COVID-19 spreading prediction model based on a multi-head self-attention mechanism

COVID-19 spreading prediction model based on a multi-head self-attention mechanism | 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 spreading prediction model based on a multi-head self-attention mechanism Can Zhang, GengXin Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4051560/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 The COVID-19 pandemic is one of the most severe global health epidemics in recent decades. Its consequences have affected hundreds of millions of people in countries around the world because of the high contagiousness and mortality rate. Results To further improve the prediction accuracy of the long-term spreading trend of COVID-19, this paper proposes a hybrid neural network prediction model based on a bidirectional long short-term memory network (Bi-LSTM) combined with a multi-head self-attention mechanism. To achieve long-term prediction, this model combines multiple linear regression with the improved susceptible-exposed-infected-recovered (SEIR) model. The bidirectional long short-term memory network can mine important features of input data in both forward and backward directions, and the multi-head self-attention mechanism can capture different attention information to improve the expression ability of the model and help improve the prediction performance. The comparative analysis and prediction of multiple models are based on official real data. Conclusion The experimental results show that compared with the long short-term memory network (LSTM) and single chamber model, the proposed COVID-19 spreading model can achieve higher prediction accuracy. COVID-19 long short-term memory multi-head self-attention infectious diseases prediction model Full Text Additional Declarations No competing interests reported. Supplementary Files data.zip 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-4051560","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":286354387,"identity":"1698e106-d24e-49df-83c1-f1d7cc6b9084","order_by":0,"name":"Can Zhang","email":"","orcid":"","institution":"Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Can","middleName":"","lastName":"Zhang","suffix":""},{"id":286354388,"identity":"da417e93-a8cb-4137-ac3e-6c9d5dc90541","order_by":1,"name":"GengXin Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDADNgbmAww8INYB4rWwJZCohYGBx4A4LfIzcg+/+LijNrFPuufjjbdtDHJ8NxIYPxfg0WJwIy/NcuaZ48ZsMmc3W85tYzCWvJHALD0DnxaJHDNj3rZjcmwSudukedsYEjfcSGBj5sHrMKCWv23HeNgkcp6BtNQT1MJwI8f4MWNbDdCWHDaQlgQDQloMzrwxY+xtO2DMJpFmbDnnnIThzDMPm6XxOqw9x/jDz7a6xPkzkh/eeFNmI893PPngZ7wOA0aiBAPDYTBLAowYGBvwa2BgYP7AwFAH0zIKRsEoGAWjABMAABPWSXG/HkIOAAAAAElFTkSuQmCC","orcid":"","institution":"Qingdao University","correspondingAuthor":true,"prefix":"","firstName":"GengXin","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2024-03-09 04:31:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4051560/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4051560/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54360365,"identity":"a791dba4-6b3c-44f4-810e-9db6a23e3e27","added_by":"auto","created_at":"2024-04-09 10:59:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":894499,"visible":true,"origin":"","legend":"","description":"","filename":"COVID19fusionpredictionmodelbasedonmultiheadselfattentionmechanism.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4051560/v1_covered_83f28f3a-d301-47a9-9aa5-f3cfbb046b0b.pdf"},{"id":54137472,"identity":"e8a376c4-d68b-4329-8bb3-feed3c5d3045","added_by":"auto","created_at":"2024-04-05 06:49:55","extension":"zip","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":39330,"visible":true,"origin":"","legend":"","description":"","filename":"data.zip","url":"https://assets-eu.researchsquare.com/files/rs-4051560/v1/fb5f5efe770d699673538a1f.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"COVID-19 spreading prediction model based on a multi-head self-attention mechanism","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"COVID-19, long short-term memory, multi-head self-attention, infectious diseases, prediction model","lastPublishedDoi":"10.21203/rs.3.rs-4051560/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4051560/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe COVID-19 pandemic is one of the most severe global health epidemics in recent decades. 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