2022/04/01 by Ciyuan Zhang, Zhang, Ciyuan, Sebin Gracy +5 · 1 citation
Mathematics · Medicine · Physics and Astronomy · #COVID-19 epidemiological studies #Complex Network Analysis Techniques #FOS: Electrical engineering #Mathematical and Theoretical Epidemiology and Ecology Models #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2204.00708
openalex publication_date 2022/04/01 · openalex created_date 2022/10/08 · openalex updated_date 2026/07/28
This paper proposes a novel discrete-time multi-virus SIR (susceptible-infected-recovered) model that captures the spread of competing SIR epidemics over a population network. First, we provide a sufficient condition for the infection level of all the viruses over the networked model to converge to zero in exponential time. Second, we propose an observation model which captures the summation of all the viruses' infection levels in each node, which represents the individuals who are infected by different viruses but share similar symptoms. We present a sufficient condition for the model to be locally observable. We propose a Luenberger observer for the system state estimation and show via simulations that the estimation error of the Luenberger observer converges to zero before the viruses die out.