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.
Keywords: GSARIMA model; Forecast; Salmonella outbreaks; Surveillance.
©2020 Rojas and Ibacache-Quiroga.