Estimation and Distributed Eradication of SIR Epidemics on Networks

被引:1
作者
Zhang, Ciyuan [1 ]
Leung, Humphrey [1 ]
Butler, Brooks A. [1 ]
Pare, Philip E. [1 ]
机构
[1] Purdue Univ, Elmore Family Sch Elect & Comp Engn, W Lafayette, IN 47907 USA
来源
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS | 2024年 / 11卷 / 02期
关键词
Epidemics; Testing; Estimation; Delays; Stochastic processes; COVID-19; Data models; Distributed algorithms; epidemics; networked control systems; parameter estimation; state estimation; DYNAMICS; SPREAD;
D O I
10.1109/TCNS.2023.3306491
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This work examines a discrete-time-networked susceptible-infected-recovered (SIR) epidemic model, where the infection, graph, and recovery parameters may be time-varying. We propose a stochastic framework to estimate the system states from observed testing data and provide an analytic expression for the error of the estimation algorithm. We validate some of our assumptions for the stochastic framework with real COVID-19 testing data. We identify the system parameters with the system states from our estimation algorithm. Employing the estimated system states, we provide a novel eradication strategy that guarantees at least exponential convergence to the set of healthy states. Also, the results are illustrated via simulations over Northern Indiana, USA.
引用
收藏
页码:756 / 768
页数:13
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