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As the COVID-19 pandemic continues to ravage the world, it is of criticalsignificance to provide a timely risk prediction of the COVID-19 inmulti-level. To implement it and evaluate the public health policies, wedevelop a framework with machine learning assisted to extract epidemic dynamicsfrom the infection data, in which contains a county-level spatiotemporalepidemiological model that combines a spatial Cellular Automaton (CA) with atemporal Susceptible-Undiagnosed-Infected-Removed (SUIR) model. Compared withthe existing time risk prediction models, the proposed CA-SUIR model shows themulti-level risk of the county to the government and coronavirus transmissionpatterns under different policies. This new toolbox is first utilized to theprojection of the multi-level COVID-19 prevalence over 412 Landkreis (counties)in Germany, including t-day-ahead risk forecast and the risk assessment to thetravel restriction policy. As a practical illustration, we predict thesituation at Christmas where the worst fatalities are 34.5 thousand, effectivepolicies could contain it to below 21 thousand. Such intervenable evaluationsystem could help decide on economic restarting and public health policiesmaking in pandemic.

Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk