ArticleTransboundary and emerging diseases2022
Discovering dynamic models of COVID-19 transmission.
Article in Transboundary and emerging diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Modeling Moose-Wolf interactions in Isle Royale National Park using sparseidentification of nonlinear dynamics.Scientific reports · 2025Article
- Sparse regression for low-dimensional time-dynamic varying coefficient models with application to air quality data.Journal of applied statistics · 2023Article
- Discovering dynamic models of COVID-19 transmission.Transboundary and emerging diseases · 2022Article
- SINDy-SA framework: enhancing nonlinear system identification with sensitivity analysis.Nonlinear dynamics · 2022Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
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