Modeling the Omicron Dynamics and Development in China: with a Deep Learning Enhanced Compartmental Model
DENG, Q.
Show abstract
The mainstream compartmental models require stochastic parameterization to estimate the transmission parameters between compartments, which depends upon detailed statistics on epidemiological characteristics that are economically and resource-wide expensive to collect. As an alternative, deep learning techniques are effective in estimating these stochastic parameters with greatly reduced dependency on data particularity. We apply deep learning to estimate transmission parameters of a customized compartmental model, then feed the estimated transmission parameters to the compartmental model to predict the development of the Omicron epidemics in China for 28 days. The average levels of predication accuracy of the model are 98% and 92% for number of infections and deaths, respectively.
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