenic light (as described by Bharti), Cetron stated. Applications have been developed for both Web and iPad. At present, the platform is available only within the CDC and its collaborators. It will include publicly available information on the intersection of demography, migration, and health in the U.S. foreign-born population, he said. The Web version, designed for technical applications by registered users, incorporates a more comprehensive, global set of interfaces and data layers. The range of possible applications of BioMosaic is summarized in Box WO-4. Case study: BioMosaic investigation of MERS  Cetron and colleagues used BioMosaic to study the potential for the international spread of MERS-CoV out of the Arabian Peninsula (Khan et al., 2013). Of particular concern was an annual gathering of millions of Muslims in Mecca, Saudi Arabia, during the pilgrimage known as the Hajj, which in 2013 took place in October. To help cities and coun- tries worldwide assess their potential for MERS-CoV importation following the 2013 Hajj, the researchers examined Hajj-related travel to the area over the pre- vious decade, along with worldwide flight patterns, to predict population move- ments out of Saudi Arabia and the broader Middle East. They also compared the magnitude of travel to countries, their capacity for timely detection of imported MERS-CoV, and their ability to mount an effective public health response, as indicated by their economic status and per capita health care expenditures. Figure WO-31 illustrates the result of this analysis, and reveals that Egypt, In- dia, Indonesia, and Pakistan are among the highest-risk countries for MERS-CoV 23  See http://www.biodiaspora.com (accessed August 6, 2014). 24  See http://healthmap.org/site/about (accessed August 6, 2014).

WORKSHOP OVERVIEW 97 introduction, Cetron observed. “The vast majority of these pilgrims are coming from and going back to very-low-income countries where we expect the surveil- lance capacity to detect the introduction of cases to be quite small,” he said. “Understanding this and prioritizing the ranking of those countries really helped us advise WHO and these other countries on where to target early diagnos- tic surveillance capability, enhanced recognition, nosocomial infection control guidelines, and to prioritize them in areas that are particularly vulnerable by this assessment.”25 Useful though it is, BioMosaic is not a predictive tool, Cetron insisted. Rather, it reveals patterns that arise from the intersection of multiple data sets and generates hypotheses that must be tested through specific studies that take into account local variations in environmental conditions and population dynamics, he explained. Invoking Stephen Hawking’s observation that the greatest enemy of knowledge is not ignorance, but the illusion of knowledge, Cetron warned against confusing the correlations BioMosaic generates with evidence of causa- tion. The purpose of BioMosaic is to distill simple patterns from large amounts of complex information; the interpretation of those truths and patterns remains to be determined experimentally, he concluded. Conclusion Cetron’s presentation on BioMosaic brought together many recurring themes of the workshop: · The complex interactions of anthropogenic drivers of infectious disease emergence and spread, · The usefulness and limitations of maps (along with other quantitative and modeling tools) as a means to characterize and predict those interactions, · The importance of local and temporal variation in environmental condi- tions and population dynamics, and · And the need to distinguish between correlative and causative observa- tions of environmental change and disease dynamics. In his closing remarks, Forum vice-chair James Hughes, of Emory Uni- versity, emphasized the challenges researchers, and health officials, face when communicating the need and means to address emerging pathogens to the global community—particularly to people most at risk to bear the burden of infectious disease. 25  As of December 3, 2013, only two Hajj pilgrims have tested positive for MERS-CoV. According to the Montreal Gazette (and as noted in ProMED Mail), the cases of the two women from Spain, who were traveling together, are designated “probable,” because tests done by Spanish laboratories have only met part of the WHO’s criteria for a confirmed case (Branswell, 2013).

98 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS BOX WO-4 What Can BioMosaic Do? BioMosaic uses layers of information to depict health risks visually through Web and iPad applications. Public health officials can then use the information to detect, respond, and prevent any type of public health emergency. Specifically, BioMosaic can be used for: ·  Surveillance—By monitoring multiple data sources to identify outbreaks, emerging diseases, events of public health significance, media reports, or other sources, BioMosaic helps predict health threats. For example, during the cholera outbreaks in Haiti in 2010, the CDC’s Division of Global Migration and Quarantine (DGMQ) used BioMosaic to monitor reports of diseases in Haiti to determine what diseases could be introduced into Haiti or brought back to the United States. ·  Evaluation of conditions at the source of a public health event—By layering information about environmental conditions, animal and human populations, and any changes from historical data in the area where a disease has been identified, BioMosaic provides new insights about health risks. During the 2013 H7N9 influenza outbreak in China, BioMosaic was used to determine the potential risk of exposure by visually comparing poultry and swine density with the human population density in China. ·  Evaluation of transportation networks from an event source into the United States—By analyzing flight schedules, flight capacity, and final passenger destination, BioMosaic provides information that can be used for public health responses at entry points to the United States. During the cholera outbreaks in Haiti in 2010, BioMosaic helped DGMQ ef- fectively target detection, education, and prevention messages at airports where travelers from Haiti were arriving. “It has never ceased to amaze me over the years: all the ingenious things that we as a species do to aid and abet the microbes,” Hughes declared. Indeed, a de- cade after SARS raised global awareness of emerging diseases, MERS-CoV—an- other novel coronavirus—serves to remind us of the many ways in which human agency drives pathogens to adapt to our species. Indeed, it is as a species that we must respond to this threat, as Khan and coauthors (2013) note:

WORKSHOP OVERVIEW 99 ·  Evaluation of conditions in the United States—By visualizing distribution of foreign-born populations and performing demographic analysis (English proficiency, education level, etc.), BioMosaic can map risk areas where interventions should be targeted. In 2010, DGMQ used BioMosaic in Haiti to collect information on where Haitians lived in the United States and shared that information with state health partners to help inform outreach and communication efforts. ·  Analysis to determine where the CDC intervention is needed—By considering the layers of information provided through BioMosaic, the CDC can determine the best populations, locations, and timing for public health interventions. During the CDC’s response to the 2013 outbreak of MERS-CoV, BioMosaic provided information about historic trends of travelers attending the Hajj, a reli- gious gathering which draws about 3 million Muslims from around the world, and more than 11,000 Americans each year. BioMosaic also provided peak periods for travel and their countries of origin, which helped DGMQ target communication messages and timing for outreach to Hajj travelers. ·  Building risk models and testing public health hypotheses—By layer- ing information that has never been pulled together before, BioMosaic may reveal new information on public health problems. For example, the Aedes aegypti mosquito can transmit both dengue fever and yellow fever. However, dengue is found all over the world and yellow fever is lim- ited to a few locations. By looking at layers of information about the environment, populations affected, and regions where dengue and yellow fever exist, BioMosaic may help illuminate something new about the spread of disease. SOURCE: CDC, 2013a. The emergence of MERS-CoV requires an internationally coordinated effort to mitigate its potential global health and economic consequences, with particular emphasis on supporting diagnostic and public health response capacity in vul- nerable, resource-limited countries. Understanding the most probable pathways for international spread of MERS-CoV could help medical and public health providers worldwide operate in a far more anticipatory and less reactive manner than occurred during SARS.

