Animal health syndromic surveillance: a systematic literature review of the progress in the last 5 years (2011–2016)
Fernanda C Dórea,1 Flavie Vial2 1Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), Uppsala, 2Epi-Connect, Skogås, Sweden Abstract: This review presents the current initiatives and potential for development in the field of animal health surveill...
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Dove Medical Press
2016
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oai:doaj.org-article:cb430569e5e3463ab15c26ed987d6f0e2021-12-02T06:38:09ZAnimal health syndromic surveillance: a systematic literature review of the progress in the last 5 years (2011–2016)2230-2034https://doaj.org/article/cb430569e5e3463ab15c26ed987d6f0e2016-11-01T00:00:00Zhttps://www.dovepress.com/animal--health-syndromic-surveillance-a-systematic-literature-review-o-peer-reviewed-article-VMRRhttps://doaj.org/toc/2230-2034Fernanda C Dórea,1 Flavie Vial2 1Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), Uppsala, 2Epi-Connect, Skogås, Sweden Abstract: This review presents the current initiatives and potential for development in the field of animal health surveillance (AHSyS), 5 years on from its advent to the front of the veterinary public health scene. A systematic review approach was used to document the ongoing AHSyS initiatives (active systems and those in pilot phase) and recent methodological developments. Clinical data from practitioners and laboratory data remain the main data sources for AHSyS. However, although not currently integrated into prospectively running initiatives, production data, mortality data, abattoir data, and new media sources (such as Internet searches) have been the objective of an increasing number of publications seeking to develop and validate new AHSyS indicators. Some limitations inherent to AHSyS such as reporting sustainability and the lack of classification standards continue to hinder the development of automated syndromic analysis and interpretation. In an era of ubiquitous electronic collection of animal health data, surveillance experts are increasingly interested in running multivariate systems (which concurrently monitor several data streams) as they are inferentially more accurate than univariate systems. Thus, Bayesian methodologies, which are much more apt to discover the interplay among multiple syndromic data sources, are foreseen to play a big part in the future of AHSyS. It has become clear that early detection of outbreaks may not be the principal expected benefit of AHSyS. As more systems will enter an active prospective phase, following the intensive development stage of the last 5 years, the study envisions AHSyS, in particular for livestock, to significantly contribute to future international-, national-, and local-level animal health intelligence, going beyond the detection and monitoring of disease events by contributing solid situation awareness of animal welfare and health at various stages along the food-producing chain, and an understanding of the risk management involving actors in this value chain. Keywords: aberration detection, animal health intelligence, biosurveillance, cluster detection, outbreak signal, temporal monitoringDórea FCVial FDove Medical PressarticleAberration detectionanimal health intelligencebiosurveillancecluster detectionoutbreak signaltemporal monitoring.Veterinary medicineSF600-1100ENVeterinary Medicine: Research and Reports, Vol Volume 7, Pp 157-170 (2016) |
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Aberration detection animal health intelligence biosurveillance cluster detection outbreak signal temporal monitoring. Veterinary medicine SF600-1100 |
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Aberration detection animal health intelligence biosurveillance cluster detection outbreak signal temporal monitoring. Veterinary medicine SF600-1100 Dórea FC Vial F Animal health syndromic surveillance: a systematic literature review of the progress in the last 5 years (2011–2016) |
| description |
Fernanda C Dórea,1 Flavie Vial2 1Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), Uppsala, 2Epi-Connect, Skogås, Sweden Abstract: This review presents the current initiatives and potential for development in the field of animal health surveillance (AHSyS), 5 years on from its advent to the front of the veterinary public health scene. A systematic review approach was used to document the ongoing AHSyS initiatives (active systems and those in pilot phase) and recent methodological developments. Clinical data from practitioners and laboratory data remain the main data sources for AHSyS. However, although not currently integrated into prospectively running initiatives, production data, mortality data, abattoir data, and new media sources (such as Internet searches) have been the objective of an increasing number of publications seeking to develop and validate new AHSyS indicators. Some limitations inherent to AHSyS such as reporting sustainability and the lack of classification standards continue to hinder the development of automated syndromic analysis and interpretation. In an era of ubiquitous electronic collection of animal health data, surveillance experts are increasingly interested in running multivariate systems (which concurrently monitor several data streams) as they are inferentially more accurate than univariate systems. Thus, Bayesian methodologies, which are much more apt to discover the interplay among multiple syndromic data sources, are foreseen to play a big part in the future of AHSyS. It has become clear that early detection of outbreaks may not be the principal expected benefit of AHSyS. As more systems will enter an active prospective phase, following the intensive development stage of the last 5 years, the study envisions AHSyS, in particular for livestock, to significantly contribute to future international-, national-, and local-level animal health intelligence, going beyond the detection and monitoring of disease events by contributing solid situation awareness of animal welfare and health at various stages along the food-producing chain, and an understanding of the risk management involving actors in this value chain. Keywords: aberration detection, animal health intelligence, biosurveillance, cluster detection, outbreak signal, temporal monitoring |
| format |
article |
| author |
Dórea FC Vial F |
| author_facet |
Dórea FC Vial F |
| author_sort |
Dórea FC |
| title |
Animal health syndromic surveillance: a systematic literature review of the progress in the last 5 years (2011–2016) |
| title_short |
Animal health syndromic surveillance: a systematic literature review of the progress in the last 5 years (2011–2016) |
| title_full |
Animal health syndromic surveillance: a systematic literature review of the progress in the last 5 years (2011–2016) |
| title_fullStr |
Animal health syndromic surveillance: a systematic literature review of the progress in the last 5 years (2011–2016) |
| title_full_unstemmed |
Animal health syndromic surveillance: a systematic literature review of the progress in the last 5 years (2011–2016) |
| title_sort |
animal health syndromic surveillance: a systematic literature review of the progress in the last 5 years (2011–2016) |
| publisher |
Dove Medical Press |
| publishDate |
2016 |
| url |
https://doaj.org/article/cb430569e5e3463ab15c26ed987d6f0e |
| work_keys_str_mv |
AT doreafc animalhealthsyndromicsurveillanceasystematicliteraturereviewoftheprogressinthelast5years2011ndash2016 AT vialf animalhealthsyndromicsurveillanceasystematicliteraturereviewoftheprogressinthelast5years2011ndash2016 |
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