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Discovering dynamic models of COVID-19 transmission.
Liang, Jinwen; Zhang, Xueliang; Wang, Kai; Tang, Manlai; Tian, Maozai.
  • Liang J; Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing, China.
  • Zhang X; Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China.
  • Wang K; Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China.
  • Tang M; Department of Mathematics, College of Engineering, Design & Physical Sciences, Brunel University, London, UK.
  • Tian M; Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing, China.
Transbound Emerg Dis ; 69(4): e64-e70, 2022 Jul.
Article in English | MEDLINE | ID: covidwho-1329028
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Limits: Animals Language: English Journal: Transbound Emerg Dis Journal subject: Veterinary Medicine Year: 2022 Document Type: Article Affiliation country: Tbed.14263

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Limits: Animals Language: English Journal: Transbound Emerg Dis Journal subject: Veterinary Medicine Year: 2022 Document Type: Article Affiliation country: Tbed.14263