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https://hdl.handle.net/10495/43918
Título : | Farm management practices, biosecurity and influenza a virus detection in swine farms: a comprehensive study in Colombia |
Autor : | Muskus López, Carlos Enrique Díaz, Andrés Ciuoderis Aponte, Karl Peña, Mario Hernández Ortiz, Juan Osorio, Jorge |
metadata.dc.subject.*: | Epidemiología Epidemiology Enfermedades Transmisibles Communicable Diseases Virus de la Influenza A Influenza A virus Teorema de Bayes Bayes Theorem Cerdo Swine Granjas porcinas Swine farms http://aims.fao.org/aos/agrovoc/c_7555 https://id.nlm.nih.gov/mesh/D004813 https://id.nlm.nih.gov/mesh/D003141 https://id.nlm.nih.gov/mesh/D009980 https://id.nlm.nih.gov/mesh/D001499 |
Fecha de publicación : | 2022 |
Editorial : | BioMed Central |
Citación : | Ciuoderis-Aponte, K., Diaz, A., Muskus, C. et al. Farm management practices, biosecurity and influenza a virus detection in swine farms: a comprehensive study in Colombia. Porc Health Manag 8, 42 (2022). https://doi.org/10.1186/s40813-022-00287-6 |
Resumen : | ABSTRACT: Biosecurity protocols (BP) and good management practices are key to reduce the risk of introduction and transmission of infectious diseases into the pig farms. In this observational cross-sectional study, survey data were collected from 176 pig farms with inventories over 100 sows in Colombia. We analyzed a complex survey dataset to explore the structure and identify clustering patterns using Multiple Correspondence Analysis (MCA) of swine farms in Colombia, and estimated its association with Influenza A virus detection. Two principal dimensions contributed to 27.6% of the dataset variation. Farms with highest contribution to dimension 1 were larger farrow-to-finish farms, using self-replacement of gilts and implementing most of the measures evaluated. In contrast, farms with highest contribution to dimension 2 were medium to large farrow-to-finish farms, but implemented biosecurity in a lower degree. Additionally, two farm clusters were identified by Hierarchical Cluster Analysis (HCA), and the odds of influenza A virus detection was statistically different between clusters (OR 7.29, CI: 1.7,66, p=<0.01). Moreover, after logistic regression analysis, three important variables were associated with higher odds of influenza detection: (1) “location in an area with a high density of pigs”, (2) “farm size”, and (3) “after cleaning and disinfecting, the facilities are allowed to dry before use”. Our results revealed two clustering patterns of swine farms. This systematic analysis of complex survey data identified relationships between biosecurity, husbandry practices and influenza status. This approach helped to identify gaps on biosecurity and key elements for designing successful strategies to prevent and control swine respiratory diseases in the swine industry. |
ISSN : | 2055-5660 |
metadata.dc.identifier.doi: | 10.1186/s40813-022-00287-6 |
Aparece en las colecciones: | Artículos de Revista en Ciencias Médicas |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
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MuskusCarlos_2022_Influenza_Pathogen_Farms.pdf | Artículo de investigación | 1.15 MB | Adobe PDF | Visualizar/Abrir |
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