Abstract
Developing methods to model and forecast the transmission of COVID-19 using tools from epidemiology, statistics, and machine learning.
Project Details
This project develops methods to model and forecast the transmission of COVID-19 using tools from epidemiology, statistics, and machine learning. Pandemic forecasting is needed to provide actionable information for outbreak response; however, modeling a novel emerging disease using real-time surveillance data from around the globe is an an unprecedented challenge. We develop computationally efficient Bayesian models for forecasting and understanding COVID-19. Our “MechBayes” (Mechanistic Bayesian) forecast model is submitted weekly to the COVID-19 Forecast Hub and the CDC, is featured on the FiveThirtyEight website, and has consistently ranked as one of the top forecasting models in different evaluations.
Funding: NIH R35 supplement: $314,023.
More information: preprint, code
