COVID-19: Examining the case growth-rate due to Visitor vs Local Mobility using Machine Learning: A Study in the United States

COVID-19: Examining the case growth-rate due to Visitor vs Local Mobility using Machine Learning: A Study in the United States | 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 COVID-19: Examining the case growth-rate due to Visitor vs Local Mobility using Machine Learning: A Study in the United States satya katragadda, ravi teja bhupatiraju, vijay raghavan, ziad ashkar, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-916701/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 Background: Travel patterns of humans play a major part in the spread of infectious diseases. This was evident in the geographical spread of COVID-19 in the United States. However, the impact of this mobility and the transmission of the virus due to local travel, compared to the population traveling across state boundaries, is unknown. This study evaluates the impact of local vs. visitor mobility in understanding the growth in the number of cases for infectious disease outbreaks. Methods: We use two different mobility metrics, namely the local risk and visitor risk extracted from trip data generated from anonymized mobile phone data across all 50 states in the United States. We analyzed the impact of just using local trips on infection spread and infection risk potential generated from visitors' trips from various other states. We used the Diebold-Mariano test to compare across three machine learning models. Finally, we compared the performance of models, including visitor mobility for all the three waves in the United States and across all 50 states. Results: We observe that visitor mobility impacts case growth and that including visitor mobility in forecasting the number of COVID-19 cases improves prediction accuracy by 34. We found the statistical significance with respect to the performance improvement resulting from including visitor mobility using the Diebold-Mariano test. We also observe that the significance was much higher during the first peak March to June 2020. Conclusion: With presence of cases everywhere (i.e. local and visitor), visitor mobility (even within the country) is shown to have significant impact on growth in number of cases. While it is not possible to account for other factors such as the impact of interventions, and differences in local mobility and visitor mobility, we find that these observations can be used to plan for both reopening and limiting visitors from regions where there are high number of cases. Infectious Diseases Hospitality and Tourism Behavioral Geography COVID-19 mobility visitor risk machine learning Full Text 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-916701","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":52798551,"identity":"06d7349a-a8aa-45ac-a070-0d9d810855ed","order_by":0,"name":"satya katragadda","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"satya","middleName":"","lastName":"katragadda","suffix":""},{"id":52798553,"identity":"6ff7f69b-6e84-4dde-814e-eefc0289cd67","order_by":1,"name":"ravi teja bhupatiraju","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"ravi","middleName":"teja","lastName":"bhupatiraju","suffix":""},{"id":52798554,"identity":"4f5f7d11-b069-4690-a02b-489ed9bb9ca0","order_by":2,"name":"vijay raghavan","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"vijay","middleName":"","lastName":"raghavan","suffix":""},{"id":52798555,"identity":"1fa8d7fc-682e-4d20-be8c-17e287a7ae49","order_by":3,"name":"ziad ashkar","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"ziad","middleName":"","lastName":"ashkar","suffix":""},{"id":52798552,"identity":"86e7bc7a-0711-4b25-b9a6-62804e6a5a6e","order_by":4,"name":"raju gottumukkala","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYLACCQYbHpK1pJGqhYHhMAlq5aN7H36wbDsvw8/efIDh455awloM7xw3lpBsu80j2XMsgXHGs+NEaJmRxgDWYnAjx4CZ58AxorQw/5BsO8djf//9B+K0yEuksQFtOcBjIMHDANRSQ1iLAVCLhcS5ZB6JM2kGB2ccOECELUCH3ZYos7Pnbz/88MGHA3VE2AI0llkCyjlAVATJNzAwMH5A8ImwZRSMglEwCkYcAAARGDbnCASjCAAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"raju","middleName":"","lastName":"gottumukkala","suffix":""}],"badges":[],"createdAt":"2021-09-17 21:39:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-916701/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-916701/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":13790529,"identity":"061ba357-e24e-4f09-a08b-f67d3230f359","added_by":"auto","created_at":"2021-09-20 18:29:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3353139,"visible":true,"origin":"","legend":"","description":"","filename":"InternalExternalMobility1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-916701/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eCOVID-19: Examining the case growth-rate due to Visitor vs Local Mobility using Machine Learning: A Study in the United States\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-916701/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":"COVID-19, mobility, visitor risk, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-916701/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-916701/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Travel patterns of humans play a major part in the spread of infectious diseases. This was evident in the geographical spread of COVID-19 in the United States. However, the impact of this mobility and the transmission of the virus due to local travel, compared to the population traveling across state boundaries, is unknown. This study evaluates the impact of local vs. visitor mobility in understanding the growth in the number of cases for infectious disease outbreaks. \u003c/p\u003e\u003cp\u003eMethods: We use two different mobility metrics, namely the local risk and visitor risk extracted from trip data generated from anonymized mobile phone data across all 50 states in the United States. We analyzed the impact of just using local trips on infection spread and infection risk potential generated from visitors' trips from various other states. We used the Diebold-Mariano test to compare across three machine learning models. Finally, we compared the performance of models, including visitor mobility for all the three waves in the United States and across all 50 states. \u003c/p\u003e\u003cp\u003eResults: We observe that visitor mobility impacts case growth and that including visitor mobility in forecasting the number of COVID-19 cases improves prediction accuracy by 34. We found the statistical significance with respect to the performance improvement resulting from including visitor mobility using the Diebold-Mariano test. We also observe that the significance was much higher during the first peak March to June 2020. \u003c/p\u003e\u003cp\u003eConclusion: With presence of cases everywhere (i.e. local and visitor), visitor mobility (even within the country) is shown to have significant impact on growth in number of cases. While it is not possible to account for other factors such as the impact of interventions, and differences in local mobility and visitor mobility, we find that these observations can be used to plan for both reopening and limiting visitors from regions where there are high number of cases.\u003c/p\u003e","manuscriptTitle":"COVID-19: Examining the case growth-rate due to Visitor vs Local Mobility using Machine Learning: A Study in the United States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-09-20 18:28:57","doi":"10.21203/rs.3.rs-916701/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":"68f94535-f728-451b-ae3e-861e3918aec6","owner":[],"postedDate":"September 20th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":7276692,"name":"Infectious Diseases"},{"id":7276693,"name":"Hospitality and Tourism"},{"id":7276694,"name":"Behavioral Geography"}],"tags":[],"updatedAt":"2021-09-20T18:28:57+00:00","versionOfRecord":[],"versionCreatedAt":"2021-09-20 18:28:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-916701","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-916701","identity":"rs-916701","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}