Impacts of a delayed and slow-paced vaccination on cases and deaths during the COVID-19 pandemic: a modelling study

Impacts of a delayed and slow-paced vaccination on cases and deaths during the COVID-19 pandemic: a modelling study | 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 Impacts of a delayed and slow-paced vaccination on cases and deaths during the COVID-19 pandemic: a modelling study Gustavo Libotte, Lucas Anjos, Regina Célia Cerqueira de Almeida, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-936335/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Background : In Brazil, vaccination has always been cutting across party political and ideological lines, which have delayed its start and brought the whole process into disrepute. Such divergences put the immunisation of the population in the background and create additional hurdles beyond the pandemic, mistrust and scepticism over vaccines. Methods : We conduct a mathematical modelling study to analyse the impacts of late vaccination and with slowly increasing coverage, as well as how harmful it would be if part of the population refused to get vaccinated or missed the second dose. We analyse data from confirmed cases, deaths caused by COVID-19, and vaccination in the state of Rio de Janeiro in the period between March 10, 2020, and October 27, 2021. The classical SIR model is extended to consider the effect of vaccination (efficacy, interval between doses, and vaccination rate) and data sets are regularised using Gaussian Process Regression. The model parameter distributions are estimated using Bayesian inference, aiming to obtain credible intervals in the simulations. Findings : We estimate that if the start of vaccination had been 30 days earlier, combined with efforts to drive vaccination rates up, 31,657 (25,801–35,117) deaths could have been averted. Our results also indicate that the slow pace of vaccination and the low demand for the second dose could cause a resurgence of cases as early as 2022. Interpretation : The government's inaction and lack of a strategic plan to fight the pandemic meant that vaccination started late, leading to thousands of deaths that could have been prevented. Even when reaching the expected vaccination coverage for the first dose, it is still challenging to increase adherence to the second dose and maintain a high vaccination rate to avoid new outbreaks. Funding : Carlos Chagas Filho Foundation for Supporting Research in the State of Rio de Janeiro (FAPERJ) and Brazilian National Council for Scientific and Technological Development (CNPq). Applied Mathematics Infectious Diseases low vaccination coverage delayed start of vaccination resurgence of cases avertable deaths COVID-19 Figures Figure 1 Figure 2 Figure 3 Figure 4 Full Text Additional Declarations There is NO Competing Interest. Supplementary Files supplementarymaterial.pdf Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-936335","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":56619028,"identity":"605a4517-09a4-4208-87be-be4a5a336704","order_by":0,"name":"Gustavo Libotte","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYDACdsY2MM3PDqYOEKGFGaQlwYBBspl4LQxsYC0Gh4nVIu/M3Pbg448/csaHeQ9++MFwJ5+gFsPDjO2GMxIMjM0O8yVL9jA8s2wgqKWZsU2aJ8EgcdthHjNmBobDBoRtgWqp39xMrBZ5ZoiWBANmYrUYMIP8kmZsOOMwj7Fkj8EzImxpb3/24IONnDx/e4/hhx8Vd4iw5QAql6AGoC0NRCgaBaNgFIyCEQ4Ay500XTEI9CwAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-4583-6026","institution":"National Laboratory for Scientific Computing","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Gustavo","middleName":"","lastName":"Libotte","suffix":""},{"id":56619029,"identity":"a1f5ee1a-948e-465f-9f82-97f7cf8d9b22","order_by":1,"name":"Lucas Anjos","email":"","orcid":"https://orcid.org/0000-0002-9623-5570","institution":"National Laboratory for Scientific Computing","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lucas","middleName":"","lastName":"Anjos","suffix":""},{"id":56619030,"identity":"6c0909d5-0cce-4809-b951-75b4aa5d70db","order_by":2,"name":"Regina Célia Cerqueira de Almeida","email":"","orcid":"","institution":"National Laboratory for Scientific Computing","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Regina","middleName":"Célia Cerqueira","lastName":"de Almeida","suffix":""},{"id":56619031,"identity":"70211155-c477-4bd3-b08a-4942584a6234","order_by":3,"name":"Sandra Mara Malta","email":"","orcid":"https://orcid.org/0000-0003-1272-6039","institution":"National Laboratory for Scientific Computing","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sandra","middleName":"Mara","lastName":"Malta","suffix":""},{"id":56619032,"identity":"5021138e-cfda-4618-96fe-628087ed2f7d","order_by":4,"name":"Roberto