Single and Multiobjective Optimal Control of the COVID Pandemic Model Involving Hospitalizations and Non-Pharmaceutical Control Actions

S. Lakshmi
{"title":"Single and Multiobjective Optimal Control of the COVID Pandemic Model Involving Hospitalizations and Non-Pharmaceutical Control Actions","authors":"S. Lakshmi","doi":"10.23937/2474-3658/1510282","DOIUrl":null,"url":null,"abstract":"Objectives: In this paper, single and multiobjective optimal control is performed on a Corona Virus disease model involving hospitalizations and non-pharmaceutical intervention tasks to minimize the damage done by the virus. This model considers the effects of hospitalization and non-pharmaceutical interventions like quarantining and social distancing. Methods: This method does not use weighted functions but minimizes the distance from the utopia point. The utopia point is obtained by the single objective optimal control procedure and the multiobjective optimal control is performed by minimizing the distance from the Utopia point. The optimization program, Pyomo where the differential equations are automatically converted to a Nonlinear Program is used in conjunction with the state-of-the-art global optimization solver BARON. Results: Four single objective optimal control and one multiobjective optimal control problem were solved. The single optimal control involves minimizing the infections, death rate, and the cost of performing the control tasks and maximizing the recovered subjects. The multiobjective optimization involves minimizing the infections, death rate, and the cost of performing the control tasks and maximizing the recovered subjects at the same time. It is observed that the multiobjective optimal control is as effective as the single objective optimization in addition to having the advantage of controlling many variables. Conclusions: The multiobjective optimization involving the minimization of the distance from the Utopia point is very effective to obtain the best control profiles and enables one to maximize the number of recovered subjects while keeping the cost of performing the control tasks as low as possible. In fact, it is as effective as the single objective optimal control that involves maximizing the recovered subjects without dealing with the cost of performing the control tasks.","PeriodicalId":93465,"journal":{"name":"Journal of infectious diseases and epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of infectious diseases and epidemiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23937/2474-3658/1510282","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Objectives: In this paper, single and multiobjective optimal control is performed on a Corona Virus disease model involving hospitalizations and non-pharmaceutical intervention tasks to minimize the damage done by the virus. This model considers the effects of hospitalization and non-pharmaceutical interventions like quarantining and social distancing. Methods: This method does not use weighted functions but minimizes the distance from the utopia point. The utopia point is obtained by the single objective optimal control procedure and the multiobjective optimal control is performed by minimizing the distance from the Utopia point. The optimization program, Pyomo where the differential equations are automatically converted to a Nonlinear Program is used in conjunction with the state-of-the-art global optimization solver BARON. Results: Four single objective optimal control and one multiobjective optimal control problem were solved. The single optimal control involves minimizing the infections, death rate, and the cost of performing the control tasks and maximizing the recovered subjects. The multiobjective optimization involves minimizing the infections, death rate, and the cost of performing the control tasks and maximizing the recovered subjects at the same time. It is observed that the multiobjective optimal control is as effective as the single objective optimization in addition to having the advantage of controlling many variables. Conclusions: The multiobjective optimization involving the minimization of the distance from the Utopia point is very effective to obtain the best control profiles and enables one to maximize the number of recovered subjects while keeping the cost of performing the control tasks as low as possible. In fact, it is as effective as the single objective optimal control that involves maximizing the recovered subjects without dealing with the cost of performing the control tasks.
涉及住院和非药物控制行为的COVID大流行模型的单目标和多目标最优控制
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信