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By leveraging tree-based machine learning (ML) algorithms, we exploited data from food safety audits to predict spatiotemporal patterns of salmonellosis in northwestern Italy. Data on human cases confirmed in 2015\u20132018 (<jats:italic>n<\/jats:italic>\u2009=\u20091969) and food surveillance data collected in 2014\u20132018 were used to develop ML algorithms. We integrated the monthly municipal human incidence with 27 potential predictors, including the observed prevalence of <jats:italic>Salmonella<\/jats:italic> in food. We applied the tree regression, random forest and gradient boosting algorithms considering different scenarios and evaluated their predictivity in terms of the mean absolute percentage error (MAPE) and R<jats:sup>2<\/jats:sup>. Using a similar dataset from the year 2019, spatiotemporal predictions and their relative sensitivities and specificities were obtained. Random forest and gradient boosting (R<jats:sup>2<\/jats:sup>\u00a0=\u00a00.55, MAPE\u2009=\u20097.5%) outperformed the tree regression algorithm (R<jats:sup>2<\/jats:sup>\u00a0=\u00a00.42, MAPE\u2009=\u20098.8%). <jats:italic>Salmonella<\/jats:italic> prevalence in food; spatial features; and monitoring efforts in ready-to-eat milk, fruits and vegetables, and pig meat products contributed the most to the models\u2019 predictivity, reducing the variance by 90.5%. Conversely, the number of positive samples obtained for specific food matrices minimally influenced the predictions (2.9%). Spatiotemporal predictions for 2019 showed sensitivity and specificity levels of 46.5% (due to the lack of some infection hotspots) and 78.5%, respectively. This study demonstrates the added value of integrating data from human and veterinary health services to develop predictive models of human salmonellosis occurrence, providing early warnings useful for mitigating foodborne disease impacts on public health.<\/jats:p>","DOI":"10.1186\/s13567-024-01323-9","type":"journal-article","created":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T12:12:57Z","timestamp":1717589577000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy"],"prefix":"10.1186","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4458-8294","authenticated-orcid":false,"given":"Aitor","family":"Garcia-Vozmediano","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristiana","family":"Maurella","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonardo A.","family":"Ceballos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elisabetta","family":"Crescio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rosa","family":"Meo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Walter","family":"Martelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monica","family":"Pitti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniela","family":"Lombardi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniela","family":"Meloni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chiara","family":"Pasqualini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuseppe","family":"Ru","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,5]]},"reference":[{"key":"1323_CR1","unstructured":"World Health Organization (2015) WHO estimates of the global burden of foodborne diseases: Foodborne Disease Burden Epidemiology Reference Group 2007\u20132015. https:\/\/apps.who.int\/iris\/handle\/10665\/199350 Accessed 20 Apr 2024"},{"key":"1323_CR2","doi-asserted-by":"publisher","first-page":"7666","DOI":"10.2903\/j.efsa.2022.7666","volume":"20","author":"European Food Safety Authority EFSA, and European Centre for Disease Prevention and Control, ECDC","year":"2022","unstructured":"European Food Safety Authority EFSA, and European Centre for Disease Prevention and Control, ECDC (2022) The European Union One Health 2021 Zoonoses Report. 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