A deterministic compartmental model for investigating the impact of escapees on the transmission dynamics of COVID-19

Josiah Mushanyu , Chidozie Williams Chukwu , Chinwendu Emilian Madubueze , Zviiteyi Chazuka , Chisara Peace Ogbogbo
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引用次数: 0

Abstract

The recent outbreak of the novel coronavirus (COVID-19) pandemic has devastated many parts of the globe. Non-pharmaceutical interventions are the widely available measures to combat and control the COVID-19 pandemic. There is great concern over the rampant unaccounted cases of individuals skipping the border during this critical period in time. We develop a deterministic compartmental model to investigate the impact of escapees (individuals who evade mandatory quarantine) on the transmission dynamics of COVID-19. A suitable Lyapunov function has shown that the disease-free equilibrium is globally asymptotically stable, provided R0<1. We performed a global sensitivity analysis using the Latin-hyper cube sampling method and partial rank correlation coefficients to determine the most influential model parameters on the short and long-term dynamics of the pandemic to minimize uncertainties associated with our variables and parameters. Results confirm a positive correlation between the number of escapees and the reported COVID-19 cases. It is shown that escapees are primarily responsible for the rapid increase in local transmissions. Also, the results from sensitivity analysis show that an increase in governmental role actions and a reduction in the illegal immigration rate will help to control and contain the disease spread.

研究逃亡者对COVID-19传播动态影响的确定性隔间模型
最近爆发的新型冠状病毒(COVID-19)大流行给全球许多地区造成了破坏。非药物干预措施是抗击和控制COVID-19大流行的广泛可用措施。在这一关键时期,个人越境的案件猖獗,令人极为关切。我们开发了一个确定性隔间模型来调查逃亡者(逃避强制隔离的个人)对COVID-19传播动态的影响。一个合适的Lyapunov函数表明,当R0<1时,无病平衡点是全局渐近稳定的。我们使用拉丁超立方体抽样方法和部分秩相关系数进行了全球敏感性分析,以确定对大流行短期和长期动态影响最大的模型参数,以最大限度地减少与我们的变量和参数相关的不确定性。结果证实,逃亡人数与报告的COVID-19病例之间存在正相关关系。结果表明,逃亡者是造成当地传播迅速增加的主要原因。此外,敏感性分析的结果表明,增加政府角色行动和减少非法移民率将有助于控制和遏制疾病的传播。
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
Healthcare analytics (New York, N.Y.)
Healthcare analytics (New York, N.Y.) Applied Mathematics, Modelling and Simulation, Nursing and Health Professions (General)
CiteScore
4.40
自引率
0.00%
发文量
0
审稿时长
79 days
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