100 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS FIGURE WO-31  Final destinations of air travelers departing Saudi Arabia, Jordan, Qa- tar, and United Arab Emirates from June to November 2012 and origins of foreign Hajj pilgrims by World Bank income classification. SOURCE: Khan presentation, 2013; data from Khan et al., 2013. REFERENCES Acemoglu, D., S. Johnson, and J. A. Robinson 2001. The colonial origins of comparative develop- ment: An empirical investigation. American Economic Review 91:1369-1401. Ackery, P. R., and R. I. Vane-Wright. 1984. Milkweed butterflies, their cladistics and biology, being an account of the natural history of the Danainae, a subfamily of the Lepidoptera, Nymphalidae: British Museum (Natural History). Afrane, Y. A., G. Zhou, B. W. Lawson, A. K. Githeko, and G. Yan. 2006. Effects of microclimatic changes caused by deforestation on the survivorship and reproductive fitness of Anopheles gambiae in western Kenya highlands. American Journal of Tropical Medicine and Hygiene 74(5):772-778. Aguas, R., and N. M. Ferguson. 2013. Feature selection methods for identifying genetic determinants of host species in RNA viruses. PLoS Computational Biology 9(10):e1003254. Altizer, S. M. 2001. Migratory behaviour and host-parasite co-evolution in natural populations of monarch butterflies infected with a protozoan parasite. Evolutionary Ecology Research 3(5):611-632. Altizer, S. M., K. S. Oberhauser, and L. P. Brower. 2000. Associations between host migration and the prevalence of a protozoan parasite in natural populations of adult monarch butterflies. Ecological Entomology 25(2):125-139. Altizer, S., R. Bartel, and B. A. Han. 2011. Animal migration and infectious disease risk. Science 331(6015):296-302. Altizer, S., R. S. Ostfeld, P. T. Johnson, S. Kutz, and C. D. Harvell. 2013. Climate change and infec- tious diseases: From evidence to a predictive framework. Science 341(6145):514-519. American University. 2011. ICE case studies: Cyclone Nargis, climate and conflict. Number 249, July 2011. http://www1.american.edu/ted/ice/cyclone-nargis.htm (accessed December 17, 2013).

WORKSHOP OVERVIEW 101 Anderson, P. K., A. A. Cunningham, N. G. Patel, F. J. Morales, P. R. Epstein, and P. Daszak. 2004. Emerging infectious diseases of plants: Pathogen pollution, climate change and agrotechnology drivers. Trends in Ecology and Evolution 19(10):535-544. 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):e00598-00513. Antia, R., R. R. Regoes, J. C. Koella, and C. T. Bergstrom. 2003. The role of evolution in the emer- gence of infectious diseases. Nature 426(6967):658-661. Autin, W. J., and J. M. Holbrook. 2012. Is the Anthropocene an issue of stratigraphy or pop culture? GSA Today 22(7):60-61. Barmore, W. J., Jr. 2003. Ecology of ungulates and their winter range in northern Yellowstone Na- tional Park, Research and Synthesis 1962–1970. Yellowstone Center for Resources. Bartel, R. A., K. S. Oberhauser, J. C. de Roode, and S. M. Altizer. 2010. Monarch butterfly migration and parasite transmission in eastern North America. Ecology 92(2):342-351. Battisti, D. S., and R. L. Naylor. 2009. Historical warnings of future food insecurity with unprec- edented seasonal heat. Science 323(5911):240-244. BBC. 2013. UN says Syria refugee crisis worst since Rwanda. http://www.bbc.co.uk/news/world- middle-east-23332527 (accessed November 21, 2013). Bebber, D. P., M. A. T. Ramotowski, and S. J. Gurr. 2013. Crop pests and pathogens move polewards in a warming world. Nature Climate Change 3:985-988, doi:10.1038/nclimate1990. 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 Côte d’Ivoire: Prevention of and care for people with HIV/AIDS. AIDS Care 18(4):356-365. Beyrer, B., S. Baral, and J. Zenilman. 2008. STDs, HIV/AIDS, and migrant populations. In STD, 4th ed., edited by K. K. Holmes, P. F. Sparling, W. E. Stamm, P. Piot, J. N. Wasserheit, L. Corey, M. S. Cohen, and D. H. Watts. New York: McGraw-Hill Medical. Beyrer, C., A. Terzian, S. Lowther, J. A. Zambrano, N. Galai, and M. K. Melchior. 2007. Civil con- flict and health information: The Democratic Republic of Congo. In Public Health and Human Rights: Evidence-Based Approaches, edited by C. Beyrer and H. F. Pizer. Baltimore, MD: The Johns Hopkins University Press. Pp. 268-285. Bharti, N., A. Tatem, M. Ferrari, R. Grais, A. Djibo, and B. Grenfell. 2011. Explaining seasonal fluctuations of measles in Niger using nighttime lights imagery. Science 334(6061):1424-1427. Blum, L. S., R. Khan, N. Nahar, and R. F. Breiman. 2009. In-depth assessment of an outbreak of Nipah encephalitis with person-to-person transmission in Bangladesh: Implications for preven- tion and control strategies. American Journal of Tropical Medicine and Hygiene 80(1):96-102. Bonds, M. H., A. P. Dobson, and D. C. Keenan. 2013. Disease ecology, biodiversity, and the latitu- dinal gradient in income. PLoS Biology 10(12):e1001456. Bradley, C. A., and S. Altizer. 2005. Parasites hinder monarch butterfly flight: Implications for disease spread in migratory hosts. Ecology Letters 8(3):290-300. Branswell, H. 2013. Pregnant woman with MERS gives birth by emergency c-section; baby well. Montreal Gazette, December 2, 2013. Brookings-Bern Project on Internal Displacement. 2008. Human rights and natural disasters. Wash- ington, DC: Brookings-Bern Project on Internal Displacement. Brower, L. P., and S. B. Malcolm. 1991. Animal migrations—Endangered phenomena. American Zoologist 31(1):265-276. Brown, J. K. M., and M. S. Hovmøller. 2002. Aerial dispersal of pathogens on the global and conti- nental scales and its impact on plant disease. Science 297(5581):537-541.

