Fuente: Microorganisms - Revista científica (MDPI) Microorganisms, Vol. 13, Pages 2773: Integrating Statistical and Machine-Learning Approaches for Salmonella enterica Surveillance in Northwestern Italy: A One Health Data-Driven Framework
Microorganisms doi: 10.3390/microorganisms13122773
Authors:
Aitor Garcia-Vozmediano
Angelo Romano
Mattia Begovoeva
Monica Pitti
Elisabetta Crescio
Aldo Brenda
Michela Di Roberto
Anna Gioia
Adriana Giraldo
Eva Massone
Michela Nobile Lanzarini
Alessia Raggio
Erica De Vita
Giuseppe Ru
Cristiana Maurella

Salmonella enterica is a major cause of foodborne illness globally. We analysed 41,945 food samples collected under official surveillance in Piedmont (north-western Italy) between 2013 and 2023 to characterise contamination patterns and evaluate an integrated analytical framework combining classical statistical modelling with machine-learning prediction. Overall prevalence was low (2.20%; 95% CI: 2.06–2.35) but heterogeneous across matrices, with poultry and pork displaying the highest contamination levels (11.8% and 7.14%). Risk increased at distribution/retail stages, and contamination declined markedly from 2013 to 2018, with lower levels in late autumn. Meteorological factors had minimal influence. Mixed-effects models identified food category and production stage as the main determinants of contamination, while the XGBoost algorithm showed stable predictive performance (median absolute error ≈ 0.02) and spatially coherent estimates. SHAP analyses confirmed food composition variables as the dominant predictors. These findings highlight persistent vulnerabilities within poultry and swine supply chains, particularly at post-production stages, and illustrate the complementary value of combining explanatory and predictive approaches to strengthen risk-based, One Health-aligned food-safety surveillance.