Mathematical Modeling for COVID-19 with Focus on Intervention Strategies and Cost-Effectiveness Analysis

Mathematical Modeling for COVID-19 with Focus on Intervention Strategies and Cost-Effectiveness Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mathematical Modeling for COVID-19 with Focus on Intervention Strategies and Cost-Effectiveness Analysis Yang Deng, Yi Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1292485/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The development of appropriate mathematical models and realistic assessments of public health intervention strategies are of great significance to effectively combat the COVID-19 epidemic. In this paper, a novel COVID-19 epidemic model is devised based on the epidemiological states of the individuals and intervention strategies. Some dynamic behaviors of the model, such as forward and backward bifurcation, are analyzed. Specifically, we calibrate the model parameter values using actual COVID-19 data in Brazil by Markov Chain Monte Carlo algorithm such that we can study the effects of interventions on a practical case. Sensitivity analysis shows that non-pharmaceutical interventions are more effective than pharmaceutical interventions in the early stages of the COVID-19 outbreak, and the interventions, namely home isolation, face-mask wearing and media publicity, can effectively reduce the risk of COVID-19 transmission. Based on the new epidemic model, we formulate an optimal control model for studying the control of COVID-19 and then present a cost-effectiveness analysis to unravel the cost and effectiveness of the combination of intervention strategies. The results show that, when taking both the economic cost and the control effectiveness into account, strategy 7 appears to be preferred in inhibiting the COVID-19 outbreak, followed by strategy 5 and strategy 4. By assessing the consequences of these interventions in the real case, we obtain the effective non-pharmaceutical interventions that provide some management implications of controlling COVID-19. COVID-19 Parameter estimation Bifurcation Sensitivity analysis Optimal control Cost-effectiveness analysis Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 27 Jan, 2022 Reviewers invited by journal 27 Jan, 2022 Editor assigned by journal 27 Jan, 2022 First submitted to journal 24 Jan, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1292485","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":79748734,"identity":"347b8596-6256-4487-929e-54c8b5f2efb0","order_by":0,"name":"Yang Deng","email":"","orcid":"","institution":"Harbin Institute of Technology Shenzhen Graduate School","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Deng","suffix":""},{"id":79748735,"identity":"920b6a5e-6daa-4fda-a6d9-9390612425a2","order_by":1,"name":"Yi Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYDACCQgpZ8AMotlI0GJswMxMmhaGxA0MxGqRn9388AFjm0X6dnb+Awwfyg4z8M9uwK+Fcc4xYwPGNoncnc3MDIwzzh1mkLhzAL8WZokEMwmQlg2HmRmYedsOMxhIJODXwiaR/g2kJd0ApOUvMVp4JHLAtiSAtTASo0VCIqfYIOGchCHQYQYHe86l80jcIKBFfkb6xgcfyurkDc4ffPjgR5m1HP8MAlrAIBEaHQdALiVCPQj8IVLdKBgFo2AUjEwAAD5POUo9g+VLAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-1664-8613","institution":"Harbin Institute of Technology Shenzhen Graduate School","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2022-01-24 16:08:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1292485/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1292485/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":17809312,"identity":"4218a319-2c94-4fab-8f87-1517900eacb7","added_by":"auto","created_at":"2022-01-31 15:47:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1232098,"visible":true,"origin":"","legend":"","description":"","filename":"snarticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1292485/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eMathematical Modeling for COVID-19 with Focus on Intervention Strategies and Cost-Effectiveness Analysis\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1292485/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nonlinear-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nody","sideBox":"Learn more about [Nonlinear Dynamics](https://www.springer.com/journal/11071)","snPcode":"11071","submissionUrl":"https://submission.nature.com/new-submission/11071/3","title":"Nonlinear Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"COVID-19, Parameter estimation, Bifurcation, Sensitivity analysis, Optimal control, Cost-effectiveness analysis","lastPublishedDoi":"10.21203/rs.3.rs-1292485/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1292485/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The development of appropriate mathematical models and realistic assessments of public health intervention strategies are of great significance to effectively combat the COVID-19 epidemic. In this paper, a novel COVID-19 epidemic model is devised based on the epidemiological states of the individuals and intervention strategies. Some dynamic behaviors of the model, such as forward and backward bifurcation, are analyzed. Specifically, we calibrate the model parameter values using actual COVID-19 data in Brazil by Markov Chain Monte Carlo algorithm such that we can study the effects of interventions on a practical case. Sensitivity analysis shows that non-pharmaceutical interventions are more effective than pharmaceutical interventions in the early stages of the COVID-19 outbreak, and the interventions, namely home isolation, face-mask wearing and media publicity, can effectively reduce the risk of COVID-19 transmission. Based on the new epidemic model, we formulate an optimal control model for studying the control of COVID-19 and then present a cost-effectiveness analysis to unravel the cost and effectiveness of the combination of intervention strategies. The results show that, when taking both the economic cost and the control effectiveness into account, strategy 7 appears to be preferred in inhibiting the COVID-19 outbreak, followed by strategy 5 and strategy 4. By assessing the consequences of these interventions in the real case, we obtain the effective non-pharmaceutical interventions that provide some management implications of controlling COVID-19.","manuscriptTitle":"Mathematical Modeling for COVID-19 with Focus on Intervention Strategies and Cost-Effectiveness Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-01-31 15:47:24","doi":"10.21203/rs.3.rs-1292485/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2022-01-28T03:14:22+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-01-28T01:57:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-01-27T10:53:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Nonlinear Dynamics","date":"2022-01-24T11:01:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nonlinear-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nody","sideBox":"Learn more about [Nonlinear Dynamics](https://www.springer.com/journal/11071)","snPcode":"11071","submissionUrl":"https://submission.nature.com/new-submission/11071/3","title":"Nonlinear Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"161fffff-b0c5-4edb-8963-f89befe62566","owner":[],"postedDate":"January 31st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-08-09T14:22:31+00:00","versionOfRecord":[],"versionCreatedAt":"2022-01-31 15:47:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1292485","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1292485","identity":"rs-1292485","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}