Dynamics and Development of the COVID-19 Epidemics in the US – a Compartmental Model with Deep Learning Enhancement
Abstract
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. We apply deep learning techniques as a lower data dependency alternative to estimate transmission parameters of a customized compartmental model, for the purposes 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 June 19 to July 3, 2020, at which point the epidemics will effectively start to die out, and that the US “Infected” population will peak round June 18 to July 2, 2020 between 1·34 million and 1·41 million individual cases. The models also predict that the number of accumulative confirmed cases will cross the 2 million mark around June 10 to 11, 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.
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