Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions

Abstract

Measurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19. In this study, the authors develop a spatiotemporal machine learning model to predict county level new cases in the US using a variety of predictive features.