102 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS Carlton, J. 2004. Invasions in the world’s oceans: How much do we know, and what does the future hold? Paper presented at Annual Meeting, American Institute of Biological Sciences, March 16-18, 2004, Washington, DC. Carvalho, C. R., R. C. Fernandes, G. M. A. Carvalho, R. W. Barreto, and H. C. Evans. 2011. Cryp- tosexuality and the genetic diversity paradox in coffee rust, Hemileia vastatrix. PLoS ONE 6(11):e26387. Castles, S., and M. J. Miller. 2009. The Age of Migration: International population movements in the modern world, 4th ed. New York: London Guildford Press. Cauchemez, S., S. Epperson, M. Biggerstaff, D. Swerdlow, L. Finelli, and N. M. Ferguson. 2013a. Using routine surveillance data to estimate the epidemic potential of emerging zoonoses: Application to the emergence of US swine origin influenza A H3N2v virus. PLoS Medicine 10(3):e1001399. Cauchemez, S., M. D. Van Kerkhove, S. Riley, C. A. Donnelly, C. Fraser, and N. M. Ferguson. 2013b. Transmission scenarios for Middle East Respiratory Syndrome coronavirus (MERS-CoV) and how to tell them apart. European Surveillance 18(24):20503. Cauchemez, S., C. Fraser, M. D. Van Kerkhove, C. A. Donnelly, S. Riley, A. Rambaut, V. Enouf, S. van der Werf, and N. M. Ferguson. 2013c. Middle East respiratory syndrome coronavirus: Quantification of the extent of the epidemic, surveillance biases, and transmissibility. Lancet Infectious Diseases 14(1):50-56. CDC (Centers for Disease Control and Prevention). 2013a. BioMosaic: A new tool for global health security. Atlanta, GA: CDC. CDC. 2013b. Dengue. http://www.cdc.gov/dengue (accessed December 4, 2013). CDC. 2013c. Healthy places—health impact assessment (HIA). http://www.cdc.gov/healthyplaces/ hia.htm (accessed November 6, 2013). Chen, L. H., and M. E. Wilson. 2013. The globalization of healthcare: Implications of medical tourism for the infectious disease clinician. Clinical Infectious Diseases 57(12):1752-1759. Clennon, J. A., P. L. Mungai, E. M. Muchiri, C. H. King, and U. Kitron. 2006. Spatial and temporal variations in local transmission of Schistosoma haematobium in Msambweni, Kenya. American Journal of Tropical Medicine and Hygiene 75(6):1034-1041. Clennon, J. A., C. H. King, E. M. Muchiri, and U. Kitron. 2007. Hydrological modelling of snail dispersal patterns in Msambweni, Kenya, and potential resurgence of Schistosoma haematobium transmission. Parasitology 134(Pt 5):683-693. Cliff, A., and P. Haggett. 2004. Time, travel and infection. British Medical Bulletin 69(1):87-99. Coulliette, A. D., K. S. Enger, M. H. Weir, and J. B. Rose. 2012. Risk reduction assessment of water- borne Salmonella and Vibrio by a chlorine contact disinfectant point-of-use device. International Journal of Hygiene and Environmental Health 216(3):355-361. Cox, F. E. G., and F. Y. Liew. 1992. T-cell subsets and cytokines in parasitic infections. Immunology Today 13:445-448. Credit Suisse. 2012. Opportunities in an urbanizing world. Report by the Credit Suisse Emerging Market Research Institute. Zurich, Switzerland: Credit Suisse. Daszak, P., A. A. Cunningham, and A. D. Hyatt. 2000. Emerging infectious diseases of wildlife— Threats to biodiversity and human health. Science 287(5452):443-449. de Roode, J. C., and S. Altizer. 2009. Host-parasite genetic interactions and virulence-transmission relationships in natural populations of Monarch butterflies. Evolution 64(2):502-514. Deutsch, C. A., J. J. Tewksbury, R. B. Huey, K. S. Sheldon, C. K. Ghalambor, D. C. Haak, and P. R. Martin. 2008. Impacts of climate warming on terrestrial ectotherms across latitude. Proceed- ings of the National Academy of Sciences of the United States of America 105(18):6668-6672. Diamond, J. 2005. Collapse: How societies choose to fail or succeed. New York: Viking Penguin. Dobson, A. P. 2005. Virology. What links bats to emerging infectious diseases? Science 310(5748): 628-629.

