{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T14:51:20Z","timestamp":1791471080320,"version":"4.3.4"},"reference-count":118,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"vor","delay-in-days":86,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"University of Padjadjaran"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Infect Dis"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Enhancing the capacity to forecast tropical disease transmission, identify key risk factors, and support timely public health responses is closely aligned with broader efforts to strengthen population health, including Sustainable Development Goal 3 on good health and well-being. Machine learning and deep learning models have shown strong potential for prediction and classification tasks, but their limited interpretability and incomplete treatment of uncertainty may constrain their use in public health decision-making. Bayesian methods, by contrast, provide a probabilistic framework for incorporating uncertainty, prior information, and spatial or temporal dependence, although they often involve greater methodological and computational complexity. In this context, we conducted a scoping review of recent research on machine learning, deep learning, and Bayesian approaches for tropical infectious disease prediction, diagnosis, risk mapping, risk analysis, and surveillance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study presents a scoping review of recent advances and current trends in machine learning, deep learning, Bayesian, and hybrid modeling approaches for tropical disease prediction, diagnosis, and risk analysis, covering studies published between 1 January 2025 and 1 January 2026 and reported following the PRISMA-ScR checklist. The review examines (i) disease-specific modeling approaches, (ii) predictive and diagnostic performance, (iii) epidemiological applications in which different methods were applied or showed advantages, and (iv) methodological and practical implications for future research and real-world applications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 97 Scopus-indexed, peer-reviewed articles published between 1 January 2025 and 1 January 2026 were included in the synthesis. Methodologically, machine\/deep learning approaches were reported in 50 studies, slightly outnumbering Bayesian methods, which were reported in 43 studies. Hybrid Bayesian\u2013machine learning approaches remained rare, with only 4 studies identified, suggesting that methodological integration is still at an early stage. The literature was concentrated on dengue and malaria, which together accounted for 61 of the 97 reviewed studies, possibly reflecting their high public health burden and greater availability of surveillance data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Overall, the literature remains strongly focused on dengue and malaria, with machine and deep learning models primarily used for prediction-oriented tasks and Bayesian approaches more often applied to risk mapping and determinant analysis because of their ability to explicitly quantify uncertainty. In practice, much of the existing literature is still centered on climate-related transmission, forecasting, and early warning. Although the integration of Bayesian models with machine learning or deep learning has been discussed in recent studies, real-world applications remain limited and are mostly exploratory rather than routinely implemented.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical trial number<\/jats:title>\n                    <jats:p>Not applicable.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12879-026-13993-1","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T01:47:38Z","timestamp":1783993658000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Recent advances in machine learning and Bayesian modeling for tropical disease prediction, diagnosis, and risk analysis: a scoping review"],"prefix":"10.1186","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1391-4138","authenticated-orcid":false,"given":"I. 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