An Optimal Control Experiment for an SEIRS Epidemiological Model

Tanner Snyder, Ryan Nierman
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Abstract

This work studies an optimal control model for a discrete-time Susceptible/Exposed/Infective/Removed/Susceptible (SEIRS) deterministic epidemiological model with a finite time horizon and changing population. The model presented converts a continuous SEIRS model that would typically be solved using differential equations into a discrete model that can be solved using dynamic programming. The discrete approach more closely resembles real life situations, as the number of individuals in a population, the rate of vaccination to be applied, and the time steps are all discrete values. The model utilizes a previously developed algorithm and applies it to the presented SEIRS model. To demonstrate the applicability of the algorithm, a series of numerical results are presented for various parameter values. KEYWORDS: Control; Cost; Discrete; Disease; Epidemiology; Minimization; Modeling; Optimality; SEIRS; Vaccination
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SEIRS流行病模型的最优控制实验
本工作研究了具有有限时间范围和不断变化的人群的离散时间易感/暴露/感染/移除/易感(SEIRS)确定性流行病学模型的最优控制模型。所提出的模型将通常使用微分方程求解的连续SEIRS模型转换为可以使用动态规划求解的离散模型。离散方法更接近于现实生活中的情况,因为人群中的个体数量、应用的疫苗接种率和时间步长都是离散值。该模型利用了先前开发的算法,并将其应用于所提出的SEIRS模型。为了证明该算法的适用性,给出了各种参数值的一系列数值结果。关键词:控制;费用离散的病流行病学;最小化;建模;最优性;seir;接种疫苗
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