[HTML][HTML] Asymptotic profiles of steady states for a diffusive SIS epidemic model with mass action infection mechanism

Y Wu, X Zou - Journal of Differential Equations, 2016 - Elsevier
Journal of Differential Equations, 2016Elsevier
Mass action and standard incidence are two major infection mechanisms in modelling
spread of infectious diseases. Spatial heterogeneity plays an important role in spread of
infectious diseases, and hence, motivates and advocates diffusive models for disease
dynamics. By analyzing a diffusive SIS model with the standard incidence infection
mechanism, some recent works [2],[12] have investigated the asymptotical profiles of the
endemic steady state for large and small diffusion rates, and the results show that controlling …
Abstract
Mass action and standard incidence are two major infection mechanisms in modelling spread of infectious diseases. Spatial heterogeneity plays an important role in spread of infectious diseases, and hence, motivates and advocates diffusive models for disease dynamics. By analyzing a diffusive SIS model with the standard incidence infection mechanism, some recent works [2], [12] have investigated the asymptotical profiles of the endemic steady state for large and small diffusion rates, and the results show that controlling the diffusion rate of the susceptible individuals can help eradicate the infection, while controlling the diffusion rate of the infectious individuals cannot. This paper aims to reveal the difference between the two infection mechanisms in a spatially heterogeneous environment. To this end, we consider a diffusive SIS model of the same structure but with the mass action infection adopted, and explore the asymptotic profiles of the endemic steady state for small and large diffusion rates. It turns out that the new model poses some new challenges due to the nonlocal term in the equilibrium problem and the unboundedness of the nonlinear term. Our results on this new model reveal some fundamental differences between the two transmission mechanisms in such spatial models, which may provide some implications on disease modelling and controls.
Elsevier
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