Discovering dynamic models of COVID-19 transmission

被引：6

作者：

Liang, Jinwen ^{[1
]}

Zhang, Xueliang ^{[2
]}

Wang, Kai ^{[2
]}

Tang, Manlai ^{[3
]}

Tian, Maozai ^{[1
,2
]}

机构：

[1] Renmin Univ China, Ctr Appl Stat, Sch Stat, Beijing 100872, Peoples R China

[2] Xinjiang Med Univ, Dept Med Engn & Technol, Urumqi, Peoples R China

[3] Brunel Univ, Coll Engn Design & Phys Sci, Dept Math, London, England

来源：

TRANSBOUNDARY AND EMERGING DISEASES | 2022年 / 69卷 / 04期

基金：

中国国家自然科学基金;

关键词：

COVID-19; global transmission; maximin aggregation; sparse identification of nonlinear dynamics (SINDy); VARIABLE SELECTION;

D O I：

10.1111/tbed.14263

中图分类号：

R51 [传染病];

学科分类号：

100401 ;

摘要：

Existing models about the dynamics of COVID-19 transmission often assume the mechanism of virus transmission and the form of the differential equations. These assumptions are hard to verify. Due to the biases of country-level data, it is inaccurate to construct the global dynamic of COVID-19. This research aims to provide a robust data-driven global model of the transmission dynamics. We apply sparse identification of nonlinear dynamics (SINDy) to model the dynamics of COVID-19 global transmission. One advantage is that we can discover the nonlinear dynamics from data without assumptions in the form of the governing equations. To overcome the problem of biased country-level data on the number of reported cases, we propose a robust global model of the dynamics by using maximin aggregation. Real data analysis shows the efficiency of our model.

引用

页码：E64 / E70

页数：7

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