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

Background: 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...

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Autores: Fernández-Fontelo, Amanda, Puig, Pedro, Caceres, German, Romero, Luis, Revie, Crawford, Sanchez, Javier, Dorea, Fernanda C., Alba-Casals, Ana
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Institut de Recerca i Tecnologia Agroalimentàries (IRTA)
Repositorio:IRTA Pubpro. Open Digital Archive
OAI Identifier:oai:repositori.irta.cat:20.500.12327/864
Acceso en línea:http://hdl.handle.net/20.500.12327/864
https://doi.org/10.1186/s12917-020-02312-8
Access Level:acceso abierto
Palabra clave:619
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spelling Enhancing the monitoring of fallen stock at different hierarchical administrative levels: an illustration on dairy cattle from regions with distinct husbandry, demographical and climate traitsFernández-Fontelo, AmandaPuig, PedroCaceres, GermanRomero, LuisRevie, CrawfordSanchez, JavierDorea, Fernanda C.Alba-Casals, Ana619Background: 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. Results: 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. Conclusions: 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.info:eu-repo/semantics/publishedVersionBMCProducció AnimalSanitat Animal202020202020info:eu-repo/semantics/article13application/pdfhttp://hdl.handle.net/20.500.12327/864https://doi.org/10.1186/s12917-020-02312-8reponame:IRTA Pubpro. Open Digital Archiveinstname:Institut de Recerca i Tecnologia Agroalimentàries (IRTA)InglésBMC Veterinary ResearchAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.irta.cat:20.500.12327/8642026-06-16T08:51:17Z
dc.title.none.fl_str_mv 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
title 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
spellingShingle 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
619
title_short 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_sort 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
dc.creator.none.fl_str_mv Fernández-Fontelo, Amanda
Puig, Pedro
Caceres, German
Romero, Luis
Revie, Crawford
Sanchez, Javier
Dorea, Fernanda C.
Alba-Casals, Ana
author Fernández-Fontelo, Amanda
author_facet Fernández-Fontelo, Amanda
Puig, Pedro
Caceres, German
Romero, Luis
Revie, Crawford
Sanchez, Javier
Dorea, Fernanda C.
Alba-Casals, Ana
author_role author
author2 Puig, Pedro
Caceres, German
Romero, Luis
Revie, Crawford
Sanchez, Javier
Dorea, Fernanda C.
Alba-Casals, Ana
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Producció Animal
Sanitat Animal
dc.subject.none.fl_str_mv 619
topic 619
description Background: 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. Results: 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. Conclusions: 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.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12327/864
https://doi.org/10.1186/s12917-020-02312-8
url http://hdl.handle.net/20.500.12327/864
https://doi.org/10.1186/s12917-020-02312-8
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv BMC Veterinary Research
dc.rights.none.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 13
application/pdf
dc.publisher.none.fl_str_mv BMC
publisher.none.fl_str_mv BMC
dc.source.none.fl_str_mv reponame:IRTA Pubpro. Open Digital Archive
instname:Institut de Recerca i Tecnologia Agroalimentàries (IRTA)
instname_str Institut de Recerca i Tecnologia Agroalimentàries (IRTA)
reponame_str IRTA Pubpro. Open Digital Archive
collection IRTA Pubpro. Open Digital Archive
repository.name.fl_str_mv
repository.mail.fl_str_mv
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