Speaker
Description
Dengue, a mosquito-borne disease, is rapidly expanding its global distribution, emerging in previously naïve populations, while also causing more intense and more frequent outbreaks in endemic areas. There is an increasingly urgent need for innovative approaches for studying drivers of dengue emergence and spread, forecasting outbreaks, and determining how environmental suitability for dengue will change in the face of short- and long-term changes in climate patterns. In this talk, I will discuss recent work harnessing new approaches for modeling dengue emergence and spread in both the city of Córdoba, Argentina, where dengue first appeared in 2009, and the Dominican Republic, where dengue has long been endemic. Among the models I will discuss are machine learning and neural network models developed to study relationships between dengue cases climate with the goal of improving forecasts of dengue transmission. I will discuss our findings from these models in the context of developing early warning systems for predictions for future dengue outbreaks and working with local public health and vector control stakeholders to harness model results to improve dengue surveillance and control.
