Understanding the impact of non-linearity in the SIS model

Tobias Friedrich, Andreas Göbel, Nicolas Klodt, Martin S. Krejca and Marcus Pappik

Physica A: Statistical Mechanics and its Applications, 2024, vol. 656, issue C

Abstract: The SIS model is a classic model from epidemiology that formalizes a variety of diffusion processes on networks, such as biological infections and information dissemination. In this model, vertices are either infected or susceptible to an infection. Infected vertices infect their neighbors independently at a rate λ>0, and each infected vertex becomes susceptible at a rate of 1. Overall, these dynamics imply that each susceptible vertex with exactly m infected neighbors becomes infected at rate λm, that is, linear in m. However, it has been observed that various processes exhibit a non-linear scaling of the infection rate with respect to m. For these kinds of processes, no fully rigorous guarantees exist so far.

Keywords: Information diffusion; Epidemic models; Non-linear infection; Survival time (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:656:y:2024:i:c:s0378437124007180

DOI: 10.1016/j.physa.2024.130209

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