Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jun 7, 2020
Open Peer Review Period: Jun 7, 2020 - Jul 13, 2020
Date Accepted: Aug 6, 2020
Date Submitted to PubMed: Aug 8, 2020
(closed for review but you can still tweet)
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.
Objective:
We propose a deep learning-enhanced compartmental model to simulate and forecast epidemic dynamics, and we test the model’s utilities with the US COVID-19 data. The customized compartmental model is of a multivariate time series construct, and two deep learning techniques are employed to estimate the time-varying transmission parameters among compartments. The transmission parameters is then fed to the compartment model to simulate and forecast the development of the US COVID-19 epidemics.
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. Clinical Trial: NA
Citation
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