2021/02/24 by Ciyuan Zhang, Humphrey C. H. Leung, Zhang, Ciyuan +5
Mathematics · Physics and Astronomy · Psychology · #COVID-19 epidemiological studies #Complex Network Analysis Techniques #FOS: Electrical engineering #Mental Health Research Topics #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2102.12549
openalex publication_date 2021/02/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This work examines the discrete-time networked SIR (susceptible-infected-recovered) epidemic model, where the infection and recovery parameters may be time-varying. We provide a sufficient condition for the SIR model to converge to the set of healthy states exponentially. 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. Employing the estimated and the true system states, we provide two novel eradication strategies that guarantee at least exponential convergence to the set of healthy states. We illustrate the results via simulations over northern Indiana, USA.