{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T17:07:26Z","timestamp":1790615246234,"version":"4.1.0"},"reference-count":53,"publisher":"American Geophysical Union (AGU)","issue":"9","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":9,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["agupubs.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["GeoHealth"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    This study introduces Disease Vector Intelligence (DVI), an explainable Machine Learning (ML) framework developed within the EYWA (EarlY WArning system for mosquito\u2010borne diseases) ecosystem. DVI integrates big Earth Observation (EO), socioeconomic, and estimated mosquito abundance data to predict the presence or absence of West Nile Virus (WNV) cases, expressed as a risk score at a high spatiotemporal resolution. The SHAP (SHapley Additive exPlanations) method is employed for a local\u2010level feature importance analysis to uncover key drivers with a high impact on the prediction. The framework consists of two ML models: one intended to be used as an Early Warning System (EWS) predicting at a regional scale, and the other identifying areas susceptible to WNV transmission at a 2\u00a0\u00d7\u00a02\u00a0km\n                    <jats:sup>2<\/jats:sup>\n                    grid scale, independent of human\u2010defined administrative boundaries. Both models were trained on 11\u00a0years of historical WNV case data and validated in Greece. SHAP analysis on both models revealed temperature as a crucial driver of WNV transmission. The fine spatial resolution of the second model uncovered micro\u2010scale key drivers, such as elevation and land cover type, that the lower resolution model missed. DVI models could be used complementarily by health authorities to aid their decision\u2010making process and to provide critical insights into important drivers that influence the transmission of WNV.\n                  <\/jats:p>","DOI":"10.1029\/2025gh001747","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T06:56:50Z","timestamp":1789109810000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Explainable Machine Learning Framework for Predicting West Nile Virus and Uncovering Key Transmission Drivers"],"prefix":"10.1029","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2541-1132","authenticated-orcid":false,"given":"Dimitrios","family":"Sainidis","sequence":"first","affiliation":[{"name":"National Observatory of Athens IAASARS BEYOND Center for EO Research and Satellite Remote Sensing  Athens Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konstantinos","family":"Tsaprailis","sequence":"additional","affiliation":[{"name":"National Observatory of Athens IAASARS BEYOND Center for EO Research and Satellite Remote Sensing  Athens Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4973-9450","authenticated-orcid":false,"given":"Charalampos","family":"Kontoes","sequence":"additional","affiliation":[{"name":"National Observatory of Athens IAASARS BEYOND Center for EO Research and Satellite Remote Sensing  Athens Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"13","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.onehlt.2021.100330"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0079276"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.3201\/eid2802.ac2802"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2023.102272"},{"key":"e_1_2_9_6_1","unstructured":"Centers for Disease Control and Prevention. (2025a).West nile virus \u2010 Current year data (2024). Retrieved fromhttps:\/\/www.cdc.gov\/west\u2010nile\u2010virus\/data\u2010maps\/current\u2010year\u2010data.html"},{"key":"e_1_2_9_7_1","unstructured":"Centers for Disease Control and Prevention. (2025b).About vector\u2010borne diseases. Retrieved fromhttps:\/\/www.cdc.gov\/vector\u2010borne\u2010diseases\/about\/index.html"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CEEC.2018.8674210"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10980\u2010019\u201000839\u20102"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/SSIAI49293.2020.9094591"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/tbed.14513"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.5066\/P9TU80IG"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.5066\/P975CC9B"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010021\u201098316\u2010x"},{"key":"e_1_2_9_16_1","unstructured":"European Centre for Disease Prevention and Control. (2025).Culex pipiens \u2010 Factsheet for experts. Retrieved fromhttps:\/\/www.ecdc.europa.eu\/en\/infectious\u2010disease\u2010topics\/related\u2010public\u2010health\u2010topics\/disease\u2010vectors\/facts\/mosquito\u2010factsheets\/culex\u2010pipiens"},{"key":"e_1_2_9_17_1","unstructured":"European Space Agency. (2021).Copernicus Sentinel\u20102 MSI Level\u20101C TOA reflectance product collection 1.https:\/\/doi.org\/10.5270\/S2_\u2010742ikth"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.2908\/NAMA_10R_3GDP"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.2908\/ROAD_GO_NA_RL3G"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.2908\/ROAD_GO_NA_RU3G"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.2908\/DEMO_R_PJANAGGR3"},{"key":"e_1_2_9_22_1","unstructured":"Eurostat. (2025).Nuts \u2010 Nomenclature of territorial units for statistics. Retrieved fromhttps:\/\/ec.europa.eu\/eurostat\/web\/nuts"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.lanepe.2022.100370"},{"key":"e_1_2_9_24_1","unstructured":"Friedl M. &Sulla\u2010Menashe D.