Department of Natural Sciences, Southern University at New Orleans, 6801 Press Drive, New Orleans, LA, 70126, USA.
Department of Mathematical and Physical Sciences, Concordia University of Edmonton, 7128 Ada Boulevard, Edmonton, AB, T5B 4E4, Canada.
Sci Rep. 2022 Sep 20;12(1):15688. doi: 10.1038/s41598-022-20276-7.
An Adaptive Susceptible-Infected-Removed-Vaccinated (A-SIRV) epidemic model with time-dependent transmission and removal rates is constructed for investigating the dynamics of an epidemic disease such as the COVID-19 pandemic. Real data of COVID-19 spread is used for the simultaneous identification of the unknown time-dependent rates and functions participating in the A-SIRV system. The inverse problem is formulated and solved numerically using the Method of Variational Imbedding, which reduces the inverse problem to a problem for minimizing a properly constructed functional for obtaining the sought values. To illustrate and validate the proposed solution approach, the present study used available public data for several countries with diverse population and vaccination dynamics-the World, Israel, The United States of America, and Japan.
构建了一个具有时变传播和移除率的自适应易感染-感染-移除-接种（A-SIRV）传染病模型，用于研究传染病（如 COVID-19 大流行）的动力学。使用 COVID-19 传播的实际数据同时识别参与 A-SIRV 系统的未知时变率和函数。使用变分嵌入法对反问题进行了数值求解，该方法将反问题转化为一个适当构造的函数的最小化问题，以获得所需的值。为了说明和验证所提出的解决方案方法，本研究使用了具有不同人口和疫苗接种动态的几个国家的公开数据-世界、以色列、美国和日本。