WORKSHOP OVERVIEW 103 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(2006):3984-3989. Dybas, C. L. 2004. Invasive species: The search for solutions. Bioscience 54(7):615-621. Dye, C. 2008. Health and urban living. Science 319(5864):766-769. Eisenberg, J. N., W. Cevallos, K. Ponce, K. Levy, S. J. Bates, J. C. Scott, A. Hubbard, N. Vieira, P. Endara, M. Espinel, G. Trueba, L. W. Riley, and J. Trostle. 2006. Environmental change and infectious disease: How new roads affect the transmission of diarrheal pathogens in rural Ecuador. Proceedings of the National Academy of Sciences of the United States of America 103(51):19460-19465. Eisenberg, J. N., J. Goldstick, W. Cevallos, G. Trueba, K. Levy, J. Scott, B. Percha, R. Segovia, K. Ponce, A. Hubbard, C. Marrs, B. Foxman, D. L. Smith, and J. Trostle. 2011. In-roads to the spread of antibiotic resistance: Regional patterns of microbial transmission in northern coastal Ecuador. Journal of the Royal Society Interface 9(70):1029-1039. Eisenberg, J. N., J. Trostle, R. J. Sorensen, and K. F. Shields. 2012. Toward a systems approach to enteric pathogen transmission: From individual independence to community interdependence. Annual Review of Public Health 33:239-257. 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. Enger, K. S., K. L. Nelson, J. B. Rose, and J. N. Eisenberg. 2013. The joint effects of efficacy and compliance: A study of household water treatment effectiveness against childhood diarrhea. Water Resources 47(3):1181-1190. 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., A. Djibo, R. F. Grais, N. Bharti, B. T. Grenfell, and O. N. Bjornstad. 2010. Rural-urban gradient in seasonal forcing of measles transmission in Niger. Proceedings of the Royal Society, B: Biological Science 277(1695):2775-2782. Fletcher, J., and A. Wayadande. 2002. Fastidious vascular-colonizing bacteria. The Plant Health Instructor, doi:10.1094/PHI-I-2002-1218-02. Flood, J. 2010. The importance of plant health to food security. Food Security 2:215-231. Frank, D. A., and S. J. McNaughton. 1992. The ecology of plants, large mammalian herbivores and drought in Yellowstone National Park. Ecology 73:2043-2058. Fraser, C., C. A. Donnelly, S. Cauchemez, W. P. Hanage, M. D. Van Kerkhove, T. D. Hollingsworth, J. Griffin, R. F. Baggaley, H. E. Jenkins, E. J. Lyons, T. Jombart, W. R. Hinsley, N. C. Grassly, F. Balloux, A. C. Ghani, N. M. Ferguson, A. Rambaut, O. G. Pybus, H. Lopez-Gatell, C. M. Alpuche-Aranda, I. B. Chapela, E. P. Zavala, D. M. E. Guevara, F. Checchi, E. Garcia, S. Hugonnet, C. Roth, and The WHO Rapid Pandemic Assessment Collaboration. 2009. Pandemic potential of a strain of influenza A (H1N1): Early findings. Science 324(5934):1557-1561. Funk, S., T. L. Bogich, K. E. Jones, A. M. Kilpatrick, and P. Daszak. 2013. Quantifying trends in disease impact to produce a consistent and reproducible definition of an emerging infectious disease. PloS ONE 8(8):e69951. Grabow, M. L., S. N. Spak, T. Holloway, B. Stone Jr., A. C. Mednick, and J. A. Patz. 2012. Air quality and exercise-related health benefits from reduced car travel in the midwestern United States. Environmental Health Perspectives 120(1):68. Gray, S. M., and N. Banerjee. 1999. Mechanisms of arthropod transmission of plant and animal viruses. Microbiology and Molecular Biology Reviews 63(1):128-148. Gross, A., O. Holdenrieder, M. Pautasso, V. Queloz, and T. N. Sieber. 2014. Hymenoscyphus pseu- doalbidus, the causal agent of European ash dieback. Molecular Plant Pathology 15(1):5-21.

104 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS Gurley, E. S., M. S. Islam, N. Nahar, R. Sultana, M. J. Hossain, N. Homaira, S. Parveen, T. Naushin, M.-U. Alam, and A. D. Khan. 2013. Behaviour change intervention to reduce caregivers’ ex- posure to patients’ oral and nasal secretions in Bangladesh. International Journal of Infection Control 9(2): doi:10.3396/ijic.v9i2.017.13. Hahn, M. B., E. S. Gurley, J. H. Epstein, M. S. Islam, J. A. Patz, P. Daszak, and S. P. Luby. 2013. The role of landscape composition and configuration on Pteropus giganteus roosting ecology and Nipah virus spillover risk in Bangladesh. American Journal of Tropical Medicine and Hygiene 90(2):247-255. Hardin, G. 1968. The tragedy of the commons. The population problem has no technical solution; It requires a fundamental extension in morality. Science 162(3859):1243-1248. Harmon, A. 2013. A race to save the orange by altering its DNA. New York Times, http://www.nytimes. com/2013/07/28/science/a-race-to-save-the-orange-by-altering-its-dna.html?pagewanted=all (accessed July 27, 2013). Harvell, D., S. Altizer, I. M. Cattadori, L. Harrington, and E. Weil. 2009. Climate change and wildlife diseases: When does the host matter the most? Ecology 90(4):912-920. Harwood, T. D., X. Xu, M. Pautasso, M. J. Jeger, and M. W. Shaw. 2009. Epidemiological risk assess- ment using linked network and grid based modelling: Phytophthora ramorum and Phytophthora kernoviae in the UK. Ecological Modelling 220(23):3353-3361. Hawley, D. M., E. E. Osnas, A. P. Dobson, W. M. Hochachka, D. H. Ley, and A. A. Dhondt. 2013. Parallel patterns of increased virulence in a recently emerged wildlife pathogen. PLoS Biology 11(5):e1001570. Hay, S. I., K. E. Battle, D. M. Pigott, D. L. Smith, C. L. Moyes, S. Bhatt, J. S. Brownstein, N. Collier, M. F. Myers, D. B. George, and P. W. Gething. 2013a. Global mapping of infectious disease. Philosophical Transactions of the Royal Society B: Biological Sciences 368(1614):20120250. Hay, S. I., D. B. George, C. L. Moyes, and J. S. Brownstein. 2013b. Big data opportunities for global infectious disease surveillance. PLoS Medicine 10(4):e1001413. Hechinger, R. F., K. D. Lafferty, A. P. Dobson, J. H. Brown, and A. M. Kuris. 2011. A common scal- ing rule for abundance, energetics, and production of parasitic and free-living species. Science 333(6041):445-448. Hennessy, T. W., T. Ritter, R. C. Holman, D. L. Bruden, K. L. Yorita, L. Bulkow, J. E. Cheek, R. J. Singleton, and J. Smith. 2008. The relationship between in-home water service and the risk of respiratory tract, skin, and gastrointestinal tract infections among rural Alaska natives. American Journal of Public Health 98(11):2072-2078. Hochachka, W. M., A. A. Dhondt, A. Dobson, D. M. Hawley, D. H. Ley, and I. J. Lovette. 2013. Mul- tiple host transfers, but only one successful lineage in a continent-spanning emergent pathogen. Proceedings of the Royal Society B: Biological Sciences 280(1766):20131068. Holdo, R. M., A. R. Sinclair, A. P. Dobson, K. L. Metzger, B. M. Bolker, M. E. Ritchie, and R. D. Holt. 2009. A disease-mediated trophic cascade in the Serengeti and its implications for ecosys- tem C. PLoS Biology 7(9):e1000210. 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. Hsiang, S. M., M. Burke, and E. Miguel. 2013. Quantifying the influence of climate on human con- flict. Science 341(6151):1235367. Hueffer, K., A. J. Parkinson, R. Gerlach, and J. Berner. 2013. Zoonotic infections in Alaska: Disease prevalence, potential impact of climate change and recommended actions for earlier disease detection, research, prevention and control. International Journal of Circumpolar Health 72. 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. Impoinvil, D. E., C. M. Mbogo, J. Keating, and J. C. Beier. 2008. The role of unused swimming pools as a habitat for Anopheles immature stages in urban Malindi, Kenya. Journal of the American Mosquito Control Association 24(3):457-459.

