  As the COVID-19 pandemic continues to ravage the world, it is of critical
significance to provide a timely risk prediction of the COVID-19 in
multi-level. To implement it and evaluate the public health policies, we
develop a framework with machine learning assisted to extract epidemic dynamics
from the infection data, in which contains a county-level spatiotemporal
epidemiological model that combines a spatial Cellular Automaton (CA) with a
temporal Susceptible-Undiagnosed-Infected-Removed (SUIR) model. Compared with
the existing time risk prediction models, the proposed CA-SUIR model shows the
multi-level risk of the county to the government and coronavirus transmission
patterns under different policies. This new toolbox is first utilized to the
projection of the multi-level COVID-19 prevalence over 412 Landkreis (counties)
in Germany, including t-day-ahead risk forecast and the risk assessment to the
travel restriction policy. As a practical illustration, we predict the
situation at Christmas where the worst fatalities are 34.5 thousand, effective
policies could contain it to below 21 thousand. Such intervenable evaluation
system could help decide on economic restarting and public health policies
making in pandemic.
