文献类型：期刊文献

英文题名：A Networked Meta-Population Epidemic Model with Population Flow and Its Application to the Prediction of the COVID-19 Pandemic

作者：Xue, Dong[1];Liu, Naichao[1];Chen, Xinyi[1];Liu, Fangzhou[2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Harbin Inst Technol, Sch Astronaut, Res Inst Intelligent Control & Syst, Harbin 150001, Peoples R China

年份：2024

卷号：26

期号：8

外文期刊名：ENTROPY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001305117800001)】；

基金：This work was supported in part by the National Natural Science Foundation of China (Grant No. 62173147 and 62373123). Furthermore, this work was sponsored by the Shanghai Pujiang Program under grant No. 20PJ1403000.

语种：英文

外文关键词：epidemics model; discrete-time networked SAIR model; population flow; joint parameter-topology estimation

摘要：This article addresses the crucial issues of how asymptomatic individuals and population movements influence the spread of epidemics. Specifically, a discrete-time networked Susceptible-Asymptomatic-Infected-Recovered (SAIR) model that integrates population flow is introduced to investigate the dynamics of epidemic transmission among individuals. In contrast to existing data-driven system identification approaches that identify the network structure or system parameters separately, a joint estimation framework is developed in this study. The joint framework incorporates historical measurements and enables the simultaneous estimation of transmission topology and epidemic factors. The use of the joint estimation scheme reduces the estimation error. The stability of equilibria and convergence behaviors of proposed dynamics are then analyzed. Furthermore, the sensitivity of the proposed model to population movements is evaluated in terms of the basic reproduction number. This article also rigorously investigates the effectiveness of non-pharmaceutical interventions via distributively controlling population flow in curbing virus transmission. It is found that the population flow control strategy reduces the number of infections during the epidemic.