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dc.contributor.authorAlarcón, Laura V.
dc.contributor.authorCipriotti, Pablo A.
dc.contributor.authorMonterubbianessi, Mariela
dc.contributor.authorPerfumo, Carlos
dc.contributor.authorMateu, Enric
dc.contributor.authorAllepuz, Alberto
dc.contributor.otherProducció Animalca
dc.date.accessioned2021-02-03T15:07:13Z
dc.date.available2021-02-03T15:07:13Z
dc.date.issued2019-11-29
dc.identifier.citationAlarcón, Laura V., Pablo A. Cipriotti, Mariela Monterubbianessi, Carlos Perfumo, Enric Mateu, and Alberto Allepuz. 2020. "Network Analysis Of Pig Movements In Argentina: Identification Of Key Farms In The Spread Of Infectious Diseases And Their Biosecurity Levels". Transboundary And Emerging Diseases 67 (3): 1152-1163. doi:10.1111/tbed.13441.ca
dc.identifier.issn1865-1674ca
dc.identifier.urihttp://hdl.handle.net/20.500.12327/1068
dc.description.abstractThis study uses network analysis to evaluate how swine movements in Argentina could contribute to disease spread. Movement data for the 2014–2017 period were obtained from Argentina's online livestock traceability registry and categorized as follows: animals of high genetic value sent to other farms, animals to or from markets, animals sent to finisher operations and slaughterhouse. A network analysis was carried out considering the first three movement types. First, descriptive, centrality and cohesion measures were calculated for each movement type and year. Next, to determine whether networks had a small‐world topology, these were compared with the results from random Erdös–Rényi network simulations. Then, the basic reproductive number (R0) of the genetic network, the group of farms with higher potential for disease spread standing at the top of the production chain, was calculated to identify farms acting as super‐spreaders. Finally, their external biosecurity scores were evaluated. The genetic network in Argentina presented a scale‐free and small‐world topology. Thus, we estimate that disease spread would be fast, preferably to highly connected nodes and with little chances of being contained. Throughout the study, 31 farms were identified as super‐spreaders in the genetic network for all years, while other 55 were super‐spreaders at least once, from an average of 1,613 farms per year. Interestingly, removal of less than 5% of higher degree and betweenness farms resulted in a >90% reduction of R0 indicating that few farms have a key role in disease spread. When biosecurity scores of the most relevant super‐spreaders were examined, it was evident that many were at risk of introducing and disseminating new pathogens across the whole of Argentina's pig production network. These results highlight the usefulness of establishing targeted surveillance and intervention programmes, emphasizing the need for better biosecurity scores in Argentinean swine production units, especially in super‐spreader farms.ca
dc.format.extent32ca
dc.language.isoengca
dc.publisherWileyca
dc.relation.ispartofTransboundary and Emerging Diseasesca
dc.rightsCopyright © 2019 Wileyca
dc.titleNetwork analysis of pig movements in Argentina: Identification of key farms in the spread of infectious diseases and their biosecurity levelsca
dc.typeinfo:eu-repo/semantics/articleca
dc.description.versioninfo:eu-repo/semantics/acceptedVersionca
dc.rights.accessLevelinfo:eu-repo/semantics/openAccess
dc.embargo.terms12 mesosca
dc.subject.udc619ca
dc.identifier.doihttps://doi.org/10.1111/tbed.13441ca
dc.contributor.groupSanitat Animalca


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