Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk
This model provides a tool for German governments and public health officials to evaluate COVID-19 transmission patterns and the impact of policies like travel restrictions, offering an incremental improvement to existing time risk prediction models.
This paper developed a machine learning-assisted spatio-temporal epidemiological model (CA-SUIR) to predict COVID-19 risk at the Germany county level. It projected multi-level COVID-19 prevalence across 412 German counties, including t-day-ahead forecasts and risk assessments for travel restrictions, predicting that effective policies could reduce Christmas fatalities from 34.5 thousand to below 21 thousand.
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.