Medronho","email":"","orcid":"https://orcid.org/0000-0003-4073-3930","institution":"Federal University of Rio de Janeiro","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Roberto","middleName":"","lastName":"Medronho","suffix":""}],"badges":[],"createdAt":"2021-09-24 22:55:35","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-936335/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-936335/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":15643859,"identity":"c4519629-a914-449f-b08f-9c82306a7156","added_by":"auto","created_at":"2021-11-17 18:27:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":98687,"visible":true,"origin":"","legend":"Schematic representation of the model.","description":"","filename":"modelstructure.png","url":"https://assets-eu.researchsquare.com/files/rs-936335/v2/4b9ba377f1bad8b91d0345bf.png"},{"id":15643879,"identity":"f07163ff-eeb5-44d7-afb6-d3e478c44817","added_by":"auto","created_at":"2021-11-17 18:30:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1291962,"visible":true,"origin":"","legend":"Speed in vaccination and expectation of disease mitigation in RJ. (A) Map of vaccine doses distributed in each Brazilian state per 100,000 inhabitants, and the portion of each type of vaccine destined for RJ, until October 27, 2021. (B) Frequency of vaccination rate for each shot, in terms of percentage of population per day. (C) Perspective of coverage of the population eligible for vaccination for each shot. (D) Model simulation considering different vaccination rates, slow (ν = 0·35%), intermediate (ν = 0·40%), and fast (ν = 0·50%), taking into account the frequencies (B) and the proportion of vaccines of each type (A). The model is simulated until reaching the same amount of vaccines administered in each scenario (see the lower right frame). The shaded areas represent the 95% credible interval. Note that the period of the end of vaccination in the scenario where an intermediate vaccination rate is adopted agrees with the prognosis in (C).","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-936335/v2/6ff8cd7ea77d2279839b40d7.png"},{"id":15643860,"identity":"cfab4dfb-616a-44c7-9e55-92fa93c5dd1c","added_by":"auto","created_at":"2021-11-17 18:27:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1070312,"visible":true,"origin":"","legend":"Importance of rolling out vaccination program as soon as possible. (A) Simulations considering scenarios in which the start of vaccination is advanced or delayed by up to 30 days in relation to the actual start date. The grey shaded area represents the six-month interval from the start of vaccination, January 20, 2021. (B) Cumulative number of dead and infected individuals when reaching 80% vaccination coverage, varying the day on which vaccination is started, as well as vaccination rates. The error bars are associated with the 95% credible interval of the simulations and the black dots refer to the simulations when the maximum a posterior of the inferred parameters are adopted. (C) Relationship between the number of individuals vaccinated and dead over time, given a 30-day early or late start in vaccination in relation to the actual date. The vertical dashed lines express the approximate period at which deaths would peak, for each particular vaccination rate. (D) Effective reproduction number, given the analysed scenarios.","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-936335/v2/c90adee4b95ded50011c1097.png"},{"id":15643862,"identity":"d602b774-2cc6-417e-aa78-13e71f015d28","added_by":"auto","created_at":"2021-11-17 18:27:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1301860,"visible":true,"origin":"","legend":"Flawed vaccination policy and excess deaths. (A) Model simulation where part of the population eligible to be vaccinated does not receive any dose. (B) Ratio between the number of deaths given potential scenarios in which the start of vaccination is ahead of the actual date. Scenarios where vaccination would be implemented 10, 20, and 30 days before January 20, 2021 are considered, as well as two vaccination rates (ν = 0·40% and ν = 0·50%), and excess deaths are estimated. (C) Variation in the cumulative number of deaths and the number of deaths at the peak of the epidemic curve (during vaccination) taking into account the start of vaccination on different days. The relative percentage amount of cumulative deaths is shown, as well as the month in which deaths would peak. (D) Simulation considering that part of the population proportional to