A forecast model for prevention of foodborne outbreaks of non-typhoidal salmonellosis

BACKGROUND: This work presents a forecast model for non-typhoidal salmonellosis outbreaks. METHOD: This forecast model is based on fitted values of multivariate regression time series that consider diagnosis and estimation of different parameters, through a very flexible statistical treatment called generalized auto-regressive and moving average models (GSARIMA). RESULTS: The forecast model was validated by analyzing the cases of Salmonella enterica serovar Enteritidis in Sydney Australia (2014–2016), the environmental conditions and the consumption of high-risk food as predictive variables. CONCLUSIONS: The prediction of cases of Salmonella enterica serovar Enteritidis infections are included in a forecast model based on fitted values of time series modeled by GSARIMA, for an early alert of future outbreaks caused by this pathogen, and associated to high-risk food. In this context, the decision makers in the epidemiology field can led to preventive actions using the proposed model.