NSF Award
2322213
Emerging infectious diseases may be from new disease in a population or have existed previously but are rapidly increasing in incidence or geographic range. Many new diseases are associated with spillover from a wildlife source into human populations. Anthropogenic (human caused) climatic- and land-use changes are often thought to be key drivers of disease emergence. This is especially true for arthropod vectors that are particularly sensitive to small changes in climatic and microclimatic conditions. Therefore, the prevention of such outbreaks necessitates advanced predictive models. Most current distribution models are based on correlations rather than on causal mechanistic relationships. Physiology-based mechanistic models hold a promise to overcome the limitations of correlational models by capturing specific biophysical signals linking life-history traits (e.g., larval development, mortality, growth) with environmental variables. Using the sand fly Phlebotomus papatasi as a case study, this project will develop a mechanistic model and will field-test its predictions, with clear public health implications in terms of reducing human exposure for the pathogens these sand flies transmit, including Old-World cutaneous leishmaniasis and pappataci fever being the most significant ones. In addition, this project will advance the theoretical and analytical frameworks of the emerging field of disease biogeography applied to a vector-borne disease in a changing world. This project will provide training for students and professional development opportunities for faculty. <br/><br/>This project will use comprehensive physiological lab experimentation to build mechanistic models of fitness to forecast vector species responses to coarse- and fine-scale environmental change. The specific aims are to: (1) determine the physiological parameters that make disease vectors more sensitive to global climate change and influence their geographical spread and transmission potential (coarse scale) and (2) determine the role of microclimatic variation due to land use modification on the metapopulation dynamics of vector-borne disease systems (fine scale). Specifically, this project will integrate mechanistic and correlative ecological niche models to reconstruct the fitness of the sand fly and the distributional ecology of the transmission. Mathematical and machine learning models will be calibrated using laboratory (physiological experimentation) and field-collected (occurrences) data coupled with environmental variables. The project will study in detail the structure, size, and position of the ecological niche of P. papatasi by manipulating critical environmental variables manifested at coarse (e.g., temperature, relative humidity, photoperiod) and fine (e.g., soil temperature, moisture, organic matter) spatial scales. Field sampling will be conducted in Israel to test the predictions of the model.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
