Enhancing the monitoring of fallen stock at different hierarchical administrative levels : an illustration on dairy cattle from regions with distinct husbandry, demographical and climate traits
Fernández-Fontelo, Amanda (Universitat Autònoma de Barcelona. Departament de Matemàtiques)
Puig, Pedro (Universitat Autònoma de Barcelona. Departament de Matemàtiques)
Caceres, German (Ministerio de Agricultura y Pesca, Alimentación. Subdirección General de Sanidad e Higiene Animal y Trazabilidad)
Romero, Luis (Ministerio de Agricultura y Pesca, Alimentación. Subdirección General de Sanidad e Higiene Animal y Trazabilidad)
Revie, Crawford (University of Strathclyde. Department of Computer and Information Sciences)
Sanchez, Javier (University Prince Edward Island. Centre for Veterinary Epidemiological Research)
Dorea, Fernanda C. (National Veterinary Institute. Department of Disease Control and Epidemiology)
Alba-Casals, Ana (Institut de Recerca i Tecnologia Agroalimentàries. Centre de Recerca en Sanitat Animal)

Data:	2020
Resum:	The automated collection of non-specific data from livestock, combined with techniques for data mining and time series analyses, facilitates the development of animal health syndromic surveillance (AHSyS). An example of AHSyS approach relates to the monitoring of bovine fallen stock. In order to enhance part of the machinery of a complete syndromic surveillance system, the present work developed a novel approach for modelling in near real time multiple mortality patterns at different hierarchical administrative levels. To illustrate its functionality, this system was applied to mortality data in dairy cattle collected across two Spanish regions with distinct demographical, husbandry, and climate conditions. The process analyzed the patterns of weekly counts of fallen dairy cattle at different hierarchical administrative levels across two regions between Jan-2006 and Dec-2013 and predicted their respective expected counts between Jan-2014 and Jun- 2015. By comparing predicted to observed data, those counts of fallen dairy cattle that exceeded the upper limits of a conventional 95% predicted interval were identified as mortality peaks. This work proposes a dynamic system that combines hierarchical time series and autoregressive integrated moving average models (ARIMA). These ARIMA models also include trend and seasonality for describing profiles of weekly mortality and detecting aberrations at the region, province, and county levels (spatial aggregations). Software that fitted the model parameters was built using the R statistical packages. The work builds a novel tool to monitor fallen stock data for different geographical aggregations and can serve as a means of generating early warning signals of a health problem. This approach can be adapted to other types of animal health data that share similar hierarchical structures.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Fallen stock ; Dairy cattle ; Syndromic surveillance ; Hierarchical time series ; ARIMA models ; Spain
Publicat a:	BMC veterinary research, Vol. 16 (april 2020) , ISSN 1746-6148

DOI: 10.1186/s12917-020-02312-8
PMID: 32290840

13 p, 2.2 MB

El registre apareix a les col·leccions:
Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències de la salut i biociències > Centre de Recerca en Sanitat Animal (CReSA-IRTA)
Articles > Articles de recerca
Articles > Articles publicats