Dynamics and Prediction of the COVID-19 Epidemics in the U.S. – A Compartmental Model with Deep Learning Enhancement

Abstract

Compartmental models dominate epidemic modeling. Estimations of transmission parameters between compartments are typically done through stochastic parameterization processes that depend upon detailed statistics on transmission characteristics, which are economically and resource-wide expensive to collect. We apply deep learning techniques as a lower data dependency alternative to estimating transmission parameters of a customized compartmental model, for the purposes of projecting further development of the US COVID-19 epidemics. The deep learning-enhanced compartment model predicts that the basic reproduction rate (R0 ) will become less than one around June 22–27, 2020, and that the infection transmission parameter will drop to virtually zero around July 14, 2020, implying that the total number of confirmed cases will likely become stabilized around that timeframe (projected at 2·4 million). Funding Statement: None. Declaration of Interests: Authors declare no competing interests.