Threshold Dynamics of Stochastic SIS Epidemic Models with Logistic Recruitment Rate

This study explores an SIS epidemic model that integrates logistic growth and a saturated incidence rate among susceptible individuals. The primary objective is to examine the long-term behavior of the stochastic system. For this purpose, a key parameter, [Formula: see text], is introduced to differentiate the system dynamics into two cases. In scenarios where [Formula: see text], the model predicts the eventual extinction of the disease. In contrast, for [Formula: see text], we show an ergodic stationary distribution. Numerical simulations validate our theoretical conditions for extinction, persistence, and the existence of a stationary distribution.

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Publication Details

Journal
New Mathematics and Natural Computation
Published
2025-04-24
DOI
https://doi.org/10.1142/s1793005727500074
Citations
1
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
1.37
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Threshold Dynamics of Stochastic SIS Epidemic Models with Logistic Recruitment Rate

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article

Threshold Dynamics of Stochastic SIS Epidemic Models with Logistic Recruitment Rate

Abdeladim Nait Brahim, Aadil Lahrouz, Adel Settati, Bilal Harchaoui, Soulaimane Aznague, Khalid El Bakkioui
article en
1 citations

Abstract

This study explores an SIS epidemic model that integrates logistic growth and a saturated incidence rate among susceptible individuals. The primary objective is to examine the long-term behavior of the stochastic system. For this purpose, a key parameter, [Formula: see text], is introduced to differentiate the system dynamics into two cases. In scenarios where [Formula: see text], the model predicts the eventual extinction of the disease. In contrast, for [Formula: see text], we show an ergodic stationary distribution. Numerical simulations validate our theoretical conditions for extinction, persistence, and the existence of a stationary distribution.

New Mathematics and Natural Computation
Abdelmalek Essaâdi University (MA)
Openalex Percentile: Top 23%
COVID-19 epidemiological studies
1.37
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