The reproduction number of COVID-19 and its correlation with public health interventions

被引:0
|
作者
Linka, Kevin [1 ]
Peirlinck, Mathias [1 ]
Kuhl, Ellen [1 ]
机构
[1] Stanford Univ, Dept Mech Engn, Stanford, CA 94305 USA
基金
美国国家卫生研究院;
关键词
COVID-19; Epidemiology; SEIR model; Reproduction number; Machine learning;
D O I
10.1007/s00466-020-01880-8
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Throughout the past six months, no number has dominated the public media more persistently than the reproduction number of COVID-19. This powerful but simple concept is widely used by the public media, scientists, and political decision makers to explain and justify political strategies to control the COVID-19 pandemic. Here we explore the effectiveness of political interventions using the reproduction number of COVID-19 across Europe. We propose a dynamic SEIR epidemiology model with a time-varying reproduction number, which we identify using machine learning. During the early outbreak, the basic reproduction number was 4.22 +/- 1.69, with maximum values of 6.33 and 5.88 in Germany and the Netherlands. By May 10, 2020, it dropped to 0.67 +/- 0.18, with minimum values of 0.37 and 0.28 in Hungary and Slovakia. We found a strong correlation between passenger air travel, driving, walking, and transit mobility and the effective reproduction number with a time delay of 17.24 +/- 2.00 days. Our new dynamic SEIR model provides the flexibility to simulate various outbreak control and exit strategies to inform political decision making and identify safe solutions in the benefit of global health.
引用
收藏
页码:1035 / 1050
页数:16
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