WORKSHOP OVERVIEW 105 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. IOM. 2008. Global climate change and extreme weather events. Washington, DC: The National Academies Press. IOM. 2009. Global issues in water, sanitation, and health. Washington, DC: The National Academies Press. IOM. 2010. Infectious disease movement in a borderless world. Washington, DC: The National Academies Press. Jeger, M. J., M. Pautasso, O. Holdenrieder, and M. W. Shaw. 2007. Modelling disease spread and control in networks: Implications for plant sciences. New Phytologist 174(2):279-297. Jetz, W., D. S. Wilcove, and A. P. Dobson. 2007. Projected impacts of climate and land-use change on the global diversity of birds. PLoS Biology 5(6):e157. Jones, K. E., N. G. Patel, M. A. Levy, A. Storeygard, D. Balk, J. L. Gittleman, and P. Daszak. 2008. Global trends in emerging infectious diseases. Nature 451(7181):990-993. Judge Magazine. 1892. They come arm in arm. Political cartoon. September 17. Khan, K., J. Sears, V. W. Hu, J. S. Brownstein, S. Hay, D. Kossowsky, R. Eckhardt, T. Chim, I. Berry, I. Bogoch, and M. Cetron. 2013. Potential for the international spread of Middle East respiratory syndrome in association with mass gatherings in Saudi Arabia. PLoS Currents 5. Khan, S. U., E. S. Gurley, M. J. Hossain, N. Nahar, M. A. Sharker, and S. P. Luby. 2012. A random- ized controlled trial of interventions to impede date palm sap contamination by bats to prevent Nipah virus transmission in Bangladesh. PLoS ONE 7(8):e42689. Ko, A. I., M. Galvao Reis, C. M. Ribeiro Dourado, W. D. Johnson, Jr., and L. W. Riley. 1999. Urban epidemic of severe leptospirosis in Brazil. Salvador leptospirosis study group. Lancet 354(9181):820-825. Kutz, S. J., E. J. Jenkins, A. M. Veitch, J. Ducrocq, L. Polley, B. Elkin, and S. Lair. 2009. The Arctic as a model for anticipating, preventing, and mitigating climate change impacts on host–parasite interactions. Veterinary Parasitology 163(3):217-228. Kutz, S. J., S. Checkley, G. G. Verocai, M. Dumond, E. P. Hoberg, R. Peacock, J. P. Wu, K. Orsel, K. Seegers, A. L. Warren, and A. Abrams. 2013. Invasion, establishment, and range expansion of two parasitic nematodes in the Canadian Arctic. Global Change Biology 19(11):3254-3262. LaCon, G. A. C. Morrison, H. Astete, S. T. Stoddard, V. Paz-Soldan, J. P. Elder, E. S. Halsey, T. W. Scott, U. Kitron, and G. M. Vasquez-Prokopec. 2014. Shifting patterns of Aedes aegypti fine- scale spatial clustering in Iquitos, Peru. PLoS Neglected Tropical Diseases. Leopold, A. 1933. Game management. New York: Scribner’s. Lloyd-Smith, J. O., D. George, K. M. Pepin, V. E. Pitzer, J. R. Pulliam, A. P. Dobson, P. J. Hudson, and B. T. Grenfell. 2009. Epidemic dynamics at the human-animal interface. Science 326(5958): 1362-1367. Loss, S. R., G. L. Hamer, E. D. Walker, M. O. Ruiz, T. L. Goldberg, U. D. Kitron, and J. D. Brawn. 2009. Avian host community structure and prevalence of West Nile virus in Chicago, Illinois. Oecologia 159(2):415-424. Luby, S. P., and E. S. Gurley. 2012. Epidemiology of henipavirus disease in humans. Current Topics in Microbiology and Immunology 359:25-40. Mavrommati, G., M. Baustian, and E. Dreelin. 2013. Coupling socioeconomic and lake systems for sustainability: A conceptual analysis using Lake St. Clair region as a case study. AMBIO 43(3):275-287. McKinney, L., L. Nielsen, D. Collinge, I. Thomsen, J. Hansen, and E. Kjær. 2014. The ash die- back crisis: Genetic variation in resistance can prove a long-term solution. Plant Pathology 63(3):485-499. McLachlan, J. S., J. J. Hellmann, and M. W. Schwartz. 2007. A framework for debate of assisted migration in an era of climate change. Conservation and Biology 21(2):297-302.

