A systematic literature review of artificial intelligence methods applied to the Human Epidemic (Covid-19)

A systematic literature review of artificial intelligence methods applied to the Human Epidemic (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 Research Article A systematic literature review of artificial intelligence methods applied to the Human Epidemic (Covid-19) Arman Kavoosi Ghafi, Issa Khodadadi, Aidin Tofangdarzade, Ali Pirkhedri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7988486/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract The COVID-19 pandemic placed unprecedented pressure on healthcare systems worldwide and accelerated the use of artificial intelligence (AI) in areas such as diagnostics, forecasting, treatment, and disease monitoring. To better understand this trend, we carried out a systematic literature review following PRISMA guidelines. Our review covered studies released between December 2019 and January 2024, drawing from major bibliographic databases and preprint servers. For the search, we applied the keywords (“Artificial Intelligence” OR “Machine Learning” OR “Deep Learning”) AND (“COVID-19” OR “Coronavirus” OR “Pandemic”). Out of approximately 110 retrieved records, 57 studies satisfied the predefined inclusion criteria and were evaluated using the CASP framework. The final body of literature was distributed as follows: disease detection and prediction accounted for about 44% (23 studies), drug and vaccine discovery for 25%, remote healthcare and IoT applications for 19% (10 studies), online social network (OSN) analytics for 4%, and general or multi-modal AI frameworks for 8%. Deep learning models applied to chest CT scans, X-rays, and RT-PCR enhancement frequently reported internal diagnostic accuracies above 90%. Meanwhile, natural language processing and embedding-based OSN methods occasionally identified symptomatic trends several days ahead of official case reports. Despite these advances, common challenges persisted, including heterogeneous datasets, insufficient external validation, and ongoing privacy and ethical concerns. Looking forward, we recommend that future research emphasize multimodal integration of clinical and social media data, establish standardized external benchmarks, adopt explainable AI (XAI) methods, and explore privacy-preserving strategies such as federated learning to strengthen generalizability and promote equitable deployment. artificial intelligence deep learning medical imaging forecasting federated learning social media analytics explainable AI Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 27 Jan, 2026 Reviews received at journal 20 Jan, 2026 Reviews received at journal 16 Jan, 2026 Reviewers agreed at journal 23 Dec, 2025 Reviewers agreed at journal 22 Dec, 2025 Reviews received at journal 04 Dec, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers invited by journal 07 Nov, 2025 Editor assigned by journal 05 Nov, 2025 Submission checks completed at journal 30 Oct, 2025 First submitted to journal 30 Oct, 2025 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-7988486","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":541893807,"identity":"73f5605a-6bde-426a-89d1-c9927dbf58f6","order_by":0,"name":"Arman Kavoosi 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(Covid-19)","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"applied-network-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apns","sideBox":"Learn more about [Applied Network Science](http://appliednetsci.springeropen.com/)","snPcode":"41109","submissionUrl":"https://submission.nature.com/new-submission/41109/3","title":"Applied Network Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"artificial intelligence, deep learning, medical imaging, forecasting, federated learning, social media analytics, explainable AI","lastPublishedDoi":"10.21203/rs.3.rs-7988486/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7988486/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe COVID-19 pandemic placed unprecedented pressure on healthcare systems worldwide and accelerated the use of artificial intelligence (AI) in areas such as diagnostics, forecasting, treatment, and disease monitoring. To better understand this trend, we carried out a systematic literature review following PRISMA guidelines. Our review covered studies released between December 2019 and January 2024, drawing from major bibliographic databases and preprint servers. For the search, we applied the keywords (\u0026ldquo;Artificial Intelligence\u0026rdquo; OR \u0026ldquo;Machine Learning\u0026rdquo; OR \u0026ldquo;Deep Learning\u0026rdquo;) AND (\u0026ldquo;COVID-19\u0026rdquo; OR \u0026ldquo;Coronavirus\u0026rdquo; OR \u0026ldquo;Pandemic\u0026rdquo;). Out of approximately 110 retrieved records, 57 studies satisfied the predefined inclusion criteria and were evaluated using the CASP framework. The final body of literature was distributed as follows: disease detection and prediction accounted for about 44% (23 studies), drug and vaccine discovery for 25%, remote healthcare and IoT applications for 19% (10 studies), online social network (OSN) analytics for 4%, and general or multi-modal AI frameworks for 8%. Deep learning models applied to chest CT scans, X-rays, and RT-PCR enhancement frequently reported internal diagnostic accuracies above 90%. Meanwhile, natural language processing and embedding-based OSN methods occasionally identified symptomatic trends several days ahead of official case reports. Despite these advances, common challenges persisted, including heterogeneous datasets, insufficient external validation, and ongoing privacy and ethical concerns. Looking forward, we recommend that future research emphasize multimodal integration of clinical and social media data, establish standardized external benchmarks, adopt explainable AI (XAI) methods, and explore privacy-preserving strategies such as federated learning to strengthen generalizability and promote equitable deployment.\u003c/p\u003e","manuscriptTitle":"A systematic literature review of artificial intelligence methods applied to the Human Epidemic (Covid-19)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 14:41:09","doi":"10.21203/rs.3.rs-7988486/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-27T09:13:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-20T05:07:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-16T16:56:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202461516394090881924237800820472834807","date":"2025-12-24T00:37:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184179403352031593289570289334979409076","date":"2025-12-23T01:47:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-04T22:33:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9397731027038760254654546721456099471","date":"2025-11-07T22:09:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38928250660091006334402007013703423526","date":"2025-11-07T10:56:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-07T09:50:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-05T16:31:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-31T00:00:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Applied Network Science","date":"2025-10-30T10:51:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"applied-network-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apns","sideBox":"Learn more about [Applied Network Science](http://appliednetsci.springeropen.com/)","snPcode":"41109","submissionUrl":"https://submission.nature.com/new-submission/41109/3","title":"Applied Network Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"089ca4a0-90da-48d8-b333-f2cc29e6ab62","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-20T17:38:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 14:41:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7988486","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7988486","identity":"rs-7988486","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}