causing massive mortality. Among these, the authors describe four especially destructive epidemics that appear to have killed between 20 and 90 percent of the entire population, leading to societal collapse: the epidemics of 1003-1011, 1545-1548, 1576-1578, and 1736-1737. The authors also compare circumstances in contemporary Mexico with those associated with apparent past episodes of infectious disease emergence, when increasing human connectivity (roads then, globalization today), and the emergence of new pathogens transmitted by aerosols (smallpox and measles in the past, severe acute respiratory syndrome [SARS] and influenza today), proved to be a very dangerous combination.

Emerging infectious diseases of wildlife arise when the delicate balance of host, pathogen, and environment is disturbed. Therefore, these events represent a critical target for infectious disease monitoring efforts of all sorts, including those that seek to track the influence of climate change, according to speaker William Karesh of the Wildlife Conservation Society. In the chapter’s second paper, he and coauthors provide several examples of studies that illustrate the direct and indirect influences of climate on infectious diseases of wildlife. They make the case that such interactions can serve as the basis for monitoring the ecological effects of climate change on emergent diseases that threaten not only wildlife, but also livestock and humans, because wild animals often serve as reservoirs for microbes that may cause pathogenic diseases in humans; these microbes are not necessarily pathogenic in their animal hosts. Moreover, the authors note, wild animals offer a number of advantages for disease monitoring programs: their comparatively short generation times reflect environmental changes more quickly than do humans; the great variety of wild species offers an equally wide range of life histories for the observation of disease dynamics; and they provide sensitive sentinels for changes in the environments to which they are specifically adapted.

As discussed by Chretien and coauthors in Chapter 2 and as first described in Linthicum et al. (1999), efforts to predict risk for Rift Valley fever (RVF) demonstrated that trends in environmental variables detected from satellite imagery can be compared with epidemiological data to reveal relationships between climate and infectious disease transmission and geographic distribution. In his workshop presentation, speaker Compton Tucker of the National Aeronautics and Space Administration (NASA)—who coauthored both of the previously mentioned papers—described how remote sensing data are collected and analyzed, and presented two additional examples of the use of this tool in examining links between climate and infectious disease.

The first involved a search for significant environmental factors common to sporadic outbreaks of Ebola hemorrhagic fever (EHF). Ebola virus also affects nonhuman primates, which have been implicated as the source of several—but not all—human outbreaks through contact with the meat of infected apes (Pinzon et al., 2004). Tucker and colleagues chose to investigate the possibility that Ebola outbreaks occur independently of human cases, in nonhuman primates, and to

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