论文标题
机器学习时空流行病学模型，以评估德国 - 县级别的covid-19风险
Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk
论文作者
论文摘要
随着19日19日的大流行不断破坏世界，在多层次中及时对COVID-19的风险预测至关重要。为了实施并评估公共卫生政策，我们开发了一个框架，其中有助于从感染数据中提取流行病动力学，其中包含县级时空流行病学模型，该模型结合了空间细胞自动机（CA）与暂时敏感诊断的临时诊断受感染的感染感染的（SUIR）模型。与现有的时间风险预测模型相比，拟议的CA-Suir模型显示了该县在不同政策下对政府和冠状病毒的传播模式的多层次风险。该新的工具箱首先用于预测德国412个Landkreis（县）的多级Covid-19，包括T-Day-Ad-Ad-Aff Adav Advect Trisk预测以及对旅行限制政策的风险评估。作为一个实用的例证，我们预测圣诞节的情况是最严重的死亡人数为34.5万，有效的政策可能包含在21,000以下。这种可行的评估系统可以帮助决定大流行的经济重新开始和公共卫生政策。
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.
