A Comparative Analysis of Regression Models for Predicting COVID-19 Mortality

A Comparative Analysis of Regression Models for Predicting COVID-19 Mortality | 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 A Comparative Analysis of Regression Models for Predicting COVID-19 Mortality Prasanth Tirumalasetty This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8161142/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 The rapid proliferation of COVID-19 data necessitated robust machine learning models for forecasting and analysis. This study aimed to identify the most suitable regression model for predicting COVID-19 mortality by comparing Simple Linear Regression (SLR) and Multiple Linear Regression (MLR) models on a comprehensive dataset. Data preprocessing involved removing features with over 30% missing values and imputing the remainder using the sample median. SLR analysis demonstrated that models based on aggregated continental data were consistently non-significant (p > 0.05), whereas all models based on country-level attributes were statistically significant (p < 0.05). The strongest SLR model utilized total cases per million as a predictor, explaining 35.01% of the variance. However, subsequent diagnostic checks on the fitted MLR model revealed critical violations of classical linear regression assumptions, specifically the failure of the Shapiro-Wilk test for normality and the detection of heteroscedasticity. These violations render the MLR coefficient estimates and significance tests unreliable. Based on scatter plot observations confirming curvilinear relationships and the linear model failures, the study concludes that non-linear approaches, such as Polynomial Regression, are required to accurately model the relationship between variables and provide statistically sound predictive performance. Artificial Intelligence and Machine Learning Regression Analysis COVID-19 Mortality Simple Linear Regression Multiple Linear Regression Model Diagnostics Heteroscedasticity Polynomial Regression Full Text Additional Declarations The authors declare potential competing interests as follows: University of Michigan Dearborn The raw/supplemental data is available via the following public repository link: https://github.com/CSSEGISandData/COVID-19 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. 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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-8161142","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":547919803,"identity":"20257a60-1cd1-4afb-a0e5-a0477557d432","order_by":0,"name":"Prasanth Tirumalasetty","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYFACxgYQKQPlSciByAMP8Olgg2jhAWs+wGBhDNaSgFcLhIJpqUgEm4BPi7l8c9tn3hw7Hv7+w8cff2yTSJ8fdvgh0BY7Od0G7Fos2xibZ/NuS+aRuJGW2HCwTSJ34+00A6CWZGOzA9i1GBxjbGbm3cbMw3CDxxCiZXYCSMuBxG34tdTzyJ8//xGkJd1wdvoHYrQc5jE4kMMI0pIgL52D3xbLtsRmxrnbjvMY3kgznHHmnIThBumcggMJBrj9Ys58/DHD223VcnLnDz/4UFFWJy8/O33zhw8VdnI4vY/CYwTGksEBTHE8Whj+MDDIN+BWPQpGwSgYBSMTAADwQWPfihBqiAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0001-1294-2375","institution":"Medical Device Company","correspondingAuthor":true,"prefix":"","firstName":"Prasanth","middleName":"","lastName":"Tirumalasetty","suffix":""}],"badges":[],"createdAt":"2025-11-20 06:45:31","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8161142/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8161142/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96605930,"identity":"4267c2ee-ee08-4e68-87fa-46ac9a08a506","added_by":"auto","created_at":"2025-11-24 09:24:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":175976,"visible":true,"origin":"","legend":"","description":"","filename":"covidmorality1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8161142/v1_covered_364ea379-7884-4095-a02d-477f877a3ea1.pdf"}],"financialInterests":"\u003cp\u003eThe authors declare potential competing interests as follows: University of Michigan Dearborn\u003c/p\u003e\n\u003cp\u003eThe raw/supplemental data is available via the following public repository link: https://github.com/CSSEGISandData/COVID-19\u003c/p\u003e","formattedTitle":"\u003cp\u003eA Comparative Analysis of Regression Models for Predicting COVID-19 Mortality\u003c/p\u003e","fulltext":[],"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":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":"Regression Analysis, COVID-19 Mortality, Simple Linear Regression, Multiple Linear Regression, Model Diagnostics, Heteroscedasticity, Polynomial Regression","lastPublishedDoi":"10.21203/rs.3.rs-8161142/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8161142/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid proliferation of COVID-19 data necessitated robust machine learning models for forecasting and analysis. 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