基于机器学习时空流行病学模型评估德国县级COVID-19风险

Lingxiao Wang, Tian Xu, Till Hannes Stoecker, H. Stoecker, Yin Jiang, K. Zhou
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引用次数: 16

摘要

在新冠肺炎疫情持续肆虐全球的背景下,及时提供多层次的新冠肺炎风险预测具有重要意义。为了实施和评估公共卫生政策,我们开发了一个框架,利用机器学习辅助从感染数据中提取流行病动态,其中包含一个县级时空流行病学模型,该模型结合了空间元胞自动机(CA)和时间易感-未诊断-感染-移除(SUIR)模型。与现有的时间风险预测模型相比,本文提出的CA-SUIR模型显示了不同政策下县域对政府的多层次风险和冠状病毒传播模式。首先将新工具箱用于德国412个县的多层次疫情预测,包括提前t日风险预测和旅行限制政策风险评估。作为一个实际的例子,我们预测圣诞节的情况,最严重的死亡人数是3.45万人,有效的政策可以将其控制在2.1万人以下。这种可干预的评估体系可以帮助决定大流行时期的经济重启和公共卫生政策制定。
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Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk
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.
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