Investigating the Relationships Between COVID-19 Cases, Public Health Interventions, Vaccine Coverage, and Temperature in Ontario and Toronto

Investigating the Relationships Between COVID-19 Cases, Public Health Interventions, Vaccine Coverage, and Temperature in Ontario and Toronto | 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 Investigating the Relationships Between COVID-19 Cases, Public Health Interventions, Vaccine Coverage, and Temperature in Ontario and Toronto Melinaz Barati Chermahini, Vernon Hoeppner This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6018943/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective : We examined the relationship between COVID-19 cases and Public Health Interventions (PHIs). We also explored the relationship between cases and vaccine, and temperature. We compared the results with published mathematical models. Methods : We developed monthly PHI scores using the Oxford COVID-19 Government Response Tracker for May 2020 to May 2021. We calculated PHI scores by summing the highest monthly score of each intervention and expressed the PHI score as a percentage of the maximum. We obtained vaccine coverage and temperature data from January 2021 to September 2023. We calculated Spearman’s rank-order correlation coefficients to examine correlations. Results : Correlation for cases and PHI was positive (r = 0.947, p <.0001). Correlation for cases and vaccine coverage was approximately zero (r = 0.0165, p = 0.957) for January 2021 to January 2022, and negative for February 2022 to September 2023 (r = -0.816, p <.0001). Correlation for cases and temperature was negative for January 2021 to January 2022 (r = -0.676, p = 0.0112), and almost zero for February 2022 to September 2023 (r = -0.162, p = 0.494). Models showed negative correlation for PHI and vaccine coverage, and mixed results for temperature. Conclusion : There was a positive correlation between cases and PHI. Prior to vaccine threshold coverage, there was no correlation for vaccination and negative correlation for temperature. Post vaccine threshold, there was a negative correlation for vaccination and no correlation for temperature. Correlation results for PHI and temperature differed from mathematical models. Epidemiology COVID-19 epidemiology public health surveillance correlation models Figures Figure 1 Figure 2 Figure 3 Full Text Additional Declarations The authors declare no competing interests. Supplementary Files OnlineResource1PHIScoringFramework.xlsx Online Resource 1 PHI scoring framework used for public health interventions implemented in Ontario from May 2020 to May 2021. Onlineresource2SASCodes.pdf Online Resource 2 Codes used to conduct Spearman’s rank-order correlation analysis for the relationship between cases, PHI, vaccination coverage and temperature. SAS OnDemand for Academics was used to conduct correlation analysis. Cite Share Download PDF Status: Posted Version 1 posted 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-6018943","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":415052659,"identity":"af2c9de9-953d-4c75-a7d8-89a3441e48b2","order_by":0,"name":"Melinaz Barati Chermahini","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYFAC5oYDQDKBgb0BSBlYEKOFEaqFB0QZSBCnhQGsRSIBRBOhRbe9sfFwQU1dHv/M51c3/CiQYOBv707Aq8XszMGGwzOOsRVL3M4pu9kDdJjEmbMb8Gu5kdhwmIeNJ3GDdE7aDR6gFgOJXAJa7j8EavknkbhB8kzazT9EabnB2HCYt80gcYME+7HbxNlyBugw3r6EYokzOWy3ZQwkeAj75fjhw595vgFDrP34s5tv/tjI8bf34teCBHgMwCSxykGA/QEpqkfBKBgFo2AEAQC2xEvwSLCGHQAAAABJRU5ErkJggg==","orcid":"","institution":"Queen's University","correspondingAuthor":true,"prefix":"","firstName":"Melinaz","middleName":"Barati","lastName":"Chermahini","suffix":""},{"id":415052660,"identity":"f3aa185d-af29-4532-9a24-962a2f803a98","order_by":1,"name":"Vernon Hoeppner","email":"","orcid":"","institution":"University of Saskatchewan","correspondingAuthor":false,"prefix":"","firstName":"Vernon","middleName":"","lastName":"Hoeppner","suffix":""}],"badges":[],"createdAt":"2025-02-13 02:50:20","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6018943/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6018943/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76421482,"identity":"8c0d35a5-6fbc-45fe-824d-c5b54170102f","added_by":"auto","created_at":"2025-02-17 04:23:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":291330,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly new COVID-19 case rate (per 100,000) and PHI score as % of the maximum score for Ontario from May 2020 to May 2021. 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SAS OnDemand for Academics was used to conduct correlation analysis.\u003c/p\u003e","description":"","filename":"Onlineresource2SASCodes.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6018943/v1/66e0b7998359bf25bdcb707d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eInvestigating the Relationships Between COVID-19 Cases, Public Health Interventions, Vaccine Coverage, and Temperature in Ontario and Toronto\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Queen's University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"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":"COVID-19, epidemiology, public health surveillance, correlation, models","lastPublishedDoi":"10.21203/rs.3.rs-6018943/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6018943/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/em\u003e:\u003cstrong\u003e \u003c/strong\u003eWe examined the relationship between COVID-19 cases and Public Health Interventions (PHIs). We also explored the relationship between cases and vaccine, and temperature. We compared the results with published mathematical models.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e We developed monthly PHI scores using the Oxford COVID-19 Government Response Tracker for May 2020 to May 2021. We calculated PHI scores by summing the highest monthly score of each intervention and expressed the PHI score as a percentage of the maximum. We obtained vaccine coverage and temperature data from January 2021 to September 2023. We calculated Spearman’s rank-order correlation coefficients to examine correlations.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e Correlation for cases and PHI was positive (r = 0.947, p \u0026lt;.0001). Correlation for cases and vaccine coverage was approximately zero (r = 0.0165, p = 0.957) for January 2021 to January 2022, and negative for February 2022 to September 2023 (r = -0.816, p \u0026lt;.0001). Correlation for cases and temperature was negative for January 2021 to January 2022 (r = -0.676, p = 0.0112), and almost zero for February 2022 to September 2023 (r = -0.162, p = 0.494). Models showed negative correlation for PHI and vaccine coverage, and mixed results for temperature.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e There was a positive correlation between cases and PHI. Prior to vaccine threshold coverage, there was no correlation for vaccination and negative correlation for temperature. Post vaccine threshold, there was a negative correlation for vaccination and no correlation for temperature. 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