Mills et al. data. Nevertheless, some of these data sets might be paired with long-term weather data to provide insight into climate effects (Pascual et al. 2002; Stenseth et al. 2006). Track data on the geographic distribu- tion, severity, and frequency of outbreaks of wildlife diseases and VBZDs in humans. This is another type of longitudinal monitor- ing program, but the list of pathogens and hosts would be broader and the data col- lected more restricted (e.g., geographic coor- dinates, dates, number of cases). These data can be used to track geographic and tempo- ral trends in VBZD incidence, identify vul- nerable populations, and test predictions based on forecasting models. Several existing databases provide useful formats (e.g., U.S. Geological Service’s Global Wildlife Disease News Map; National Biological Information Infrastructure 2010), the CDC’s ArboNet, and the Global Early Warning System for Major Animal Diseases including Zoonoses [World Health Organization (WHO) 2010]. Integration of such formats with the monitor- ing, data gathering, and data dissemination capacity of CDC and WHO networks would provide a powerful tool, making geographic and incidence data quickly available to the broad community of public health profession- als, researchers, and modelers. Conduct experimental laboratory and field studies of effects of climate change on hosts and vectors and their abilities to maintain and transmit pathogens. Laboratory studies and manipulative field studies (Post et al. 2008) can be used to test mechanisms of climate change effects on hosts, vectors, and patho- gens or to provide hypotheses for field test- ing. Because specific environmental factors cannot be isolated and controlled in the field, laboratory studies will be the best way to test the effects of specific changes in temperature, humidity, or physiological stress on host, vec- tor, or pathogen populations. For example, stress-related immunosuppression has been suggested as an explanation for increased rates of transmission and for viral recrudescence including increases in pathogen load in hanta- virus hosts observed in field studies (Botten et al. 2003; Kuenzi et al. 2005). Laboratory experiments provided preliminary evidence in support of this hypothesis in the case of Seoul hantavirus infection in Norway rats (Rattus norvegicus) (Klein et al. 2004). Additional con- trolled laboratory experiments will be nec- essary to confirm this hypothesis for other VBZD pathogens and to help quantify the environmental and social factors (temperature, crowding, aggression, breeding, etc.) associated with transmission and recrudescence. Neither field nor laboratory studies alone can provide complete answers to these ques- tions. Physiological patterns observed in the laboratory must be tested under natural field 1512 conditions and the physiological mechanisms for patterns observed in the field must be tested in the laboratory. Use data from laboratory and field studies, epidemiological studies, and remote sensing to develop predictive models of changes in zoonotic disease risk and the projected distribution and abundance of major hosts and vectors. The util- ity of such predictive models has been proven. Long-term direct monitoring of host popula- tion density and prevalence of hantavirus infec- tion in North American deer mice provided early warning of elevated risk for HPS in the southwestern United States (CDC 1998, 1999; Yates et al. 2002). Models have used rainfall and temperature data to identify high-risk areas for plague and HPS in the same geographic area (Eisen et al. 2007; Enscore et al. 2002; Parmenter et al. 1999). Remote sensors have demonstrated climate-associated changes in vegetation or other environmental parameters that foretold increased risk for Rift Valley fever (Anyamba et al. 2001) or HPS (Glass et al. 2002). Ecological niche modeling has been used to provide evidence for climate change- mediated range shifts for plague and tularemia and to provide predictions of continued future poleward expansion (Nakazawa et al. 2007). Ecological niche models also used climatic data and other environmental datasets to predict the geographic distribution of the hosts and vec- tors for Chagas disease and the filovirus hem- orrhagic fevers (Peterson et al. 2002, 2004). Another model predicted the northward spread of the Lyme disease and anaplasmosis vector I. scapularis under proposed climate change scenarios (Brownstein et al. 2005). Recent sur- veillance has confirmed that expansion (Ogden et al. 2010). Continued data collection from long-term field studies combined with labora- tory data providing measured physiological tol- erances for specific hosts and vectors and effects of stressors on pathogen transmission can be used to develop increasingly accurate models of range expansions and effects of increased population density on heat stress and pathogen transmission. Although predictive models may accurately identify the changes in suitable physical habitat for host and vector species, it is important to remember that the realized niche (actual range of conditions occupied by a species) is always smaller than the fundamental niche (potential range in the absence of predators, competi- tors, and other limiters); the many nonclimatic confounders discussed above (barriers to dis- persal, predation, competition) will need to be included in accurate models (Lafferty 2009). An Ecosystem Approach Over the last few decades it has become clear that effective disease prevention must con- sider the whole environment in which disease occurs. The maintenance of healthy people requires the maintenance of healthy ecosystems. Understanding ecosystem changes in relation to climate change will require not only a clear understanding of future changes in the physical environment but also a precise understanding of the physiological, ecological, and evolution- ary responses to these changes by pathogens, vectors, and vertebrate hosts. Because of the complexity of pathogen–vector–host inter- actions, the multiple interactions involved, and the environment in which these interactions are occurring, studies must be multidisciplinary, long term, spatially diverse, and sufficiently replicated to provide conclusions that are reli- able and generalizable. No single institution has the expertise and resources to establish multiple, intensive, long-term field monitoring programs and appropriate laboratory experi- ments, collect and interpret satellite images, and apply these data to the development of mathematical simulation models and forecast- ing tools. Collaborative partnerships among government and nongovernmental organiza- tions and universities will have the best chance of achieving these goals. In order for broad collaborations to be most effective, mecha- nisms must be developed for effective commu- nication, data sharing, and research integration among institutions and disciplines. Application to Public Health An important product of these studies would be an enhanced ability to initiate mitigation strate- gies, promulgate early warning information, and target response capabilities. Predictions based on long-term monitoring programs and forecasting models should be quickly dissemi- nated to local, state, and international pub- lic health partners. In consultation with those partners, intervention strategies should be developed to mitigate the impact of disease on human populations. In response to predic- tions of increased risk of HPS in the south- western United States in 1998 and 1999, the CDC, in partnership with several southwestern States, developed and promulgated prevention messages via television, radio, pamphlets and posters. Despite large increases in local rodent host populations, numbers of human cases were lower than in the previous HPS outbreak in 1993/1994 (CDC 1998, 1999). Prediction and promulgation efforts can be enhanced through integration with several existing U.S. government initiatives directed at climate change, including a) Climate Mapper, which makes the results of climate change models accessible to a broad user community; b) the Malaria Early Warning System, which recently examined rainfall pat- terns in sub-Saharan Africa using meteoro- logical station data, rainfall estimated from satellite images, and malaria incidence; and c) the Global Earth Observation System of Systems, which is an ambitious undertaking ovlume 118 | number 11 | November 2010 • Environmental Health Perspectives