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In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : 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.

OBJECTIVE : We apply deep learning techniques as a lower data dependency alternative to estimate transmission parameters of a customized compartmental model, for the purpose of simulating the dynamics of the US COVID-19 epidemics and projecting its further development.

METHODS : We construct a compartmental model. We develop a multistep deep learning methodology to estimate the model's transmission parameters. We then feed the estimated transmission parameters to the model to predict the development of the US COVID-19 epidemics for 35 and 42 days. Epidemics are considered suppressed when the basic reproduction number (R_0) becomes less than one.

RESULTS : The deep learning-enhanced compartmental model predicts that R_0 will become less than one around August 17 to 19, 2020, at which point the epidemics will effectively start to die out, and that the US "Infected" population will peak round August 16 to 18, 2020, between 3,228,574 and 3,308,911 individual cases. The models also predict that the number of accumulative confirmed cases will cross the 5 million mark around August 7, 2020.

CONCLUSIONS : Current compartmental models require stochastic parameterization to estimate the transmission parameters. These models' effectiveness depends upon detailed statistics on transmission characteristics. As an alternative, deep learning techniques are effective in estimating these stochastic parameters with greatly reduced dependency on data particularity.



Deng Q I