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FIGURE SA-14 Arctic shipping shortcuts.

SOURCE: Reprinted with permission from Foreign Affairs (Borgerson, 2008). Copyright 2008 by the Council on Foreign Relations, Inc.

FIGURE SA-14 Arctic shipping shortcuts.

SOURCE: Reprinted with permission from Foreign Affairs (Borgerson, 2008). Copyright 2008 by the Council on Foreign Relations, Inc.

ernmost communities, Parkinson noted, but it is also likely to expose the region's inhabitants and ecosystems to invasive species of all kinds, including potentially pathogenic microbes and their vectors. In the face of these challenges, Parkinson echoed the suggestions of many other speakers at this workshop to enhance the surveillance and monitoring of climate-sensitive infectious diseases on a global basis and to establish international networks to share such information.

Plague Dynamics

Throughout human history the various forms of plague, caused by the bacterium Yersinia pestis and transmitted by fleas among a wide range of hosts, have caused both endemic and epidemic disease. "Plague is a highly variable disease," explained speaker Nils Christian Stenseth of the University of Oslo, Norway. "It is a complex system with complex temporal, seasonal, interannual dynamics." In his contribution to Chapter 2, Stenseth describes these intricate relationships and his approach to modeling plague dynamics based on long-term monitoring of pathogen prevalence in central Asian rodent populations. These studies have led him to conclude that although relatively few cases of plague are currently reported, the disease poses a significant and imminent threat to human populations due, in part, to the influence of climate change.

Using longitudinal data collected over 50 years in Kazakhstan, a focal region for plague where cases are regularly reported, Stenseth and colleagues determined


that Y. pestis prevalence increases dramatically in its primary host, the great gerbil (Rhombomys opimus), during warmer springs and wetter summers (Stenseth et al., 2006). Rodent populations also tend to increase under these conditions and, along with them, the possibility that plague will be transmitted to humans. These conditions apparently existed in central Asia during the onset of the Black Death in the fourteenth century, as well as in the years preceding a mid-nineteenth-century plague pandemic. As Earth's climate warms, warmer springs and wet summers are expected to become increasingly common in the region, and also in North America.

Historical, Scientific, and Technological Approaches

Several workshop presentations described methods used to identify, measure, analyze, and predict the direct and indirect effects of climate change on infectious disease emergence. Each of the topics discussed below represents part of an interdisciplinary approach that, participants agreed, must continue to expand in order to pursue a common goal. As Colwell observed, understanding complex interactions between biological and physical environments paves the way for the development of predictive models and, thereby, for early and efficient responses to infectious disease threats.

Analysis of Historical Data

Historical analysis provides a perspective on climate and infectious disease far more sweeping than can be obtained from scientific monitoring. When speaker Rodolfo Acuña-Soto of the Universidad Nacional Autónoma de México and coworkers chronicled major epidemics (based on historical accounts) and climate conditions (as reflected in the width of tree rings) over the last millennium in the Valley of Mexico, they revealed an association between severe and prolonged droughts, catastrophic epidemics, and societal collapse (see Acuña-Soto et al. in Chapter 3).

Amid one such "megadrought" during the sixteenth century, hemorrhagic fever appears to have killed an estimated 80 percent of the indigenous people of the Valley of México; survivors mated primarily with Spanish colonists, repopu-lating the region with predominantly Mestizo offspring. Droughts also accompanied each of a series of 22 typhus epidemics that occurred between 1655 and 1915.

Drought is still a major problem for Mexico and is expected to continue to burden the country in the future, Acuña-Soto noted. In addition, contemporary increases in human connectivity and infectious disease emergence resemble the circumstances of past regional epidemics, such as those that followed the Spanish conquest of the region more than 400 years ago.


Wildlife Monitoring

Emerging infectious diseases of wildlife, such as those described in Box SA-2, arise from a disturbance in a delicate balance of host, pathogen, and environment. For this reason—and also because wild animals often serve as reservoir species for zoonotic threats to human health—they 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 (see Karesh in Chapter 3).

