Abstract

The main aim of the work is to present a general class of two time scales discrete-time epidemic models. In the proposed framework the disease dynamics is considered to act on a slower time scale than a second different process that could represent movements between spatial locations, changes of individual activities or behaviors, or others.To include a sufficiently general disease model, we first build up from first principles a discrete-time susceptible–exposed–infectious–recovered–susceptible (SEIRS) model and characterize the eradication or endemicity of the disease with the help of its basic reproduction number mathcal{R}_{0}.Then, we propose a general full model that includes sequentially the two processes at different time scales and proceed to its analysis through a reduced model. The basic reproduction number overline{mathcal{R}}_{0} of the reduced system gives a good approximation of mathcal{R}_{0} of the full model since it serves at analyzing its asymptotic behavior.As an illustration of the proposed general framework, it is shown that there exist conditions under which a locally endemic disease, considering isolated patches in a metapopulation, can be eradicated globally by establishing the appropriate movements between patches.

Highlights

  • Infectious diseases such as SARS-CoV-2, AIDS, Ebola, or COVID-19 are becoming a part of usual life

  • 4.2 Results To illustrate the use of the developed framework, we explore the possibility of eradication of the disease through appropriate movements when it is endemic in every isolated patch

  • 5 Discussion Contrary to what happens in continuous time [5, 16, 17, 24, 28], in the literature there are almost no references on discrete-time epidemic models with two time scales

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Summary

Introduction

Infectious diseases such as SARS-CoV-2, AIDS, Ebola, or COVID-19 are becoming a part of usual life. They can catastrophically spread and cause a significant number of deaths. The only way to try to compare the effectiveness of these methods is to formulate appropriate mathematical models that help us on making predictions [4]. Mathematical models in epidemiology have a long history of more than two centuries. Most of these models are formulated in continuous time, possibly because of the wealth of analytical tools available for their study. At least in the last twenty years, time-discrete mathematical epidemics models have been used with a significant and increasing frequency

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