Development of an epidemiological model SEIasIsyIcrHpUicDthPiVacRS for human-pathogen interactions

Development of an epidemiological model SEIasIsyIcrHpUicDthPiVacRS for human-pathogen interactions | 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 Development of an epidemiological model SEI as I sy I cr H p U ic D th P i V ac RS for human-pathogen interactions Raj Kumar, Shiv Kumar Yadav, Manish Kumar Gupta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8797522/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 study presents a novel epidemiological model $SEI_{as}I_{sy}I_{cr}H_pU_{ic}D_{th}P_{i}V_{ac}RS$ to analyse the human-pathogen transmission dynamics by incorporating multiple parameters of human-pathogen interactions, multiple disease compartments, and vaccination effects. The model is governed by a system of differential equations and solved by exploring the fourth-order Runge-Kutta method. The impact of variation in the epidemiological parameters, such as transmission rates ($\beta_1, \beta_2, \beta_3$), vaccination efficacy ($\sigma$), and the proportion of asymptomatic cases ($\delta_{as}$), has been analyzed to understand the transmission and control of a viral disease. The model is an extension of an earlier study in which natural death rate, birthrate, vaccination, and re-susceptible cases were not incorporated in the earlier study. The model has been validated using a larger population dataset from Uttar Pradesh, India, compared to that of Kenya, by investigating the transmission dynamics of coronavirus across a wider range of compartments. The framework is also adaptable to other epidemic diseases like SARS, monkeypox, swine flu, influenza, etc. The observation in this study reveals that non-pharmaceutical interventions reduce transmission rates by $20-30$% significantly delay infection peaks, reduce hospitalizations up to $71.7$%, and alleviate health-care burdens. The study highlights the critical role of vaccination in mitigating severe outcomes, with higher coverage reducing symptomatic and critical cases. The findings emphasize the importance of early detection of asymptomatic carriers, dynamic public health policies, and healthcare system preparedness in controlling pathogen spread, providing intriguing perspectives on epidemic management in resource-limited settings. This study enhances pathogen spread understanding, identifies optimal controls, and supports dynamic policies like early asymptomatic detection and equity-focused vaccine distribution. The study also suggests future work on sensitivity analysis ($\tau_{as}$, $\lambda_{uic}$), ML/GIS hybrids for spatial/multi-strain modelling, and optimal control for low-resource cost-effectiveness. SEIasIsyIcrHpUicDthPiVacRS model human pathogens epidemic stability analysis vaccination Full Text Additional Declarations No competing interests reported. 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-8797522","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590887768,"identity":"a16427ed-d7f7-4a6f-b486-526dc1faa1b7","order_by":0,"name":"Raj Kumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYLCCBAYGZn72BiDLwIIELZI9B0BaJEiwyeBGAogiQot5/+FjDx7usWNnuPn86oYfBRIM/O3dCXi1yNxISzdIeJbMzDg7p+xmD9BhEmfObsCrRUKCx0wi4QAzM7N0TtoNHqAWA4lcAlr4z4C01DOzSZ5Ju/mHKC0MOSAth5l5JNiP3SbOFom0NKCW48wSPDlst2UMJHgI+4X/8DHJHweqk+2PH392880fGzn+9l78WmAgmYGBxwDE4CFKOQjYMTCwPyBa9SgYBaNgFIwsAACir0GmRPhSOAAAAABJRU5ErkJggg==","orcid":"","institution":"Veer Bahadur Singh Purvanchal University","correspondingAuthor":true,"prefix":"","firstName":"Raj","middleName":"","lastName":"Kumar","suffix":""},{"id":590887769,"identity":"06f52c61-ea6f-4eab-8111-b370d3a8a149","order_by":1,"name":"Shiv Kumar Yadav","email":"","orcid":"","institution":"Veer Bahadur Singh Purvanchal University","correspondingAuthor":false,"prefix":"","firstName":"Shiv","middleName":"Kumar","lastName":"Yadav","suffix":""},{"id":590887770,"identity":"031a9c35-6a66-4947-a088-d106f9ad6006","order_by":2,"name":"Manish Kumar Gupta","email":"","orcid":"","institution":"Veer Bahadur Singh Purvanchal University","correspondingAuthor":false,"prefix":"","firstName":"Manish","middleName":"Kumar","lastName":"Gupta","suffix":""}],"badges":[],"createdAt":"2026-02-05 13:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8797522/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8797522/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106404344,"identity":"5fe844e9-e1cd-4502-bf71-3cf459088513","added_by":"auto","created_at":"2026-04-08 09:15:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1625932,"visible":true,"origin":"","legend":"","description":"","filename":"Rajsinglemanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8797522/v1_covered_8112b5cd-57ce-4b3f-8f3e-edf4e133469e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDevelopment of an epidemiological model \u003cem\u003eSEI\u003c/em\u003e\u003csub\u003e\u003cem\u003eas\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003esy\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003ecr\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eH\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eic\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eD\u003c/em\u003e\u003csub\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eac\u003c/em\u003e\u003c/sub\u003e\u003cem\u003eRS\u003c/em\u003e for human-pathogen interactions\u003c/p\u003e","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":"SEIasIsyIcrHpUicDthPiVacRS model, human pathogens, epidemic, stability analysis, vaccination","lastPublishedDoi":"10.21203/rs.3.rs-8797522/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8797522/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The study presents a novel epidemiological model $SEI_{as}I_{sy}I_{cr}H_pU_{ic}D_{th}P_{i}V_{ac}RS$ to analyse the human-pathogen transmission dynamics by incorporating multiple parameters of human-pathogen interactions, multiple disease compartments, and vaccination effects. The model is governed by a system of differential equations and solved by exploring the fourth-order Runge-Kutta method. The impact of variation in the epidemiological parameters, such as transmission rates ($\\beta_1, \\beta_2, \\beta_3$), vaccination efficacy ($\\sigma$), and the proportion of asymptomatic cases ($\\delta_{as}$), has been analyzed to understand the transmission and control of a viral disease. The model is an extension of an earlier study in which natural death rate, birthrate, vaccination, and re-susceptible cases were not incorporated in the earlier study. The model has been validated using a larger population dataset from Uttar Pradesh, India, compared to that of Kenya, by investigating the transmission dynamics of coronavirus across a wider range of compartments. The framework is also adaptable to other epidemic diseases like SARS, monkeypox, swine flu, influenza, etc. The observation in this study reveals that non-pharmaceutical interventions reduce transmission rates by $20-30$\\% significantly delay infection peaks, reduce hospitalizations up to $71.7$\\%, and alleviate health-care burdens. The study highlights the critical role of vaccination in mitigating severe outcomes, with higher coverage reducing symptomatic and critical cases. The findings emphasize the importance of early detection of asymptomatic carriers, dynamic public health policies, and healthcare system preparedness in controlling pathogen spread, providing intriguing perspectives on epidemic management in resource-limited settings. This study enhances pathogen spread understanding, identifies optimal controls, and supports dynamic policies like early asymptomatic detection and equity-focused vaccine distribution. The study also suggests future work on sensitivity analysis ($\\tau_{as}$, $\\lambda_{uic}$), ML/GIS hybrids for spatial/multi-strain modelling, and optimal control for low-resource cost-effectiveness.","manuscriptTitle":"Development of an epidemiological model SEIasIsyIcrHpUicDthPiVacRS for human-pathogen interactions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 06:35:15","doi":"10.21203/rs.3.rs-8797522/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":"a8ba53e8-98f1-43e6-af40-0f1804f084a5","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T18:10:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 06:35:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8797522","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8797522","identity":"rs-8797522","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}