A forecast model for prevention of foodborne outbreaks of non-typhoidal salmonellosis
Archivos
Fecha
2020
Profesor Guía
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PeerJ Publishing
Ubicación
ISBN
ISSN
item.page.issne
item.page.doiurl
Facultad
Facultad de Farmacia
Departamento o Escuela
Escuela de Nutricion y Dietetica
Determinador
Recolector
Especie
Nota general
Resumen
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.
Descripción
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Palabras clave
FORECAST, GSARIMA MODEL, SALMONELLA OUTBREAKS, SURVEILLANCE
Licencia
Distributed under Creative Commons CC-BY 4.0