(2022).Modis\/terra+ aqua land cover type yearly l3 global 500m sin grid v061. Nasa eosdis land processes distributed active archive center.61.https:\/\/doi.org\/10.5067\/MODIS\/MCD12Q1.061"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1012866"},{"key":"e_1_2_9_26_1","unstructured":"Hellenic Statistical Authority. (2024).Estimated population 2024. Retrieved fromhttps:\/\/www.statistics.gr\/en\/statistics\/\u2010\/publication\/SPO18\/\u2010"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","unstructured":"Huffman G. J. Stocker E. F. Bolvin D. T. Nelkin E. J. &Tan J.(2024).GPM IMERG late precipitation L3 half hourly 0.1 degree x 0.1 degree V07[Dataset].Goddard Earth Sciences Data and Information Services Center (GES DISC).https:\/\/doi.org\/10.5067\/GPM\/IMERG\/3B\u2010HH\u2010L\/07","DOI":"10.5067\/GPM\/IMERG\/3B\u2010HH\u2010L\/07"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121490"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.ppat.1011880"},{"key":"e_1_2_9_30_1","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg S. M.","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1093\/jme\/tjz132"},{"key":"e_1_2_9_32_1","unstructured":"National Public Health Organization. (2024).Npho. Retrieved fromhttps:\/\/eody.gov.gr\/en\/npho\/"},{"key":"e_1_2_9_33_1","unstructured":"National Public Health Organization. (2024b).Weekly epidemiological reports \u2013 West nile virus disease. Retrieved fromhttps:\/\/eody.gov.gr\/en\/epidemiological\u2010statistical\u2010data\/weekly\u2010epidemiological\u2010reports\/weekly\u2010epidemiological\u2010reports\u2010west\u2010nile\u2010virus\u2010disease\/"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","unstructured":"NPHO. (2026).Validation sample dataset for west nile virus prediction framework[Dataset].Zenodo.https:\/\/doi.org\/10.5281\/zenodo.19845411","DOI":"10.5281\/zenodo.19845411"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs11161862"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph10083543"},{"key":"e_1_2_9_37_1","doi-asserted-by":"publisher","DOI":"10.1186\/1756\u20103305\u20107\u2010187"},{"key":"e_1_2_9_38_1","doi-asserted-by":"crossref","unstructured":"Pitsiorlas I. Jamoussi N. &Kountouris M.(2025).A conformal predictive measure for assessing catastrophic forgetting.https:\/\/arxiv.org\/abs\/2505.10677","DOI":"10.1109\/AMLDS63918.2025.11159334"},{"key":"e_1_2_9_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS53475.2024.10641865"},{"key":"e_1_2_9_40_1","doi-asserted-by":"publisher","DOI":"10.7763\/IJMLC.2013.V3.307"},{"key":"e_1_2_9_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-99-5166-6_66"},{"key":"e_1_2_9_42_1","doi-asserted-by":"publisher","unstructured":"Sainidis D.(2026).Mbd\u2010prediction: Code for west nile virus prediction framework[Software].Zenodo.https:\/\/doi.org\/10.5281\/zenodo.19603920","DOI":"10.5281\/zenodo.19603920"},{"key":"e_1_2_9_43_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010020\u201079193\u20102"},{"key":"e_1_2_9_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.actatropica.2018.05.003"},{"key":"e_1_2_9_45_1","doi-asserted-by":"publisher","DOI":"10.7554\/elife.58511"},{"key":"e_1_2_9_46_1","doi-asserted-by":"publisher","DOI":"10.1029\/2020gh000244"},{"key":"e_1_2_9_47_1","doi-asserted-by":"publisher","DOI":"10.3390\/jcm11113026"},{"key":"e_1_2_9_48_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0128377"},{"key":"e_1_2_9_49_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs13132557"},{"key":"e_1_2_9_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jinf.2024.106296"},{"key":"e_1_2_9_51_1","doi-asserted-by":"publisher","DOI":"10.5067\/MODIS\/MOD11A1.061"},{"key":"e_1_2_9_52_1","unstructured":"WoldClim. (2024).Bioclimatic variables. Retrieved fromhttps:\/\/www.worldclim.org\/data\/bioclim.html"},{"key":"e_1_2_9_53_1","unstructured":"World Health Organization. (2024).Vector\u2010borne diseases. Retrieved fromhttps:\/\/www.who.int\/news\u2010room\/fact\u2010sheets\/detail\/vector\u2010borne\u2010diseases"},{"key":"e_1_2_9_54_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apgeog.2013.09.022"}],"container-title":["GeoHealth"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/agupubs.onlinelibrary.wiley.com\/doi\/pdf\/10.1029\/2025GH001747","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/agupubs.onlinelibrary.wiley.com\/doi\/full-xml\/10.1029\/2025GH001747","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/agupubs.onlinelibrary.wiley.com\/doi\/pdf\/10.1029\/2025GH001747","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T16:13:37Z","timestamp":1790612017000},"score":1,"resource":{"primary":{"URL":"https:\/\/agupubs.onlinelibrary.wiley.com\/doi\/10.1029\/2025GH001747"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":53,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["10.1029\/2025GH001747"],"URL":"https:\/\/doi.org\/10.1029\/2025gh001747","archive":["Portico"],"relation":{},"ISSN":["2471-1403","2471-1403"],"issn-type":[{"value":"2471-1403","type":"print"},{"value":"2471-1403","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"2025-11-17","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-06-01","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-09-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e2025GH001747"}}