Geospatial Based Surveillance of Malaria Risk in Dar es Salaam Using a Hybrid 3DCNN+LSTM and CA Model

Geospatial Based Surveillance of Malaria Risk in Dar es Salaam Using a Hybrid 3DCNN+LSTM and CA Model | 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 Surveillance of Malaria Risk in Dar es Salaam Using a Hybrid 3DCNN+LSTM and CA Model Edmund Kanjagaile, Dorothea Deus, Anastazia Msusa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8870814/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 Malaria remains as a significant public health burden in tropical and subtropical regions facing challenges in efficiently identifying and predicting risk areas. Conventional field‑survey methods used for mapping of Anopheles breeding sites are often costly, time‑consuming, and spatially incomplete. Therefore, therefore is a pressing need for a geospatially integrated surveillance framework for accurately mapping malaria risk and forecasting future risk dynamics to support targeted control efforts. A geospatial hybrid-modeling framework developed by integrating multi‑source remote sensing, Malaria and Climate datasets. Random Forest model employed solely to determine the relative importance of input variables, subsequently weighted and selected for inclusion in a deep learning architecture. The predictive model combined 3D Convolution Neural Network for capturing spatial patterns with a Long-Short Term Memory to learn temporal dynamics. The model trained against a baseline mean squared error (MSE) of 0.1. To improve spatial realism in the final risk maps, a Cellular Automata (CA) model incorporated using a 3×3 Moore neighborhood structure, with parameters calibrated at γ = 0.293 and β = 43.9 to enhance the spatial propagation of risks across neighboring cells. The framework successfully mapped malaria risks in Dar es Salaam with a Spearman correlation of 0.92 while Kigamboni South, Tundwi, and Msongola identified as high-risk areas. The hybrid 3DCNN–LSTM model prediction performance reduced training and validation losses by 97.3% and 90.2% respectively from the baseline with test MSE of 0.005. Prediction to 2060 exhibited a steady annual spatial increase in malaria risk of approximately 0.0022 risk units (slope = 0.077), demonstrating the model's ability for future risk estimation. Geographic Information Systems Malaria risk geospatial surveillance remote sensing 3D Convolutional Neural Network (3DCNN) Long Short-Term Memory (LSTM) Cellular Automata (CA) Random Forest climate variability deep learning 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. 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-8870814","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590821583,"identity":"14c89ef6-7600-46da-859f-aee9df7ac128","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":590821584,"identity":"6cabe86c-1859-43ba-8f7c-d248b7ee1c40","order_by":1,"name":"Dorothea Deus","email":"","orcid":"","institution":"Ardhi University","correspondingAuthor":false,"prefix":"","firstName":"Dorothea","middleName":"","lastName":"Deus","suffix":""},{"id":590821585,"identity":"79a9d60b-6cb2-4a50-a986-a7d4b659a51e","order_by":2,"name":"Anastazia Msusa","email":"","orcid":"","institution":"Ardhi University","correspondingAuthor":false,"prefix":"","firstName":"Anastazia","middleName":"","lastName":"Msusa","suffix":""}],"badges":[],"createdAt":"2026-02-13 10:45:43","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-8870814/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8870814/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102962260,"identity":"ea57c95e-23c8-4254-a431-7a1646066955","added_by":"auto","created_at":"2026-02-19 04:06:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1680460,"visible":true,"origin":"","legend":"","description":"","filename":"GeospatialBasedSurveillanceofMalariaRiskinDaresSalaamUsingaHybrid3DCNN.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8870814/v1_covered_71cea879-54a6-479f-b0e7-9aa1027ca877.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGeospatial Based Surveillance of Malaria Risk in Dar es Salaam Using a Hybrid 3DCNN+LSTM and CA Model\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Malaria risk, geospatial surveillance, remote sensing, 3D Convolutional Neural Network (3DCNN), Long Short-Term Memory (LSTM), Cellular Automata (CA), Random Forest, climate variability, deep learning","lastPublishedDoi":"10.21203/rs.3.rs-8870814/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8870814/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMalaria remains as a significant public health burden in tropical and subtropical regions facing challenges in efficiently identifying and predicting risk areas. Conventional field‑survey methods used for mapping of Anopheles breeding sites are often costly, time‑consuming, and spatially incomplete. Therefore, therefore is a pressing need for a geospatially integrated surveillance framework for accurately mapping malaria risk and forecasting future risk dynamics to support targeted control efforts. A geospatial hybrid-modeling framework developed by integrating multi‑source remote sensing, Malaria and Climate datasets. Random Forest model employed solely to determine the relative importance of input variables, subsequently weighted and selected for inclusion in a deep learning architecture. The predictive model combined 3D Convolution Neural Network for capturing spatial patterns with a Long-Short Term Memory to learn temporal dynamics. The model trained against a baseline mean squared error (MSE) of 0.1. To improve spatial realism in the final risk maps, a Cellular Automata (CA) model incorporated using a 3\u0026times;3 Moore neighborhood structure, with parameters calibrated at γ\u0026thinsp;=\u0026thinsp;0.293 and β\u0026thinsp;=\u0026thinsp;43.9 to enhance the spatial propagation of risks across neighboring cells. The framework successfully mapped malaria risks in Dar es Salaam with a Spearman correlation of 0.92 while Kigamboni South, Tundwi, and Msongola identified as high-risk areas. The hybrid 3DCNN\u0026ndash;LSTM model prediction performance reduced training and validation losses by 97.3% and 90.2% respectively from the baseline with test MSE of 0.005. 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