Prediction and Spatiotemporal Heterogeneity Pulmonary Tuberculosis in Iran using Geographically Weighted Machine Learning

Prediction and Spatiotemporal Heterogeneity Pulmonary Tuberculosis in Iran using Geographically Weighted Machine Learning | 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 Prediction and Spatiotemporal Heterogeneity Pulmonary Tuberculosis in Iran using Geographically Weighted Machine Learning Saber Ghaffari fam, Leili Tapak, Erfan Ayubi, Mahshid Nasehi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8406119/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Spatial analyses of pulmonary tuberculosis (PTB) have garnered significant attention due to the inherent spatial dependence and heterogeneity of this infectious disease. In the present study, we employed the Geographically Weighted Random Forest (GWRF) model to rigorously evaluate the effects of meteorological variables and the Human Development Index (HDI) on PTB incidence throughout the study period, in addition, we predicted the incidence of PTB over the next five years. Methods This study utilizes publicly available PTB incidence data from 31 provinces of Iran spanning 2009 to 2023. We employed the GWRF model to investigate the local associations between the standardized incidence ratio (SIR) of PTB and various influencing factors, including the HDI, temperature, relative humidity, dew temperature, and wet temperature, all obtained from multiple data sources. Results Temperature showed a stronger influence in the southern and northern regions, while HDI exhibited high importance in several southern and central provinces. In addition, humidity demonstrated localized effects, particularly in southern and eastern areas. prediction analyses indicated an increasing trend in PTB incidence in provinces such as Qom, Kerman, and Ilam over the next five years, whereas a declining trend is anticipated in provinces including Sistan and Baluchestan, Kermanshah, and North Khorasan. Conclusions These findings highlight the critical role of spatially varying metrological and socioeconomic factors in shaping PTB incidence and underscore the need for region-specific prevention and control strategies. Pulmonary Tuberculosis Spatial machine learning Meteorological Human Developmental Index Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 29 Dec, 2025 Reviewers agreed at journal 29 Dec, 2025 Reviewers agreed at journal 29 Dec, 2025 Reviewers agreed at journal 29 Dec, 2025 Reviewers invited by journal 29 Dec, 2025 Editor assigned by journal 29 Dec, 2025 Editor invited by journal 29 Dec, 2025 Submission checks completed at journal 25 Dec, 2025 First submitted to journal 25 Dec, 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. 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-8406119","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":567051525,"identity":"4448c790-0aa2-419b-9352-488c6306e0ac","order_by":0,"name":"Saber Ghaffari fam","email":"","orcid":"","institution":"Hamadan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Saber","middleName":"Ghaffari","lastName":"fam","suffix":""},{"id":567051526,"identity":"42de823c-4a23-44da-8f8f-a6ba08e4f096","order_by":1,"name":"Leili Tapak","email":"","orcid":"","institution":"Hamadan University of Medical 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Learning\u003c/strong\u003e\u003c/p\u003e","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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pulmonary Tuberculosis, Spatial machine learning, Meteorological, Human Developmental Index","lastPublishedDoi":"10.21203/rs.3.rs-8406119/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8406119/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpatial analyses of pulmonary tuberculosis (PTB) have garnered significant attention due to the inherent spatial dependence and heterogeneity of this infectious disease. In the present study, we employed the Geographically Weighted Random Forest (GWRF) model to rigorously evaluate the effects of meteorological variables and the Human Development Index (HDI) on PTB incidence throughout the study period, \u003cem\u003ein addition, we predicted the incidence of PTB over the next five years.\u003c/em\u003e \u003cstrong\u003eMethods\u003c/strong\u003e This study utilizes publicly available PTB incidence data from 31 provinces of Iran spanning 2009 to 2023. We employed the GWRF model to investigate the local associations between the standardized incidence ratio (SIR) of PTB and various influencing factors, including the HDI, temperature, relative humidity, dew temperature, and wet temperature, all obtained from multiple data sources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTemperature showed a stronger influence in the southern and northern regions, while HDI exhibited high importance in several southern and central provinces. 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