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控制新冠病毒病的传播:最优控制分析

Controlling the Spread of COVID-19: Optimal Control Analysis.

作者信息

Madubueze Chinwendu E, Dachollom Sambo, Onwubuya Isaac Obiajulu

机构信息

Department of Mathematics/Statistics/Computer Science, University of Agriculture Makurdi, P.M.B. 2373, Markurdi, Nigeria.

Department of Mathematics/Statistics, Akanu Ibiam Federal Polytechnic, Unwana, P.M.B. 1007 Afikpo, Ebonyi State, Nigeria.

出版信息

Comput Math Methods Med. 2020 Sep 17;2020:6862516. doi: 10.1155/2020/6862516. eCollection 2020.

Abstract

Coronavirus disease 2019 (COVID-19) is a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS CoV-2). It was declared on March 11, 2020, by the World Health Organization as pandemic disease. The disease has neither approved medicine nor vaccine and has made governments and scholars search for drastic measures in combating the pandemic. Regrettably, the spread of the virus and mortality due to COVID-19 has continued to increase daily. Hence, it is imperative to control the spread of the disease particularly using nonpharmacological strategies such as quarantine, isolation, and public health education. This work studied the effect of these different control strategies as time-dependent interventions using mathematical modeling and optimal control approach to ascertain their contributions in the dynamic transmission of COVID-19. The model was proven to have an invariant region and was well-posed. The basic reproduction number and effective reproduction numbers were computed with and without interventions, respectively, and were used to carry out the sensitivity analysis that identified the critical parameters contributing to the spread of COVID-19. The optimal control analysis was carried out using the Pontryagin's maximum principle to figure out the optimal strategy necessary to curtail the disease. The findings of the optimal control analysis and numerical simulations revealed that time-dependent interventions reduced the number of exposed and infected individuals compared to time-independent interventions. These interventions were time-bound and best implemented within the first 100 days of the outbreak. Again, the combined implementation of only two of these interventions produced a good result in reducing infection in the population. While, the combined implementation of all three interventions performed better, even though zero infection was not achieved in the population. This implied that multiple interventions need to be deployed early in order to reduce the virus to the barest minimum.

摘要

2019冠状病毒病(COVID-19)是由严重急性呼吸综合征冠状病毒2(SARS-CoV-2)引起的一种疾病。2020年3月11日,世界卫生组织宣布该疾病为大流行病。该疾病既没有获批的药物,也没有疫苗,这使得各国政府和学者寻求严厉措施来抗击这一疫情。遗憾的是,COVID-19病毒的传播和由此导致的死亡率每天都在持续上升。因此,必须控制该疾病的传播,特别是采用隔离、检疫和公共卫生教育等非药物策略。本研究采用数学建模和最优控制方法,将这些不同的控制策略作为时间依赖性干预措施进行研究,以确定它们在COVID-19动态传播中的作用。该模型被证明具有一个不变区域且是适定的。分别计算了有无干预情况下的基本再生数和有效再生数,并用于进行敏感性分析,以确定导致COVID-19传播的关键参数。利用庞特里亚金极大值原理进行最优控制分析,以找出控制该疾病所需的最优策略。最优控制分析和数值模拟的结果表明,与时间非依赖性干预相比,时间依赖性干预减少了暴露和感染个体的数量。这些干预措施有时间限制,最好在疫情爆发的前100天内实施。此外,仅实施其中两种干预措施的联合实施在减少人群感染方面产生了良好效果。而三种干预措施的联合实施效果更好,尽管人群中未实现零感染。这意味着需要尽早部署多种干预措施,以便将病毒减少到最低限度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a3e/7499329/7a10ae9ac139/CMMM2020-6862516.001.jpg

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