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

PeerJ. 2020 Nov 10:8:e10009. doi: 10.7717/peerj.10009. eCollection 2020.


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.

Grants and funding

This research was funded by the Centro de Micro-Bioinnovación, Universidad de Valparaíso, Chile (DIUV-CIDI 4/2016), and grant “Fondecyt de Iniciación 1119004” (Fernando Rojas) from the National Agency of Research and Development of Chile. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.