Wild animals also offer a number of advantages for disease monitoring programs, Karesh explained. 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 from which researchers can choose to model disease scenarios at different generational rates; and they provide sensitive sentinels for changes in the environments to which they are specifically adapted. Karesh observed that fish, bird, and marine mammal populations in South America declined dramatically during the El Niño event that occurred there in 1991-1992. In the case of Ebola hemorrhagic fever, Karesh observed that gorilla die-offs have preceded human outbreaks of Ebola virus by several weeks.

Highly pathogenic avian influenza can move between wild birds, domesticated poultry, and people, resulting in an increased risk of disease in cohabitated populations. Although wild birds cannot predict efficient human-to-human transfer of H5N1 avian influenza, the Global Avian Influenza Network for Surveillance (GAINS) gathers data in 23 developing countries—largely through the efforts of volunteers—on wild bird diseases; disseminates information to governments, international organizations, the private sector, and the general public; and helps to develop appropriate responses before outbreaks occur (GAINS, 2008).

Long-term monitoring of infectious diseases in wildlife also made possible the previously described model of plague dynamics and climate by Stenseth and coworkers (Stenseth et al., 2006). Under the Soviet regime, scientists began surveying rodent populations in Kazakhstan and testing them for plague in 1949; the practice continued through 1995, providing a wealth of data for statistical analysis. Such long-term studies are crucial to the prevention of human epidemics of plague and other zoonotic diseases that cannot be eradicated because they persist in a vast range of wildlife species, Stenseth said.

Remote Sensing

Satellite imagery is used to measure environmental variables over time, including land cover (a proxy for rainfall) and surface air temperature and humidity. Trends in these conditions, when compared with epidemiological data, reveal relationships between climate and infectious disease transmission and geographic


distribution—for example, the previously discussed link between vegetation density and risk for epidemic RVF in humans (Linthicum et al., 1999).

In his workshop presentation, speaker Compton Tucker of the National Aeronautics and Space Administration (NASA) described the collection and analysis of remote sensing data and presented two examples of its use in examining links between climate and infectious disease (see Tucker in Chapter 3). The first involved a search for significant environmental factors common to sporadic outbreaks of Ebola hemorrhagic fever. Analyzing satellite data collected continuously since 1981, he and coworkers found an apparent "trigger event" that occurred prior to each outbreak: a period of drought, followed by a sudden return to very wet conditions (Pinzon et al., 2004). Today, satellite data from eastern equatorial Africa are screened routinely for this weather pattern; the results guide targeted testing for Ebola virus in local primates, which may provide an early warning of future outbreaks in humans.

Tucker and colleagues have also used satellite imagery to investigate an outbreak of RVF in Yemen, which seemed suspicious because of its proximity in location and time to a terrorist attack on a U.S. Navy ship, the USS Cole. Records of a satellite-derived index of photosynthetic capacity in local vegetation (another rainfall indicator) suggested that significant precipitation had fallen in the region prior to the outbreak, so the researchers concluded that it probably arose naturally.

Predictive Models

Several models for predicting the onset or prevalence of infectious diseases based on climatic indicators have been discussed in previous sections of this chapter (see also contributions to Chapter 2 by Chretien, Colwell, and Stenseth). Remote sensing of sea surface temperature and height, along with vegetation indices, are used to anticipate ENSO effects on a variety of diseases (Anyamba et al., 2006), to identify areas at risk for RVF outbreaks (Linthicum et al., 1999), and to provide early warning of epidemic cholera in Bangladesh (Gil et al., 2004; Speelmon et al., 2000). Stenseth suggested that statistical models capable of predicting past plague epidemics in central Asia (from tree-ring-derived measures of temperature and humidity) (Stenseth et al., 2006) could anticipate the influence of current climate conditions on population density and disease prevalence in rodent reservoirs of plague.

Climate-driven predictive models of mosquito-borne encephalitis transmission are also used by the State of California to estimate disease risk and inform public health interventions. Speaker William Reisen of the University of California, Davis, described the ongoing development of these models and their use in targeting surveillance to support integrated vector management (see Reisen and Barker in Chapter 3). The goal of these efforts, Reisen said, is to limit local population sizes of mosquitoes in order to prevent these vectors from amplifying


West Nile and related viruses to levels that put humans at risk for infection and to do so as efficiently as possible.

