An SIRS-model considering waning efficacy and periodic re-vaccination

IF 5.3 1区 数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Joseph Páez Chávez , Aytül Gökçe , Thomas Götz , Burcu Gürbüz
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Abstract

In this paper, we extend the classical SIRS (Susceptible-Infectious-Recovered-Susceptible) model from mathematical epidemiology by incorporating a vaccinated compartment, V, accounting for an imperfect vaccine with waning efficacy over time. The SIRSV-model divides the population into four compartments and introduces periodic re-vaccination for waning immunity. The efficacy of the vaccine is assumed to decay with the time passed since the vaccination. Periodic re-vaccinations are applied to the population. We develop a partial differential equation (PDE) model for the continuous vaccination time and a coupled ordinary differential equation (ODE) system when discretizing the vaccination period. We analyze the equilibria of the ODE model and investigate the linear stability of the disease-free equilibrium (DFE). Furthermore, we explore an optimization framework where vaccination rate, re-vaccination time, and non-pharmaceutical interventions (NPIs) are control variables to minimize infection levels. The optimization objective is defined using different norm-based measures of infected individuals. A numerical analysis of the model’s dynamic behavior under varying control parameters is conducted using path-following methods. The analysis focuses on the impacts of vaccination strategies and contact limitation measures. Bifurcation analysis reveals complex behaviors, including bistability, fold bifurcations, forward and backward bifurcations, highlighting the need for combined vaccination and contact control strategies to manage disease spread effectively.
考虑到效力减弱和定期再接种的 SIRS 模型
在本文中,我们从数学流行病学扩展了经典的SIRS(易感-感染-恢复-易感)模型,纳入了一个接种疫苗的隔间,V,考虑了不完美的疫苗随着时间的推移而减弱的效力。siv模型将人群分为四个部分,并引入定期重新接种疫苗以防止免疫力下降。假定疫苗的效力随着接种后的时间流逝而衰减。定期对人口重新接种疫苗。我们建立了连续接种时间的偏微分方程(PDE)模型和离散接种时间的耦合常微分方程(ODE)系统。我们分析了ODE模型的平衡点,并研究了无病平衡点的线性稳定性。此外,我们探索了一个优化框架,其中疫苗接种率,重新疫苗接种时间和非药物干预(npi)是控制变量,以尽量减少感染水平。使用不同的基于规范的感染个体度量来定义优化目标。采用路径跟踪方法对模型在不同控制参数下的动态特性进行了数值分析。分析的重点是疫苗接种策略和接触限制措施的影响。分岔分析揭示了复杂的行为,包括双稳定性、双重分岔、向前分岔和向后分岔,强调需要联合接种疫苗和接触控制策略来有效管理疾病传播。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Chaos Solitons & Fractals
Chaos Solitons & Fractals 物理-数学跨学科应用
CiteScore
13.20
自引率
10.30%
发文量
1087
审稿时长
9 months
期刊介绍: Chaos, Solitons & Fractals strives to establish itself as a premier journal in the interdisciplinary realm of Nonlinear Science, Non-equilibrium, and Complex Phenomena. It welcomes submissions covering a broad spectrum of topics within this field, including dynamics, non-equilibrium processes in physics, chemistry, and geophysics, complex matter and networks, mathematical models, computational biology, applications to quantum and mesoscopic phenomena, fluctuations and random processes, self-organization, and social phenomena.
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