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Although its seasonal pattern is well established, the role of air pollution and meteorological factors remains unclear. This study aims to investigate their impact on daily AOM visits by integrating machine learning and epidemiological approaches. We conducted a retrospective analysis of pediatric AOM diagnoses (2007\u20132015) at S. Croce and Carle Hospital (Cuneo, Italy). Predictors included PM10, NO\u2082, O\u2083, and eleven meteorological variables. Ensemble machine learning models (Random Forest, XGBoost, and AdaBoost) were trained and validated using 10-fold cross-validation. Model interpretability was assessed through SHAP values. Distributed Lag Nonlinear Models (DLNM) were applied to estimate delayed exposure\u2013response relationships over lag periods of 0\u20131, 0\u20133, 0\u20135, and 0\u201310 days, with results expressed as Relative Risks (RRs) and 95% Confidence Intervals (CIs). AdaBoost showed the best performance (R\u00b2 = 0.974; MAE\u2009=\u20090.019 cases\/day; cross-validated R\u00b2 = 0.987). SHAP analysis identified mean temperature as the most influential predictor (44%), while PM10, NO\u2082, and O\u2083 each contributed approximately 10%. DLNM analysis confirmed a strong and consistent effect of temperature across all lag periods (RR\u2009&gt;\u20091.20, CI\u2009&gt;\u20091). Moderate associations were observed for NO\u2082 and PM10 (RR: 1.02\u20131.04). O\u2083 exhibited smaller but significant effects at shorter lags (RR\u2009=\u20091.01 at 0\u20131 days; RR\u2009=\u20091.02 at 0\u20133 days; CI\u2009&gt;\u20091). Environmental factors, particularly temperature, play a significant role in pediatric AOM incidence. The integration of machine learning and DLNM enhances predictive accuracy and improves the understanding of exposure timing. These findings support the development of early warning systems and targeted preventive strategies under adverse environmental conditions. Further validation in larger urban settings is needed.<\/jats:p>","DOI":"10.1007\/s00484-026-03226-0","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T13:47:13Z","timestamp":1779198433000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning and dose response effect models: an integrated approach to analyze the association between environmental variables and Cuneo Emergency Department admissions for Acute Otitis Media (2007-2015)"],"prefix":"10.1007","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9274-7613","authenticated-orcid":false,"given":"M","family":"Rondinone","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1736-5017","authenticated-orcid":false,"given":"V","family":"Condemi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5445-0029","authenticated-orcid":false,"given":"M","family":"Gestro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3235-8712","authenticated-orcid":false,"given":"V","family":"Telesca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,19]]},"reference":[{"issue":"10","key":"3226_CR1","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s11882-023-01079-2","volume":"20","author":"MLD Bardou","year":"2023","unstructured":"Bardou MLD, Pontarolli D, Grumach AS (2023) Otitis as a warning sign for inborn errors of immunity (IEI). 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