发现 COVID-19 传播的动态模型。
Discovering dynamic models of COVID-19 transmission.
机构信息
Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing, China.
Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China.
出版信息
Transbound Emerg Dis. 2022 Jul;69(4):e64-e70. doi: 10.1111/tbed.14263. Epub 2021 Aug 11.
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.
现有的关于 COVID-19 传播动力学的模型通常假设病毒传播的机制和微分方程的形式。这些假设很难验证。由于国家层面数据的偏差，构建 COVID-19 的全球动态是不准确的。本研究旨在提供一个稳健的数据驱动的 COVID-19 传播动力学的全球模型。我们应用稀疏非线性动力学识别（SINDy）来模拟 COVID-19 的全球传播动力学。一个优点是，我们可以从数据中发现非线性动力学，而无需在控制方程的形式上做出假设。为了克服报告病例数量的国家层面数据的偏差问题，我们提出了一种使用极大极小聚集的稳健的全球动力学模型。实际数据分析表明了我们模型的效率。
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