FEPO: a machine learning ensemble approach for predicting extreme phlebotomine sand fly abundance and leishmaniasis-risk hotspots across Europe

FEPO: a machine learning ensemble approach for predicting extreme phlebotomine sand fly abundance and leishmaniasis-risk hotspots across Europe | 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 Article FEPO: a machine learning ensemble approach for predicting extreme phlebotomine sand fly abundance and leishmaniasis-risk hotspots across Europe Majid Soheili, Oldrich Rakovec, Ehsan Modiri, Suha K. Arserim, and 25 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7852217/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 Climate change significantly influences the spread of infectious diseases, including leishmaniasis, which is transmitted by phlebotomine sand flies. The geographical distribution of sand flies has expanded northward from the Mediterranean region, increasing the risk of leishmaniasis in areas that previously lacked systematic vector surveillance. This study presents FEPO (SandFlies Extreme POpulation prediction), a machine learning ensemble model that serves as a core component for developing early warning systems for vector-borne diseases. FEPO uses more than one thousand field trap records collected between 2011 and 2022, along with 1 km meteorological, hydrological, and morphological grids, to produce daily maps of sand fly density spanning 26 European countries. The model stacks gradient boosted decision trees using CatBoost and applies a tailored under and over sampling strategy to address the scarcity and skewness of observational data, where occasional population surges are buried among many zero and low abundance counts. Tenfold cross validation shows that FEPO achieves an 11% lower mean absolute error compared to baseline regression models. The model reveals persistent hotspots along the Mediterranean and Balkan coasts, as well as in parts of Central and Northern Europe, where environmental conditions favor vector proliferation. By delivering high resolution outputs, FEPO enables public health agencies to target trapping and mitigate outbreaks while also offering a transferable blueprint for early warning systems that address other climate sensitive disease vectors. Earth and environmental sciences/Climate sciences Health sciences/Diseases Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Supplementary Files Ph.tobbi15thmonth.png Ph.perniciosus15thmonth.png Ph.sergenti15thmonth.png Ph.papatasi15thmonth.png 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-7852217","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":536915458,"identity":"a5ef6075-586b-4fde-b33c-690ee851575b","order_by":0,"name":"Majid Soheili","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIie2RoQoCQRCG51g4y4J1LfoEwoFwIr7MLAaLzWKQY0XQ4iOIz6DFPLKg5cBq87YbjEbHw2RYLwruF5YN8+0/MwsQCPwmSJLPBKIZ4QTq5b2qYghzaJgKCoB8v0zRooLSXpKjG2TNbm1uyG2sahmxK3xKmiMe1mA7vdXBkN5bFZl47I1JCdFKIL296FLJBMhUeZVz8VIyvb06VtZWxSC7D69yKVMEp/DGtLFKcorPYKXgWRLbSXJuDI9DpUQ8/tLYaHC/TbJmcjo595j2VWs53929MSDx4yOEv56p0deSQCAQ+HOebVFTxC+q6uoAAAAASUVORK5CYII=","orcid":"","institution":"Helmholtz Centre for Environmental Research","correspondingAuthor":true,"prefix":"","firstName":"Majid","middleName":"","lastName":"Soheili","suffix":""},{"id":536915459,"identity":"f305c844-2398-459c-83bb-42ba675312f2","order_by":1,"name":"Oldrich Rakovec","email":"","orcid":"","institution":"Czech University of Life Sciences Prague","correspondingAuthor":false,"prefix":"","firstName":"Oldrich","middleName":"","lastName":"Rakovec","suffix":""},{"id":536915460,"identity":"934f3663-7312-47e9-be65-5dba042e22dd","order_by":2,"name":"Ehsan Modiri","email":"","orcid":"","institution":"Helmholtz Centre for Environmental Research","correspondingAuthor":false,"prefix":"","firstName":"Ehsan","middleName":"","lastName":"Modiri","suffix":""},{"id":536915461,"identity":"985883e4-e209-4b6b-932b-5c3534f990e1","order_by":3,"name":"Suha K. 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Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7852217/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7852217/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Climate change significantly influences the spread of infectious diseases, including leishmaniasis, which is transmitted by phlebotomine sand flies. The geographical distribution of sand flies has expanded northward from the Mediterranean region, increasing the risk of leishmaniasis in areas that previously lacked systematic vector surveillance. This study presents FEPO (SandFlies Extreme POpulation prediction), a machine learning ensemble model that serves as a core component for developing early warning systems for vector-borne diseases. FEPO uses more than one thousand field trap records collected between 2011 and 2022, along with 1 km meteorological, hydrological, and morphological grids, to produce daily maps of sand fly density spanning 26 European countries. The model stacks gradient boosted decision trees using CatBoost and applies a tailored under and over sampling strategy to address the scarcity and skewness of observational data, where occasional population surges are buried among many zero and low abundance counts. Tenfold cross validation shows that FEPO achieves an 11% lower mean absolute error compared to baseline regression models. The model reveals persistent hotspots along the Mediterranean and Balkan coasts, as well as in parts of Central and Northern Europe, where environmental conditions favor vector proliferation. By delivering high resolution outputs, FEPO enables public health agencies to target trapping and mitigate outbreaks while also offering a transferable blueprint for early warning systems that address other climate sensitive disease vectors.","manuscriptTitle":"FEPO: a machine learning ensemble approach for predicting extreme phlebotomine sand fly abundance and leishmaniasis-risk hotspots across Europe","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 02:53:37","doi":"10.21203/rs.3.rs-7852217/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":"fe80b218-4ff5-4792-b7e4-3c66e669cc74","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57095782,"name":"Earth and environmental sciences/Climate sciences"},{"id":57095783,"name":"Health sciences/Diseases"},{"id":57095784,"name":"Biological sciences/Ecology"},{"id":57095785,"name":"Earth and environmental sciences/Ecology"},{"id":57095786,"name":"Earth and environmental sciences/Environmental sciences"},{"id":57095787,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-28T09:25:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 02:53:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7852217","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7852217","identity":"rs-7852217","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}