This $130,064 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences (CFDA 47.049) program supports collaborative research to develop new statistical algorithms for analyzing spatiotemporal data on vector-borne disease transmission, using dengue virus in Rio de Janeiro as a case study. The project aims to address methodological challenges in modeling complex, nonlinear, and partially-observed spatial-temporal systems, with the goal of improving disease surveillance and control. Researchers at New York University will lead this effort, which includes developing mathematical models of dengue transmission dynamics and applying the new statistical inference algorithms to analyze spatiotemporal dengue case data from Rio de Janeiro, along with data on human mobility, temperature, and rainfall. The results are expected to advance capabilities for modeling the spatiotemporal spread of infectious diseases and informing public health decision-making.
Generated 11/19/24, 5:40 AM