Geospatial based Comparison of malaria and climate variability in Dar es salaam Using GAM and Random Forest

Geospatial based Comparison of malaria and climate variability in Dar es salaam Using GAM and Random Forest | 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 Geospatial based Comparison of malaria and climate variability in Dar es salaam Using GAM and Random Forest Edmund Kanjagaile, Dorothea Deus This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8871163/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 Background: Malaria is public health burden in tropical and subtropical regions like Dar es Salaam, such that climate variability exerts a major influence on malaria transmission dynamics. Traditional field-based mapping and modeling techniques are often limited by expense, spatial incompleteness, and reliance on linear or non-linear systems. These gaps resolved by applying a geospatially integrated models to analyze compounded effects of climate variables on malaria incidence. Methods: The research utilized confirmed malaria case data from 2015 to 2023 from 1,794 health facilities, alongside climate variables (temperature, rainfall, humidity) from TerraClimate cross-verified with TMA records. Bilinear interpolation and inverse distance weighting applied to both climate variables and Malaria for spatial compatibility a mapping. The GAM applied to identify non-linear relationships, while RF models to examine linear as well as non-linear relationships. Partial dependence plots and Pearson correlations were used to evaluate model fit and climate impact. Results: Malaria cases decreased over time, but spatial heterogeneity persisted. Within 2015, 2019 and 2023 GAM explained 23.4%, 39.1%, and 40.2% deviance respectively, with partial correlation of 0.484, 0.626, and 0.634. Given that temperature increases the influence over time, with effective degrees of freedom rising from 8.287 to 8.828. RF models were superior to GAM, forecasting 71.07%, 71.36%, and 74.37% of variance for corresponding years, and with Pearson correlations of 0.977, 0.975, and 0.980. Partial dependence plots showed strong positive associations of malaria cases with temperature (26.4 to 27.2°C) and rainfall (75 to 130 mm), and a more complex, typically inverse relationship of humidity (20.5 to 22.0 mmHg). Conclusions: The Random Forest model was more highly predictive than GAM, confirming the reality of malaria transmission in Dar es Salaam being moderated by linear and non-linear relationships with climate factors. These results identify geospatially integrated modeling paradigms to be more effective to enhance malaria surveillance and inform climate-resilient public health policy. Climate Analysis and Modeling Climate Variability Malaria General Adaptive Model Random Forest Geospatial Linear and Non-linear relationship Early warning system Full Text Additional Declarations The authors declare no competing interests. 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. 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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-8871163","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590838938,"identity":"75233933-ad39-46dc-8107-d0bfa501891f","order_by":0,"name":"Edmund Kanjagaile","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYDACCQTTgIGhAkgxMzfg1cED0nIAruUMSAsjKVoY20A0AS320s2HP3+oOCzHwN68dcPHebXR/O1ALT8qtuG2ReZYmsSBM4eNGXiOld2cue147ozDjA2MPWdu43FYjhnDwba0xAYg4zbvtmO5DUAtzIxt+LTkf/5w8F9afYP8G6CWOcdy5xPWksMgcbDBJoFBggeopaEmdwNBLTfSzCTOHLMxbONJK7s549iB3I1ALQfx+YV9RvLjDxU1EvL87Ie33fhQU5c77/zhgw9+VODWAgdsEOowmDxAWD0C1JGieBSMglEwCkYIAAAFClx2t5f25wAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0008-2431-2144","institution":"ClimateMedix Foundation","correspondingAuthor":true,"prefix":"","firstName":"Edmund","middleName":"","lastName":"Kanjagaile","suffix":""},{"id":590838939,"identity":"8fb94568-0647-47c4-9ec6-cef96971cf51","order_by":1,"name":"Dorothea Deus","email":"","orcid":"","institution":"Ardhi University","correspondingAuthor":false,"prefix":"","firstName":"Dorothea","middleName":"","lastName":"Deus","suffix":""}],"badges":[],"createdAt":"2026-02-13 11:14:37","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8871163/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8871163/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103049719,"identity":"66cbb41c-256f-4ff8-91be-94c730149b1e","added_by":"auto","created_at":"2026-02-20 07:45:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1442959,"visible":true,"origin":"","legend":"","description":"","filename":"Paper2MSc.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8871163/v1_covered_7de99246-aaad-4302-bbcb-836919293dbd.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGeospatial based Comparison of malaria and climate variability in Dar es salaam Using GAM and Random Forest\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"ClimateMedix Foundation","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Climate Variability, Malaria, General Adaptive Model, Random Forest, Geospatial, Linear and Non-linear relationship, Early warning system","lastPublishedDoi":"10.21203/rs.3.rs-8871163/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8871163/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u0026nbsp;Malaria is public health burden in tropical and subtropical regions like Dar es Salaam, such that climate variability exerts a major influence on malaria transmission dynamics. Traditional field-based mapping and modeling techniques are often limited by expense, spatial incompleteness, and reliance on linear or non-linear systems. These gaps resolved by applying a geospatially integrated models to analyze compounded effects of climate variables on malaria incidence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u0026nbsp;The research utilized confirmed malaria case data from 2015 to 2023 from 1,794 health facilities, alongside climate variables (temperature, rainfall, humidity) from TerraClimate cross-verified with TMA records. Bilinear interpolation and inverse distance weighting applied to both climate variables and Malaria for spatial compatibility a mapping. The GAM applied to identify non-linear relationships, while RF models to examine linear as well as non-linear relationships. Partial dependence plots and Pearson correlations were used to evaluate model fit and climate impact.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eMalaria cases decreased over time, but spatial heterogeneity persisted. Within 2015, 2019 and 2023 GAM explained 23.4%, 39.1%, and 40.2% deviance respectively, with partial correlation of 0.484, 0.626, and 0.634. Given that temperature increases the influence over time, with effective degrees of freedom rising from 8.287 to 8.828. RF models were superior to GAM, forecasting 71.07%, 71.36%, and 74.37% of variance for corresponding years, and with Pearson correlations of 0.977, 0.975, and 0.980. Partial dependence plots showed strong positive associations of malaria cases with temperature (26.4 to 27.2°C) and rainfall (75 to 130 mm), and a more complex, typically inverse relationship of humidity (20.5 to 22.0 mmHg).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u0026nbsp;The Random Forest model was more highly predictive than GAM, confirming the reality of malaria transmission in Dar es Salaam being moderated by linear and non-linear relationships with climate factors. These results identify geospatially integrated modeling paradigms to be more effective to enhance malaria surveillance and inform climate-resilient public health policy.\u003c/p\u003e","manuscriptTitle":"Geospatial based Comparison of malaria and climate variability in Dar es salaam Using GAM and Random Forest","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 05:55:36","doi":"10.21203/rs.3.rs-8871163/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":"ce3a71ba-8ef3-4e46-852d-0063466b8e4a","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62875572,"name":"Climate Analysis and Modeling"}],"tags":[],"updatedAt":"2026-02-17T02:04:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 05:55:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8871163","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8871163","identity":"rs-8871163","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}