Sensitivity analysis and optimal countermeasures control of model of the spread of COVID-19 co-infection with HIV/AIDS

IF 0.5 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
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引用次数: 0

Abstract

This paper analyzes and examines the optimal control in the co-infection of COVID-19 with HIV/AIDS by providing preventive and treatment control measures. The population is divided into eight subpopulations. The preventive control of COVID-19 is denoted by u1. The preventive control of HIV/AIDS is denoted by u2. The treatment control of COVID-19 is denoted by u3, and the treatment control of COVID-19 for the subpopulation co-infected with HIV/AIDS is denoted by u4. Based on the model analysis, non-endemic and endemic equilibrium points are obtained, along with the basic reproduction number of the COVID-19, HIV/AIDS, and COVID-19-HIV/AIDS sub-models. Numerical simulations reveal that using preventive control u1 is more effective in reducing the spread of COVID-19 compared to u3 or u4, both individually and together. Preventive control u2 is more effective in controlling the spread of HIV/AIDS compared to the absence of control. The sensitivity analysis of parameter identifies parameters that significantly affect the reduction or increase in the spread of COVID-19-HIV/AIDS co-infection. We found that in order to reduce the co-infection’s spread, we should pay attention to the reducing the contact rate of HIV/AIDS patients or increasing their treatment rate.
COVID-19合并HIV/AIDS传播模型敏感性分析及最优对策控制
本文通过提供预防和治疗控制措施,分析和检验了COVID-19与HIV/AIDS合并感染的最优控制。这个种群被分为八个亚种群。COVID-19的预防控制用u1表示。艾滋病毒/艾滋病的预防性控制用u2表示。COVID-19治疗控制用u3表示,合并HIV/AIDS亚群的COVID-19治疗控制用u4表示。在模型分析的基础上,得到了COVID-19、HIV/AIDS和COVID-19-HIV/AIDS子模型的非地方性和地方性平衡点以及基本复制数。数值模拟表明,与单独或共同使用预防性控制措施u1相比,使用预防性控制措施u1在减少COVID-19传播方面更有效。预防性控制与不加以控制相比,在控制艾滋病毒/艾滋病的传播方面更为有效。参数的敏感性分析确定了显著影响covid -19-艾滋病毒/艾滋病合并感染传播减少或增加的参数。我们发现,为了减少合并感染的传播,应注意降低HIV/AIDS患者的接触率或提高其治疗率。
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
Communications in Mathematical Biology and Neuroscience
Communications in Mathematical Biology and Neuroscience COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.10
自引率
15.40%
发文量
80
期刊介绍: Communications in Mathematical Biology and Neuroscience (CMBN) is a peer-reviewed open access international journal, which is aimed to provide a publication forum for important research in all aspects of mathematical biology and neuroscience. This journal will accept high quality articles containing original research results and survey articles of exceptional merit.
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