such natural disasters will undoubtedly also change. Improved risk-based hazard mitigation, based on improved understanding of the public health effects of natural hazards under existing and future climatic regimes, is an important research priority. Such collaborative research should include:
Determining processes and techniques to integrate the wealth of information provided by the diverse earth science, engineering, emergency response, and public health disciplines so that more sophisticated scenarios can be developed to ultimately form the basis for improved natural hazard mitigation strategies.
Human disturbances of natural terrestrial systems—for example, by activities as diverse as underground resource extraction, waste disposal, or landcover and habitat change—are creating new types of health risks. Research to understand and document the health risks arising from disturbance of terrestrial systems is a critical requirement for alleviating existing health threats and preventing new exposures. Such collaborative research should include:
Analysis of the effect of geomorphic and hydrological landsurface alteration on disease ecology, including emergence/resurgence and transmission of disease.
Determining the health effects associated with water quality changes induced by novel technologies and other strategies currently being implemented, or planned, for extending groundwater and surface water supplies to meet increasing demands for water by a growing world population.
Geospatial information—geological maps for earth scientists and epidemiological data for public health professionals—is an essential integrative tool that is fundamental to the activities of both communities. The application of modern complex spatial analytical techniques has the potential to provide a rigorous base for integrated earth science and public health research by facilitating the analysis of spatial relationships between public health effects and natural earth materials and processes. Research activity should be focused on the development of high-resolution, spatially and temporally accurate models for predicting disease distribution that incorporate layers of geological, geographic, and socioeconomic data.