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
收藏
页码：E64 / E70
页数：7
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