Reisen and coworkers found that although regional mosquito abundance was positively correlated with antecedent (January-February) temperatures and with precipitation levels, and inversely correlated with summer temperatures, climate measures alone explained only a fraction of the variability in mosquito populations. They discovered that climate variation produced very different responses (in both mosquito population size and viral amplification) in different environments. "One model doesn't fit all," Reisen concluded. "These relationships are very complex and have to be developed for specific biomes." Thus, the California Mosquito-borne Encephalitis Virus Surveillance and Response Plan currently incorporates measures of climate variation; however, the researchers are refining their models with the goal of using climate forecasts to provide earlier warning of transmission risk.


As they explored the various routes by which climate variability and extreme weather events influence infectious disease emergence, workshop participants identified a range of challenges inherent to research on this topic. Many of these considerations were also discussed in Under the Weather (NRC, 2001), as noted in the Executive Summary of that report:

There are many substantial research challenges associated with studying linkages among climate, ecosystems, and infectious diseases. For instance, climate-related impacts must be understood in the context of numerous other forces that drive infectious disease dynamics, such as rapid evolution of drug- and pesticide-resistant pathogens, swift global dissemination of microbes and vectors through expanding transportation networks, and deterioration of public health programs in some regions. Also, the ecology and transmission dynamics of different infectious diseases vary widely from one context to the next, thus making it difficult to draw general conclusions or compare results from individual studies. Finally, the highly interdisciplinary nature of this issue necessitates sustained collaboration among disciplines that normally share few underlying scientific principles and research methods, and among scientists that may have little understanding of the capabilities and limitations of each other's fields.

Consistent with these prior findings, workshop participants noted the following challenges intrinsic to the tasks of detecting, predicting, and mitigating infectious disease threats associated with climate change:

• Complexity of disease transmission patterns

• Global inequalities

• Varying space and time scales


• Establishing causation

• Lack of scientific certainty versus need for action

Complexity of Disease Transmission Patterns

Studies of influenza and dengue fever, as well as theoretical models, reveal that oscillations in disease incidence may occur even in the absence of seasonal changes in person-to-person transmissibility (see Burke in Chapter 1). Depending on parameters such as human birth rate, disease duration, and length of immunity, different epidemic viruses can display different intrinsic epidemic oscillatory frequencies. Burke observed that if such intrinsic epidemic frequency oscillations coincide with (resonate with) the annual seasonal changes in environmental conditions, then even very small annual environmentally-driven changes in trans-missibility may, under some circumstances, drive very large seasonal changes in disease incidence (Dushoff et al., 2004). The impact of a given increment of change in climate upon the future transmission of a given disease cannot be determined without understanding the particular relationship between two oscillating patterns—the intrinsic incidence oscillation and the seasonally-driven oscillation. Resonance can raise the magnitude of seasonally epidemic disease. "Every effort should be made to isolate and thereby understand these component subsystems if we are to explain and predict epidemic patterns," Burke concluded.

Global Inequalities

The effects of climate change are likely to be far greater in the tropics, where the majority of the world's poorest people live, than in the wealthier temperate zones. As Haines observed, in the areas where links between climate and disease may best be studied, people are least able to investigate them. Similarly, a predictive model that highlights regions at higher risk for infectious disease emergence (see Figure SA-15) suggests that such "hot spots" are concentrated in equatorial developing countries, where opportunities for monitoring and research are severely limited (Jones et al., 2008). The model's developers conclude that "[t]he global effort for [emerging infectious disease] surveillance and investigation is poorly allocated, with the majority of our scientific resources focused on places [such as North America, Europe, and Australia] from where the next important emerging pathogen is least likely to originate" (Jones et al., 2008). They argue, instead, that the resources for emerging infectious disease surveillance should target hot spots in tropical Africa, Latin America, and Asia, and populations at greatest risk for infection, in order to detect outbreaks of emergent diseases at the earliest possible stage.

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