eMB: Forecasting mosquito vector populations to improve situational awareness

This project will address the growing public health threat posed by disease-carrying mosquitoes in the United States. Mosquito species capable of transmitting debilitating viruses like dengue, Zika, and chikungunya are expanding their geographic range, putting millions of US residents at risk. Currently, public health efforts to control these mosquitoes are often reactive, responding only after a case has been identified. This project will shift the paradigm from reaction to prevention by developing an early-warning system that forecasts surges in mosquito populations, much like weather forecasts predict storms. By anticipating when and where mosquito numbers will be high, public health authorities can implement mosquito control measures more effectively, helping to prevent disease outbreaks before they start. Moreover, this project will provide valuable training opportunities for the next generation of scientists and public health professionals. The overarching goal of this project is to develop and validate a suite of modeling tools and ensembling approaches to generate 1- to 4-week ahead forecasts of the relative abundance of Aedes aegypti and Aedes albopictus. Forecasts will be produced at multiple spatial scales to align with the operational needs of public health and mosquito control agencies. The project will develop a multi-model framework that integrates different methodologies, including mechanistic compartmental models of the mosquito life cycle, a semi-mechanistic model based on the real-time estimation of the population reproduction number, and machine learning approaches. Furthermore, the outputs of individual models will be combined using several ensembling techniques and a signal decomposition method to improve forecast performance and reliability. All models will be trained and validated using a comprehensive mosquito surveillance dataset spanning from 2010-2024 across five locations in the United States. The project will develop an open-source package to run the forecasting tools proposed and validated within this project. 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.