Chiang Mai Journal of Science

Print ISSN: 0125-2526 | eISSN : 2465-3845

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Analyzing the Dynamics of A Periodic SIQR Epidemic Model with Mild and Severe Infectious

Ruitong Zhang and Lianying Cao
* Author for corresponding; e-mail address: zcly200153@nefu.edu.cn
Volume: Vol.52 No.4 (July 2025)
Research Article
DOI: https://doi.org/10.12982/CMJS.2025.037
Received: 21 October 2024, Revised: 27 January 2025, Accepted: 19 Febuary 2025, Published: 1 July 2025

Citation:  Zhang R. and Cao L., Analyzing the dynamics of a periodic SIQR epidemic model with mild and severe infectious. Chiang Mai Journal of Science, 2025; 52(4): e2025037. DOI 10.12982/CMJS.2025.037.

Abstract

     Based on the seasonal characteristics of infectious diseases, we present a non-autonomous compartmental model that considers mild and severe infection populations. We adopt different isolation methods for these two infected populations. The transmission period is divided into off-season J1 and peak season J2 . This study aims to provide the theoretical basis for controlling such infectious diseases. First, we use the Lipschitz condition to prove the model has a unique solution. Then, we construct a periodic linear system and obtain its fundamental solution matrix . The basic reproduction number is given by the equation . Furthermore, the disease-free equilibrium is globally asymptotically stable if . In addition, sensitivity analysis shows that if there are more mild infections within a period, then the infection rate ( ) and home isolation rate have a significant impact on . If the proportion of peak season is high, then we have . It indicates that reducing the infection rate can effectively control the spread of the epidemic compared with reducing . In summary, we can determine the most effective way to control the disease based on the number of mild and severe patients and the proportion of off-season and peak-season. Finally, we select two sets of parameters for numerical simulation.

Keywords: non-autonomous model, mild and severe infection, the basic reproduction number, sensitivity analysis

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