106 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS McLellan, S. L., E. J. Hollis, M. M. Depas, M. Van Dyke, J. Harris, and C. O. Scopel. 2007. Dis- tribution and fate of Escherichia coli in Lake Michigan following contamination with urban stormwater and combined sewer overflows. Journal of Great Lakes Research 33(3):566-580. Molnár, P. K., S. J. Kutz, B. M. Hoar, and A. P. Dobson. 2013. Metabolic approaches to understanding climate change impacts on seasonal host-macroparasite dynamics. Ecology Letters 16(1):9-21. Money, N. P. 2006. The triumph of the fungi: A rotten history. New York: Oxford University Press. Pp. 102-111. Morse, S. S., J. A. 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(9857):1956-1965. Mueller, J. M., and J. J. Hellmann. 2008. An assessment of invasion risk from assisted migration. Conservation and Biology 22(3):562-567. Munz, R. 2013. Demography and migration: An outlook for the 21st century. http://www.migration policy.org/pubs/Demography-Migration-Outlook.pdf (accessed November 21, 2013). Murphy, F. A. 1998. Emerging zoonoses. Emerging Infectious Diseases 4(3):429. Murphy, F. A., and N. Nathanson. 1994. The emergence of new virus diseases: An overview. Seminars in Virology 5:87-102. Mutuku, F. M., C. H. King, P. Mungai, C. Mbogo, J. Mwangangi, E. M. Muchiri, E. D. Walker, and U. Kitron. 2011. Impact of insecticide-treated bed nets on malaria transmission indices on the south coast of Kenya. Malaria Journal 10:356. Mutuku, F. M., M. Khambira, D. Bisanzio, P. Mungai, I. Mwanzo, E. M. Muchiri, C. H. King, and U. Kitron. 2013. Physical condition and maintenance of mosquito bed nets in Kwale county, coastal Kenya. Malaria Journal 12:46. Myers, S. S., and J. A. Patz. 2009. Emerging threats to human health from global environmental change. Annual Review of Environment and Resources 34:223-252. Nagano, C. D., W. H. Sakai, S. B. Malcolm, B. J. Cockrell, J. P. Donahue, and L. P. Brower. 1993. Spring migration of monarch butterflies in California. Biology and conservation of the monarch butterfly. Edited by S. B. Malcolm and M. P. Zalucki. Los Angeles, CA: Natural History Mu- seum of Los Angeles County. Pp. 219-232. Nahar, N., U. K. Mondal, R. Sultana, M. J. Hossain, M. S. Khan, E. S. Gurley, E. Oliveras, and S. P. Luby. 2013. Piloting the use of indigenous methods to prevent Nipah virus infection by interrupting bats’ access to date palm sap in Bangladesh. Health Promotion International 28(3):378-386. National Intelligence Council. 2012. Global trends 2030: Alternative worlds. Washington, DC. Nguyen, C., B. M. Barker, S. Hoover, D. E. Nix, N. M. Ampel, J. A. Frelinger, M. J. Orbach, and J. N. Galgiani. 2013. Recent advances in our understanding of the environmental, epidemiological, immunological, and clinical dimensions of coccidioidomycosis. Clinical Microbiology Review 26(3):505-525. Oerke, E. C. 2006. Crop losses to pests. Journal of Agricultural Science 144:31-43. Olson, S. H., R. Gangnon, G. A. Silveira, and J. A. Patz. 2010. Deforestation and malaria in Mancio Lima county, Brazil. Emerging Infectious Diseases 16(7):1108-1115. Parkinson, A. J., M. G. Bruce, and T. Zulz. 2008. International circumpolar surveillance, an arctic network for the surveillance of infectious diseases. Emerging Infectious Diseases 14(1):18-24. Pascual, M., J. A. Ahumada, L. F. Chaves, X. Rodo, and M. Bouma. 2006. Malaria resurgence in the East African highlands: Temperature trends revisited. Proceedings of the National Academy of Sciences of the United States of America 103(15):5829-5834. Patz, J. A., and M. B. Hahn. 2013. Climate change and human health: A One Health approach. Current Topics in Microbiology and Immunology 366:141-171. Patz, J. A., S. J. Vavrus, C. K. Uejio, and S. L. McLellan. 2008. Climate change and waterborne disease risk in the great lakes region of the U.S. American Journal of Preventive Medicine 35(5):451-458.

WORKSHOP OVERVIEW 107 Pautasso, M. 2013. Phytophthora ramorum—A pathogen-linking network epidemiology, landscape pathology, and conservation biogeography. CAB Reviews 8(024):1-14. Pautasso, M., and M. J. Jeger. 2014. Network epidemiology and plant trade networks. AoB Plants 6:plu007. Pautasso, M., T. F. Döring, M. Garbelotto, L. Pellis, and M. Jeger. 2012. Impacts of climate change on plant diseases—opinions and trends. European Journal of Plant Pathology 133:295-313. Pautasso, M., G. Aas, V. Queloz, and O. Holdenrieder. 2013. European ash (Fraxinus excelsior) dieback—a conservation biology challenge. Biological Conservation 158:37-49. Pettersson, L., J. Boman, P. Juto, M. Evander, and C. Ahlm. 2008. Outbreak of Puumala virus infec- tion, Sweden. Emerging Infectious Diseases 14(5):808-810. Pozzi, F., D. Balk, G. Yetman, and A. Nelson. 2003. Development of a global dataset on population distribution in urban and rural areas. Paper read at Thirtieth International Symposium on Remote Sensing of Environment, November 10-14, 2003, Honolulu, Hawai’i. Preston, R. 2012. The hot zone: A terrifying true story. New York: Random House. Quammen, D. 2007. Deadly contact. National Geographic 212:78-105. Queloz, V., C. Grünig, R. Berndt, T. Kowalski, T. Sieber, and O. Holdenrieder. 2011. Cryptic specia- tion in Hymenoscyphus albidus. Forest Pathology 41(2):133-142. Rain, D. 1999. Eaters of the dry season: Circular labor migration in the West African Sahel. Boulder, CO: Westview. Reis, R. B., G. S. Ribeiro, R. D. Felzemburgh, F. S. Santana, S. Mohr, A. X. Melendez, A. Queiroz, A. C. Santos, R. R. Ravines, W. S. Tassinari, M. S. Carvalho, M. G. Reis, and A. I. Ko. 2008. Impact of environment and social gradient on Leptospira infection in urban slums. PLoS Ne- glected Tropical Diseases 2(4):e228. Reisen, W. K., Y. Fang, and V. M. Martinez. 2006. Effects of temperature on the transmission of West Nile virus by Culex tarsalis (diptera: Culicidae). Journal of Medical Entomology 43(2):309-317. Revich, B., N. Tokarevich, and A. J. Parkinson. 2012. Climate change and zoonotic infections in the Russian arctic. International Journal of Circumpolar Health 71:18792. Revkin, A. 2011. Confronting the “anthropocene.” In Dot Earth. New York: New York Times. Riley, L. W., A. I. Ko, A. Unger, and M. G. Reis. 2007. Slum health: Diseases of neglected popula- tions. BMC International Health and Human Rights 7:2. Rockström, J., W. Steffen, K. Noone, A. Persson, F. S. Chapin, 3rd, E. F. Lambin, T. M. Lenton, M. Scheffer, C. Folke, H. J. Schellnhuber, B. Nykvist, C. A. de Wit, T. Hughes, S. van der Leeuw, H. Rodhe, S. Sorlin, P. K. Snyder, R. Costanza, U. Svedin, M. Falkenmark, L. Karlberg, R. W. Corell, V. J. Fabry, J. Hansen, B. Walker, D. Liverman, K. Richardson, P. Crutzen, and J. A. Foley. 2009a. A safe operating space for humanity. Nature 461(7263):472-475. Rockström, J., W. Steffen, K. Noone, Å. Persson, F. S. Chapin III, E. Lambin, T. M. Lenton, M. Scheffer, C. Folke, and H. J. Schellnhuber. 2009b. Planetary boundaries: Exploring the safe operating space for humanity. Ecology and Society 14(2):32. Rosenzweig, C., A. Iglesias, X. Yang, P. R. Epstein, and E. Chivian. 2001. Climate change and extreme weather events; implications for food production, plant diseases, and pests. Global Change & Human Health 2(2):90-104. Russell, C. A., J. M. Fonville, A. E. Brown, D. F. Burke, D. L. Smith, S. L. James, S. Herfst, S. van Boheemen, M. Linster, E. J. Schrauwen, L. Katzelnick, A. Mosterin, T. Kuiken, E. Maher, G. Neumann, A. D. Osterhaus, Y. Kawaoka, R. A. Fouchier, and D. J. Smith. 2012. The potential for respiratory droplet-transmissible A/H5N1 influenza virus to evolve in a mammalian host. Science 336(6088):1541-1547. Santini, A., L. Ghelardini, C. d. Pace, M.-L. Desprez-Loustau, P. Capretti, A. Chandelier, T. Cech, D. Chira, S. Diamandis, and T. Gaitniekis. 2013. Biogeographical patterns and determinants of invasion by forest pathogens in Europe. New Phytologist 197(1):238-250. Smith, K. M., S. J. Anthony, W. M. Switzer, J. H. Epstein, T. Seimon, H. Jia, M. D. Sanchez, T. T. Huynh, G. G. Galland, and S. E. Shapiro. 2012. Zoonotic viruses associated with illegally im- ported wildlife products. PLoS ONE 7(1):e29505.

