2020/07/26 by Ashish R. Hota, Hota, Ashish R., Kavish Gupta +1
Medicine · Physics and Astronomy · Psychology · #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Mathematical and Theoretical Epidemiology and Ecology Models #Mental Health Research Topics #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2008.00826
openalex publication_date 2020/07/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study the class of SIS epidemics on temporal networks and propose a new\nactivity-driven and adaptive epidemic model that captures the impact of\nasymptomatic and infectious individuals in the network. In the proposed model,\nreferred to as the A-SIYS epidemic, each node can be in three possible states:\nsusceptible, infected without symptoms or asymptomatic and infected with\nsymptoms or symptomatic. Both asymptomatic and symptomatic individuals are\ninfectious. We show that the proposed A-SIYS epidemic captures several\nwell-established epidemic models as special cases and obtain sufficient\nconditions under which the disease gets eradicated by resorting to mean-field\napproximations.\n In addition, we highlight a potential inaccuracy in the derivation of the\nupper bound on the decay ratio in the activity-driven adaptive SIS (A-SIS)\nmodel in (Ogura et. al., 2019) and present a more general version of their\nresult. We numerically illustrate the evolution of the fraction of infected\nnodes in the A-SIS epidemic model and show that the bound in (Ogura et. al.,\n2019) often fails to capture the behavior of the epidemic in contrast with our\nresults.\n