An optimal control model for COVID-19, zika, dengue, and chikungunya co-dynamics with reinfection.

IF 2 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS
Andrew Omame, Mary Ele Isah, Mujahid Abbas
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引用次数: 0

Abstract

The co-circulation of different emerging viral diseases is a big challenge from an epidemiological point of view. The similarity of symptoms, cases of virus co-infection, and cross-reaction can mislead in the diagnosis of the disease. In this article, a new mathematical model for COVID-19, zika, chikungunya, and dengue co-dynamics is developed and studied to assess the impact of COVID-19 on zika, dengue, and chikungunya dynamics and vice-versa. The local and global stability analyses are carried out. The model is shown to undergo a backward bifurcation under a certain condition. Global sensitivity analysis is also performed on the parameters of the model to determine the most dominant parameters. If the zika-related reproduction number 0Z is used as the response function, then important parameters are: the effective contact rate for vector-to-human transmission of zika ( β 2 h , which is positively correlated), the human natural death rate ( ϑ h , positively correlated), and the vector recruitment rate ( Ψ v , also positively correlated). In addition, using the class of individuals co-infected with COVID-19 and zika ( CZ h ) as response function, the most dominant parameters are: the effective contact rate for COVID-19 transmission ( β 1 , positively correlated), the effective contact rate for vector-to-human transmission of zika ( β 2 h , positively correlated). To control the co-circulation of all the diseases adequately under an endemic setting, time dependent controls in the form of COVID-19, zika, dengue, and chikungunya preventions are incorporated into the model and analyzed using the Pontryagin's principle. The model is fitted to real COVID-19, zika, dengue, and chikungunya datasets for Espirito Santo (a city with the co-circulation of all the diseases), in Brazil and projections made for the cumulative cases of each of the diseases. Through simulations, it is shown that COVID-19 prevention could greatly reduce the burden of co-infections with zika, dengue, and chikungunya. The negative impact of the COVID-19 pandemic on the control of the arbovirus diseases is also highlighted. Furthermore, it is observed that prevention controls for zika, dengue, and chikungunya can significantly reduce the burden of co-infections with COVID-19.

COVID-19、寨卡病毒、登革热和基孔肯雅病毒再感染共同动力学的优化控制模型。
从流行病学的角度来看,不同新发病毒性疾病的共同流行是一个巨大的挑战。症状相似、病毒共感染病例和交叉反应可能会误导疾病的诊断。本文建立并研究了 COVID-19、寨卡、基孔肯雅和登革热共同动力学的新数学模型,以评估 COVID-19 对寨卡、登革热和基孔肯雅动力学的影响,反之亦然。模型进行了局部和全局稳定性分析。结果表明,该模型在一定条件下会发生向后分叉。还对模型参数进行了全局敏感性分析,以确定最主要的参数。如果将寨卡相关繁殖数ℛ 0Z作为响应函数,那么重要的参数有:寨卡病媒向人类传播的有效接触率(β 2 h,正相关)、人类自然死亡率(ϑ h,正相关)和病媒招募率(Ψ v,也正相关)。此外,以COVID-19和寨卡共同感染者类别(ℐ CZ h)作为响应函数,最主要的参数是:COVID-19传播的有效接触率(β 1,正相关)、寨卡病媒向人类传播的有效接触率(β 2 h,正相关)。为了在疾病流行的情况下充分控制所有疾病的共同传播,在模型中加入了 COVID-19、寨卡、登革热和基孔肯雅预防措施等与时间相关的控制措施,并使用庞特里亚金原理进行分析。该模型适用于巴西圣埃斯皮里图(所有疾病的共同流行城市)的 COVID-19、寨卡、登革热和基孔肯雅病的真实数据集,并对每种疾病的累积病例进行预测。模拟结果表明,COVID-19 预防可大大减轻寨卡、登革热和基孔肯雅病的共同感染负担。COVID-19 大流行对控制虫媒病毒疾病的负面影响也得到了强调。此外,还观察到对寨卡、登革热和基孔肯雅病的预防控制可大大减轻 COVID-19 共同感染的负担。
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
Optimal Control Applications & Methods
Optimal Control Applications & Methods 工程技术-应用数学
CiteScore
3.90
自引率
11.10%
发文量
108
审稿时长
3 months
期刊介绍: Optimal Control Applications & Methods provides a forum for papers on the full range of optimal and optimization based control theory and related control design methods. The aim is to encourage new developments in control theory and design methodologies that will lead to real advances in control applications. Papers are also encouraged on the development, comparison and testing of computational algorithms for solving optimal control and optimization problems. The scope also includes papers on optimal estimation and filtering methods which have control related applications. Finally, it will provide a focus for interesting optimal control design studies and report real applications experience covering problems in implementation and robustness.
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