108 GLOBAL CHANGE AND INFECTIOUS DISEASE DYNAMICS Snieszko, S. 1974. The effects of environmental stress on outbreaks of infectious diseases of fishes. Journal of Fish Biology 6(2):197-208. Stauffer, W. M., M. Weinberg, R. D. Newman, L. M. Causer, M. J. Hamel, L. Slutsker, and M. S. Cetron. 2008. Pre-departure and post-arrival management of P. falciparum malaria in refugees relocating from sub-Saharan Africa to the United States. American Journal of Tropical Medicine and Hygiene 79(2):141-146. Steffen, W. L., A. Sanderson, P. Tyson, J. Jäger, P. A. Matson, B. Moore III, F. Oldfield, K. Richardson, H. Schellnuber, and B. L. Turner II. 2004. Global change and the earth system: A planet under pressure. Berlin: Springer. Steffen, W., J. Grinevald, P. Crutzen, and J. McNeill. 2011. The anthropocene: Conceptual and histori- cal perspectives. Philosophical Transactions A: Mathematics, Physics, Enginering, and Science 369(1938):842-867. Steinmann, P., J. Keiser, R. Bos, M. Tanner, and J. Utzinger. 2006. Schistosomiasis and water re- sources development: Systematic review, meta-analysis, and estimates of people at risk. Lancet Infectious Diseases 6(7):411-425. Strange, R. N., and P. R. Scott. 2005. Plant disease: A threat to global food security. Annual Review of Phytopathology 43:83-116. Studholme, D. J., E. Kemen, D. MacLean, S. Schornack, V. Aritua, R. Thwaites, M. Grant, J. Smith, and J. D. Jones. 2010. Genome-wide sequencing data reveals virulence factors implicated in banana Xanthomonas wilt. FEMS Microbiology Letters 310(2):182-192. Sultana, R., N. Nahar, N. A. Rimi, S. Azad, M. S. Islam, E. S. Gurley, and S. P. Luby. 2012. Backyard poultry raising in Bangladesh: A valued resource for the villagers and a setting for zoonotic transmission of avian influenza. A qualitative study. Rural Remote Health 12:1927. Swanson, S. J., C. R. Phares, B. Mamo, K. E. Smith, M. S. Cetron, and W. M. Stauffer. 2012. Alben- dazole therapy and enteric parasites in United States-bound refugees. New England Journal of Medicine 366(16):1498-1507. Tamerius, J. D., and A. C. Comrie. 2011. Coccidioidomycosis incidence in Arizona predicted by seasonal precipitation. PLoS ONE 6(6):e21009. Tan, L. V., H. R. van Doorn, H. D. Nghia, T. T. Chau, T. P. Tu le, M. de Vries, M. Canuti, M. Deijs, M. F. Jebbink, S. Baker, J. E. Bryant, N. T. Tham, B. K. NT, M. F. Boni, T. Q. Loi, T. Phuong le, J. T. Verhoeven, M. Crusat, R. E. Jeeninga, C. Schultsz, N. V. Chau, T. T. Hien, L. van der Hoek, J. Farrar, and M. D. de Jong. 2013. Identification of a new cyclovirus in cerebrospinal fluid of patients with acute central nervous system infections. mBio 4(3):e00231-00213. Tatem, A. J., S. Adamo, N. Bharti, C. R. Burgert, M. Castro, A. Dorelien, G. Fink, C. Linard, M. John, L. Montana, M. R. Montgomery, A. Nelson, A. M. Noor, D. Pindolia, G. Yetman, and D. Balk. 2012. Mapping populations at risk: Improving spatial demographic data for infectious disease modeling and metric derivation. Population and Health Metrics 10(1):8. Tatum, L. 1971. The southern corn leaf blight epidemic. Science 171(3976):1113-1116. Tokarevich, N. K., A. A. Tronin, O. V. Blinova, R. V. Buzinov, V. P. Boltenkov, E. D. Yurasova, and J. Nurse. 2011. The impact of climate change on the expansion of Ixodes persulcatus habitat and the incidence of tick-borne encephalitis in the north of European Russia. Global Health Action 4:8448. 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. Ullstrup, A. 1972. The impacts of the southern corn leaf blight epidemics of 1970-1971. Annual Review of Phytopathology 10(1):37-50. United Nations Environmental Programme. 2005. Millennium Ecosystem Assessment. http://www. unep.org/maweb/en/Index.aspx (accessed November 8, 2013). United Nations Human Settlements Programme. 2003. Global report on human settlements 2003: The challenge of slums. Geneva: UN.

