A Novel Approach Considering Spatial Heterogeneity in Tuberculosis Prediction of China

A Novel Approach Considering Spatial Heterogeneity in Tuberculosis Prediction of China | 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 Novel Approach Considering Spatial Heterogeneity in Tuberculosis Prediction of China Xian Zhang, Xiaohua Ni, Qianqian Zhang, Xiaowei Zheng, Nazrullozoda Sulaimon, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8690819/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Background Present research has seldom comprehensively considered the impact factors’ spatial heterogeneities of tuberculosis(TB) in constructing artificial intelligence(AI) model. Objective This study aimed to construct optimal simulation and prediction models for TB in different provinces of mainland China, based on a novel modeling framework for integrating the spatial heterogeneities of both impact factors and artificial intelligence algorithms. Main results will hold significant value for achieving the global target of ending TB under Sustainable Development Goal 3.3. Methods Monthly TB incidence rate, six meteorological factors and three air quality factors are obtained from 31 provinces during 2004–2020. The spatiotemporal variations of TB and the corresponding impact factors were analysed for linear trends. Spearman's rank correlation analysis was used to quantify the relationships between TB and the impact factors. Significantly correlated factors were selected to build a model library that incorporates the spatial heterogeneity of predictors and six AI models. The Distance Between Indices of Simulation and Observation (DISO) was used to evaluate the comprehensive performance of models, and obtain the optimal one. Results TB incidence exhibited a significant declining trend during 2004–2020 (2.42 per 100,000, p < 0.05). The monthly incidence was significantly positively correlated with PM 2.5 (3-month lag, r = 0.72), PM 10 (3-month lag, r = 0.73), and pressure(PRE, 3-month lag, r = 0.66)(p < 0.05). It was significantly negatively correlated with O 3 (3-month lag, r=-0.49), temperature (TEM, 3-month lag, r=-0.46), relative humidity (RHU, 3-month lag, r=-0.39), precipitation (PRE, 3-month lag, r=-0.46), and sunshine duration (SSD, 3-month lag, r=-0.30) (p < 0.05). Among the AI models, the Autoregressive Integrated Moving Average with Exogenous variables(ARIMAX) demonstrated the best performance in Inner Mongolia (DISO = 0.20), while Random Forest(RF) was most prominent in Qinghai Province (DISO = 0.24). Conclusion In different provinces, ARIMAX and RF demonstrated superior predictive performance, which provide a valuable scientific foundation for prevention and control. Tuberculosis Spatiotemporal Distribution Influencing Factors AI models Spatial Heterogeneity Optimal Prediction Full Text Additional Declarations No competing interests reported. Supplementary Files Supportinginformation0125.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 24 Mar, 2026 Reviews received at journal 09 Mar, 2026 Reviewers agreed at journal 05 Mar, 2026 Reviews received at journal 24 Feb, 2026 Reviews received at journal 19 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers invited by journal 08 Feb, 2026 Editor invited by journal 02 Feb, 2026 Editor assigned by journal 30 Jan, 2026 Submission checks completed at journal 30 Jan, 2026 First submitted to journal 25 Jan, 2026 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-8690819","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588997255,"identity":"37674903-ef03-4b30-bc9c-97ed1590bc36","order_by":0,"name":"Xian Zhang","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xian","middleName":"","lastName":"Zhang","suffix":""},{"id":588997256,"identity":"9fe6c29a-0a70-4de6-ab5e-160a0b97ec63","order_by":1,"name":"Xiaohua Ni","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of 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Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Tuberculosis Spatiotemporal Distribution, Influencing Factors, AI models, Spatial Heterogeneity, Optimal Prediction","lastPublishedDoi":"10.21203/rs.3.rs-8690819/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8690819/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePresent research has seldom comprehensively considered the impact factors\u0026rsquo; spatial heterogeneities of tuberculosis(TB) in constructing artificial intelligence(AI) model.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to construct optimal simulation and prediction models for TB in different provinces of mainland China, based on a novel modeling framework for integrating the spatial heterogeneities of both impact factors and artificial intelligence algorithms. Main results will hold significant value for achieving the global target of ending TB under Sustainable Development Goal 3.3.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eMonthly TB incidence rate, six meteorological factors and three air quality factors are obtained from 31 provinces during 2004\u0026ndash;2020. The spatiotemporal variations of TB and the corresponding impact factors were analysed for linear trends. Spearman's rank correlation analysis was used to quantify the relationships between TB and the impact factors. Significantly correlated factors were selected to build a model library that incorporates the spatial heterogeneity of predictors and six AI models. The Distance Between Indices of Simulation and Observation (DISO) was used to evaluate the comprehensive performance of models, and obtain the optimal one.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTB incidence exhibited a significant declining trend during 2004\u0026ndash;2020 (2.42 per 100,000, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The monthly incidence was significantly positively correlated with PM\u003csub\u003e2.5\u003c/sub\u003e(3-month lag, r\u0026thinsp;=\u0026thinsp;0.72), PM\u003csub\u003e10\u003c/sub\u003e(3-month lag, r\u0026thinsp;=\u0026thinsp;0.73), and pressure(PRE, 3-month lag, r\u0026thinsp;=\u0026thinsp;0.66)(p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). It was significantly negatively correlated with O\u003csub\u003e3\u003c/sub\u003e (3-month lag, r=-0.49), temperature (TEM, 3-month lag, r=-0.46), relative humidity (RHU, 3-month lag, r=-0.39), precipitation (PRE, 3-month lag, r=-0.46), and sunshine duration (SSD, 3-month lag, r=-0.30) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among the AI models, the Autoregressive Integrated Moving Average with Exogenous variables(ARIMAX) demonstrated the best performance in Inner Mongolia (DISO\u0026thinsp;=\u0026thinsp;0.20), while Random Forest(RF) was most prominent in Qinghai Province (DISO\u0026thinsp;=\u0026thinsp;0.24).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn different provinces, ARIMAX and RF demonstrated superior predictive performance, which provide a valuable scientific foundation for prevention and control.\u003c/p\u003e","manuscriptTitle":"A Novel Approach Considering Spatial Heterogeneity in Tuberculosis Prediction of China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 19:32:46","doi":"10.21203/rs.3.rs-8690819/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-24T04:44:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-09T05:45:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305102344538590146620894518486188162310","date":"2026-03-05T08:08:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-25T01:11:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-20T03:02:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92834357424290900898445401637308609107","date":"2026-02-09T02:16:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188842453824204607398312096570652152911","date":"2026-02-09T00:00:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62839925965640998325806552102764552926","date":"2026-02-08T14:50:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-08T13:14:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-02T06:14:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-30T06:24:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-30T06:20:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-01-25T07:17:32+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"a4d3e74a-2b5f-4fcc-8c4d-a08ec9ddec58","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-24T04:55:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 19:32:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8690819","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8690819","identity":"rs-8690819","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}