α does not take the second dose of the vaccine. Two scenarios are considered in which only the first dose of the vaccine has efficacy equivalent to μη, combined with two vaccination rates (ν = 0·35% and ν = 0·40%).","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-936335/v2/2951c3610148134abde8decd.png"},{"id":15643880,"identity":"50dbc02a-d7c0-4ffd-ae2b-928551ce0ac8","added_by":"auto","created_at":"2021-11-17 18:30:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3710558,"visible":true,"origin":"","legend":"","description":"","filename":"Libotteetal2021.pdf","url":"https://assets-eu.researchsquare.com/files/rs-936335/v2_covered.pdf"},{"id":15643863,"identity":"c75bdeb3-37b9-407a-8296-3a41610a62a9","added_by":"auto","created_at":"2021-11-17 18:27:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4771945,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-936335/v2/9621d80439143b6313b92d77.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"\u003cp\u003eImpacts of a delayed and slow-paced vaccination on cases and deaths during the COVID-19 pandemic: a modelling study\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-936335/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"low vaccination coverage, delayed start of vaccination, resurgence of cases, avertable deaths, COVID-19","lastPublishedDoi":"10.21203/rs.3.rs-936335/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-936335/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: In Brazil, vaccination has always been cutting across party political and ideological lines, which have delayed its start and brought the whole process into disrepute. Such divergences put the immunisation of the population in the background and create additional hurdles beyond the pandemic, mistrust and scepticism over vaccines.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We conduct a mathematical modelling study to analyse the impacts of late vaccination and with slowly increasing coverage, as well as how harmful it would be if part of the population refused to get vaccinated or missed the second dose. We analyse data from confirmed cases, deaths caused by COVID-19, and vaccination in the state of Rio de Janeiro in the period between March 10, 2020, and October 27, 2021. The classical SIR model is extended to consider the effect of vaccination (efficacy, interval between doses, and vaccination rate) and data sets are regularised using Gaussian Process Regression. The model parameter distributions are estimated using Bayesian inference, aiming to obtain credible intervals in the simulations.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e: We estimate that if the start of vaccination had been 30 days earlier, combined with efforts to drive vaccination rates up, 31,657 (25,801–35,117) deaths could have been averted. Our results also indicate that the slow pace of vaccination and the low demand for the second dose could cause a resurgence of cases as early as 2022.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInterpretation\u003c/strong\u003e: The government's inaction and lack of a strategic plan to fight the pandemic meant that vaccination started late, leading to thousands of deaths that could have been prevented. Even when reaching the expected vaccination coverage for the first dose, it is still challenging to increase adherence to the second dose and maintain a high vaccination rate to avoid new outbreaks.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: Carlos Chagas Filho Foundation for Supporting Research in the State of Rio de Janeiro (FAPERJ) and Brazilian National Council for Scientific and Technological Development (CNPq).\u003c/p\u003e","manuscriptTitle":"Impacts of a delayed and slow-paced vaccination on cases and deaths during the COVID-19 pandemic: a modelling study","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2021-11-17 18:27:14","doi":"10.21203/rs.3.rs-936335/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2021-10-14 16:46:34","doi":"10.21203/rs.3.rs-936335/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eb8e241f-56ca-4fb0-bacd-6093b416429e","owner":[],"postedDate":"November 17th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":8574383,"name":"Applied Mathematics"},{"id":8574384,"name":"Infectious Diseases"}],"tags":[],"updatedAt":"2026-05-21T09:34:10+00:00","versionOfRecord":[],"versionCreatedAt":"2021-11-17 18:27:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-936335","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-936335","identity":"rs-936335","version":["v2"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}