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

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dc.contributor.authorRojas, Fernando
dc.contributor.authorIbacache-Quiroga, Claudia
dc.date.accessioned2022-03-17T15:04:21Z
dc.date.available2022-03-17T15:04:21Z
dc.date.issued2020
dc.description.abstractBackground. 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.en_ES
dc.facultadFacultad de Farmaciaen_ES
dc.identifier.doi10.7717/peerj.10009
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/3863
dc.language.isoenen_ES
dc.publisherPeerJ Publishingen_ES
dc.rightsDistributed under Creative Commons CC-BY 4.0en_ES
dc.sourcePeerJen_ES
dc.subjectFORECASTen_ES
dc.subjectGSARIMA MODELen_ES
dc.subjectSALMONELLA OUTBREAKSen_ES
dc.subjectSURVEILLANCEen_ES
dc.titleA forecast model for prevention of foodborne outbreaks of non-typhoidal salmonellosisen_ES
dc.typeArticuloen_ES
uv.catalogadorRCR DIBRAen_ES
uv.departamentoEscuela de Nutricion y Dieteticaen_ES

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