WORKSHOP OVERVIEW 109 USDA/APHIS (U.S. Department of Agriculture/Animal and Plant Health Inspection Service). 2014. Citrus greening background. http://www.aphis.usda.gov/wps/portal/aphis/ourfocus/ planthealth?1dmy&urile=wcm%3apath%3a%2Faphis_content_library%2Fsa_our_focus%2 Fsa_plant_health%2Fsa_domestic_pests_and_diseases%2Fsa_pests_and_diseases%2Fsa_ plant_disease%2Fsa_citrus%2Fct_background (accessed July 22, 2014). 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. Vitt, P., K. Havens, A. T. Kramer, D. Sollenberger, and E. Yates. 2010. Assisted migration of plants: Changes in latitudes, changes in attitudes. Biological Conservation 143:18-27. Vittor, A. Y., R. H. Gilman, J. Tielsch, G. Glass, T. Shields, W. S. Lozano, V. Pinedo-Cancino, and J. A. Patz. 2006. The effect of deforestation on the human-biting rate of Anopheles darlingi, the primary vector of falciparum malaria in the Peruvian Amazon. American Journal of Tropical Medicine and Hygiene 74(1):3-11. Vittor, A. Y., W. Pan, R. H. Gilman, J. Tielsch, G. Glass, T. Shields, W. Sanchez-Lozano, V. V. Pinedo, E. Salas-Cobos, S. Flores, and J. A. Patz. 2009. Linking deforestation to malaria in the Amazon: Characterization of the breeding habitat of the principal malaria vector, Anopheles darlingi. American Journal of Tropical Medicine and Hygiene 81(1):5-12. Vurro, M., B. Bonciani, and G. Vannacci. 2010. Emerging infectious diseases of crop plants in developing countries: Impact on agriculture and socio-economic consequences. Food Security 2(2):113-132. Walker, B., S. Barrett, S. Polasky, V. Galaz, C. Folke, G. Engstrom, F. Ackerman, K. Arrow, S. Carpenter, K. Chopra, G. Daily, P. Ehrlich, T. Hughes, N. Kautsky, S. Levin, K. G. Maler, J. Shogren, J. Vincent, T. Xepapadeas, and A. de Zeeuw. 2009. Environment. Looming global- scale failures and missing institutions. Science 325(5946):1345-1346. Walsh, J., D. Molyneux, and M. Birley. 1993. Deforestation: Effects on vector-borne disease. Para- sitology 106(S1):S55-S75. Werres, S., R. Marwitz, A. W. De Cock, P. J. Bonants, M. De Weerdt, K. Themann, E. Ilieva, and R. P. Baayen. 2001. Phytophthora ramorum sp. nov., a new pathogen on Rhododendron and Viburnum. Mycological Research 105(10):1155-1165. Wheeler, T., and J. von Braun. 2013. Climate change impacts on global food security. Science 341(6145):508-513. Whitman, D. 2000. The sickening sewer crisis. U.S. News & World Report (June 4). http://www. usnews.com/usnews/news/articles/000612/archive_016392.htm (accessed December 18, 2013). WHO (World Health Organization). 2005. Ecosystems and human well-being: Health synthesis: A report of the Millennium Ecosystem Assessment. Geneva: World Health Organization. WHO. 2013a. Dengue and severe dengue. http://www.who.int/mediacentre/factsheets/fs117/en (ac- cessed December 4, 2013). WHO. 2013b. Schistosomiasis. http://www.who.int/mediacentre/factsheets/fs115/en/index.html (ac- cessed December 4, 2013). Wilcove, D. S., and M. Wikelski. 2008. Going, going, gone: Is animal migration disappearing? PLoS Biology 6(7):e188. Wolfe, N. D., C. P. Dunavan, and J. Diamond. 2007. Origins of major human infectious diseases. Nature 447(7142):279-283. Zalasiewicz, J. A. C. 2008. Are we now living in the anthropocene? GSA Today 18(2):4-8. Zelner, J. L., J. Trostle, J. E. Goldstick, W. Cevallos, J. S. House, and J. N. Eisenberg. 2012. Social connectedness can inhibit disease transmission: Social organization, cohesion, village context and infection risk in rural Ecuador. American Journal of Public Health 102(12):2233-2239.

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The twentieth century witnessed an era of unprecedented, large-scale, anthropogenic changes to the natural environment. Understanding how environmental factors directly and indirectly affect the emergence and spread of infectious disease has assumed global importance for life on this planet. While the causal links between environmental change and disease emergence are complex, progress in understanding these links, as well as how their impacts may vary across space and time, will require transdisciplinary, transnational, collaborative research. This research may draw upon the expertise, tools, and approaches from a variety of disciplines. Such research may inform improvements in global readiness and capacity for surveillance, detection, and response to emerging microbial threats to plant, animal, and human health.

The Influence of Global Environmental Change on Infectious Disease Dynamics is the summary of a workshop hosted by the Institute of Medicine Forum on Microbial Threats in September 2013 to explore the scientific and policy implications of the impacts of global environmental change on infectious disease emergence, establishment, and spread. This report examines the observed and potential influence of environmental factors, acting both individually and in synergy, on infectious disease dynamics. The report considers a range of approaches to improve global readiness and capacity for surveillance, detection, and response to emerging microbial threats to plant, animal, and human health in the face of ongoing global environmental change.

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