| Efforts to contain emerging vector-borne diseases have had limited success largely because of critical shortages in manpower and resources required to develop effective strategies for managing disease risk. There exists a need for improved methods that can help create evidence-based decisions concerning targeting of disease control activities with existing resources. Public health agencies can greatly benefit from disease risk maps since an accurate understanding of the spatial distribution of both the pathogens and vectors is integral to vector-borne disease prevention strategies. Advances have been made in the application of remote sensing, geographic information systems and spatial statistics to vector-borne disease mapping. My research applied these new technologies to define spatial risk for two emerging vector-borne diseases at two different time scales. First, I developed spatial models that can be used in the short-term management of recently introduced West Nile virus. At the national scale, I constructed a county-level West Nile virus risk map. This risk model serves as an early warning system for human cases by correcting for variability in case reports and quantifying the predictive ability of non-human surveillance. At the local scale, I modeled the association between environment and mosquito vector habitat to develop a human West Nile virus risk map for the New York City area. The model could be implemented as a decision support system for seasonal mosquito control. Second, I examined how the identification of environmental risk factors can be used for long-term planning of control and prevention efforts for Lyme disease. At the national scale, I used climatic data to construct a spatially predictive logistic model for the probability of established tick vector populations in the US. Climate change scenarios were then used to extrapolate the habitat suitability model in time and produce long-range forecasts of the future distribution of the tick vector. At the local scale, I analyzed the impact of landscape fragmentation on Lyme disease risk, revealing that landscape mosaic plays a significant role in defining local heterogeneity in disease risk. Overall, this research demonstrates the value of spatial models for the improvement of control strategies and prevention efforts for vector-borne diseases. |
