{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T19:21:04Z","timestamp":1787685664964,"version":"build-2784847793"},"reference-count":130,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Background: Mosquito-borne viral diseases are a growing global health threat, and artificial intelligence (AI) and machine learning (ML) are increasingly proposed as forecasting tools to support early-warning and response. However, the available evidence is fragmented across pathogens, settings and modelling approaches. This review provides, to the best of our knowledge, the first comprehensive comparative assessment of AI\/ML models forecasting mosquito-borne viral diseases in human populations, jointly synthesising predictive performance across model families and appraising both methodological quality and operational readiness. Methods: Following PRISMA 2020, we searched PubMed, Embase and Scopus up to August 2025. We included studies applying AI\/ML or statistical models to predict arboviral incidence, outbreaks or temporal trends and reporting at least one quantitative performance metric. Given the substantial heterogeneity in outcomes, predictors and time\u2013space scales, we conducted a descriptive synthesis. Risk of bias and applicability were evaluated using PROBAST. Results: Ninety-eight studies met the inclusion criteria, of which 91 focused on dengue. The forecasts spanned national to city-level settings and annual-to-weekly resolutions. Across classification tasks, tree-ensemble models showed the most consistent performance, with accuracies typically above 0.85, while classical ML and deep-learning models showed wider variability. For regression tasks, errors increased with temporal horizon and spatial aggregation: short-term, fine-scale forecasts (e.g., weekly city level) often achieved low absolute errors, whereas long-horizon national models frequently exhibited very large errors and unstable performance. PROBAST assessment indicated that most studies (63\/98) were at high risk of bias, with only 24 judged at low risk and limited external validation. Conclusions: AI\/ML models, especially tree-ensemble approaches, show strong potential for short-term, fine-scale forecasting, but their reliability drops substantially at broader spatial and temporal scales. Most remain research-stage, with limited external validation and minimal operational deployment. This review clarifies current capabilities and highlights three priorities for real-world use: standardised reporting, rigorous external validation, and context-specific calibration.<\/jats:p>","DOI":"10.3390\/make8010015","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T11:46:43Z","timestamp":1767786403000},"page":"15","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Artificial Intelligence Models for Forecasting Mosquito-Borne Viral Diseases in Human Populations: A Global Systematic Review and Comparative Performance Analysis"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9185-9747","authenticated-orcid":false,"given":"Flavia","family":"Pennisi","sequence":"first","affiliation":[{"name":"Faculty of Medicine, University Vita-Salute San Raffaele, 20132 Milan, Italy"},{"name":"PhD National Program in One Health Approaches to Infectious Diseases and Life Science Research, Department of Public Health, Experimental and Forensic Medicine, University of Pavia, 27100 Pavia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4315-0481","authenticated-orcid":false,"given":"Antonio","family":"Pinto","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University Vita-Salute San Raffaele, 20132 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5796-671X","authenticated-orcid":false,"given":"Fabio","family":"Borgonovo","sequence":"additional","affiliation":[{"name":"Division of Public Health, Infectious Diseases and Occupational Medicine, Department of Medicine, Mayo Clinic College of Medicine and Science, Mayo Clinic, Rochester, MN 55905, USA"},{"name":"Department of Infectious Diseases, \u201cLuigi Sacco\u201d University Hospital, Azienda Socio-Sanitaria Territoriale (ASST) Fatebenefratelli FBF Sacco, 20157 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4214-9862","authenticated-orcid":false,"given":"Giovanni","family":"Scaglione","sequence":"additional","affiliation":[{"name":"Department of Infectious Diseases, \u201cLuigi Sacco\u201d University Hospital, Azienda Socio-Sanitaria Territoriale (ASST) Fatebenefratelli FBF Sacco, 20157 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riccardo","family":"Ligresti","sequence":"additional","affiliation":[{"name":"PhD National Program in One Health Approaches to Infectious Diseases and Life Science Research, Department of Public Health, Experimental and Forensic Medicine, University of Pavia, 27100 Pavia, Italy"},{"name":"Department of Infectious Diseases, \u201cLuigi Sacco\u201d University Hospital, Azienda Socio-Sanitaria Territoriale (ASST) Fatebenefratelli FBF Sacco, 20157 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2017-3665","authenticated-orcid":false,"given":"Omar Enzo","family":"Santangelo","sequence":"additional","affiliation":[{"name":"Regional Health Care and Social Agency of Lodi, Azienda Socio-Sanitaria Territoriale (ASST) Lodi, 26900 Lodi, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7179-7246","authenticated-orcid":false,"given":"Sandro","family":"Provenzano","sequence":"additional","affiliation":[{"name":"Local Health Unit of Trapani, ASP Trapani, 91100 Trapani, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6587-4794","authenticated-orcid":false,"given":"Andrea","family":"Gori","sequence":"additional","affiliation":[{"name":"Department of Infectious Diseases, \u201cLuigi Sacco\u201d University Hospital, Azienda Socio-Sanitaria Territoriale (ASST) Fatebenefratelli FBF Sacco, 20157 Milan, Italy"},{"name":"Department of Biomedical and Clinical Sciences \u201cL. Sacco\u201d, University of Milan, 20157 Milan, Italy"},{"name":"Centre for Multidisciplinary Research in Health Science (MACH), University of Milan, 20122 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6012-9453","authenticated-orcid":false,"given":"Vincenzo","family":"Baldo","sequence":"additional","affiliation":[{"name":"Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padua, 35128 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0960-9563","authenticated-orcid":false,"given":"Carlo","family":"Signorelli","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University Vita-Salute San Raffaele, 20132 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3848-981X","authenticated-orcid":false,"given":"Vincenza","family":"Gianfredi","sequence":"additional","affiliation":[{"name":"Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padua, 35128 Padova, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e101","DOI":"10.1016\/S1473-3099(16)30518-7","article-title":"Epidemic arboviral diseases: Priorities for research and public health","volume":"17","author":"Gubler","year":"2017","journal-title":"Lancet Infect. 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