Deep Learning-Based Approaches for Early Diagnosis and Tracking of COVID-19

Deep Learning-Based Approaches for Early Diagnosis and Tracking of COVID-19 | 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 Article Deep Learning-Based Approaches for Early Diagnosis and Tracking of COVID-19 REDOUANE LHIADI, Taoufiq Amaanaoui, Abdelali Kaaouachi, Abdessamad Jaddar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4110808/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 COVID-19 pandemic has prompted governments and specialists worldwide to collaborate in search of effective solutions and strategies for containment and eventual societal recovery. With advancements in equipment capabilities, remote communications, and on-board/distributed computing, information-based technologies are playing an increasingly critical role in identifying, detecting, and diagnosing potential COVID-19 cases. This study aims to explore factors for early diagnosis, tracking, and identification of COVID-19 spread, focusing on data collection and discussing opportunities for improvement. The study recognizes that deep learning models are well-suited for mitigating the impact of COVID-19, given the availability of a large volume of pandemic data through various technologies and collaborative efforts. While deep learning and big data approaches may not have been extensively implemented or clinically tested, they offer quick responses and valuable insights to medical staff and decision-makers. However, designing deep learning algorithms for COVID-19 presents numerous challenges. The quality and quantity of COVID-19 datasets need further improvement, demanding ongoing efforts from the research community to enhance data quality and reliability. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing/Computational science Deep learning COVID-19 pandemic contact tracing deep learning algorithms big data Full Text Additional Declarations There is NO Competing Interest. 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. 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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-4110808","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":281101616,"identity":"02abee99-dc9a-4b29-b169-004caac87cc7","order_by":0,"name":"REDOUANE LHIADI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYBACPgnGBgYeAxAzASwgByIOPMCjhQ1dizFYSwJeLUCChwGhJbEBwcahRbq58cObAoZ8fvbkYxI/d9ikzw87/BBoi52cbgMOLTIHmyXnGDBYzux5libZeyYtd+PtNAOglmRjswO4HJbYIA30i4HBjRwzCd62w7kbZyeAtBxI3IZbS/NvkBZ7oBbJv23/0w1np38gpKUNYotEjpk0b9uBBHnpHIK2tFnOMZAwkDjzLNlati3ZcIN0TsGBBAPcfuGXSH98480fGwP+9uSDN9+22cnLz07f/OFDhZ0cLi1QIIFgGoBVGuBVjgbkG0hRPQpGwSgYBSMBAAD8/1rmZ0apSwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-6931-8979","institution":"National School of business and Management, University of Mohammed 1st","correspondingAuthor":true,"prefix":"","firstName":"REDOUANE","middleName":"","lastName":"LHIADI","suffix":""},{"id":281101617,"identity":"bd15dc8d-beac-45ba-86ab-23c8881f6658","order_by":1,"name":"Taoufiq Amaanaoui","email":"","orcid":"","institution":"National School of business and Management, University of Mohammed 1st","correspondingAuthor":false,"prefix":"","firstName":"Taoufiq","middleName":"","lastName":"Amaanaoui","suffix":""},{"id":281101618,"identity":"4b16c23b-8872-475f-8e63-98bda68d33b8","order_by":2,"name":"Abdelali Kaaouachi","email":"","orcid":"","institution":"National School of business and Management, University of Mohammed 1st","correspondingAuthor":false,"prefix":"","firstName":"Abdelali","middleName":"","lastName":"Kaaouachi","suffix":""},{"id":281101619,"identity":"ffde2e4d-1e85-4dce-873e-50930350465f","order_by":3,"name":"Abdessamad Jaddar","email":"","orcid":"","institution":"National School of business and Management, University of Mohammed 1st","correspondingAuthor":false,"prefix":"","firstName":"Abdessamad","middleName":"","lastName":"Jaddar","suffix":""}],"badges":[],"createdAt":"2024-03-16 01:35:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4110808/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4110808/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52988918,"identity":"975f455f-415c-4255-bad1-54577b88ee18","added_by":"auto","created_at":"2024-03-19 11:38:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":335175,"visible":true,"origin":"","legend":"","description":"","filename":"DeepLearningBasedApproachesforEarlyDiagnosisandTrackingofCOVID19.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4110808/v1_covered_2add986a-6261-4e27-b39b-c3c93b486296.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Deep Learning-Based Approaches for Early Diagnosis and Tracking of COVID-19","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":"Deep learning,COVID-19 pandemic, contact tracing, deep learning algorithms, big data","lastPublishedDoi":"10.21203/rs.3.rs-4110808/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4110808/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The COVID-19 pandemic has prompted governments and specialists worldwide to collaborate in search of effective solutions and strategies for containment and eventual societal recovery. 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