Dynamics and Prediction of the COVID-19 Epidemics in the US:a Compartmental Model with Deep Learning Enhancement

DENG, Q.

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 estimate 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 (R_0) will become less than one around June 18-19, 2020, and that the US Infected population will peak on June 17-18, 2020 at around 1.36 million individual cases. The model also predicts that the number of accumulative confirmed cases will cross the 2 million mark around June 11, 2020. It also projects that the infection transmission parameter will drop to virtually zero as early as July 8, 2020, implying that the total number of confirmed cases will likely become stabilized around that time frame (predicted at 2.23-2.24 million).