ArticleActa veterinaria Scandinavica2010
Spatial patterns of bovine corona virus and bovine respiratory syncytial virus in the Swedish beef cattle population.
Article in Acta veterinaria Scandinavica, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 28 citations in OpenAlex.
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- Sero-prevalence, cross-species infection and serological determinants of prevalence of Bovine Coronavirus in Cattle, Sheep and Goats in Ghana.Veterinary microbiology · 2020Article
- Seroprevalence and risk factors of bovine respiratory syncytial virus in cattle in the Nineveh Governorate, Iraq.Veterinary world · 2019Article
- Bovine respiratory syncytial virus and bovine coronavirus antibodies in bulk tank milk - risk factors and spatial analysis.Preventive veterinary medicine · 2016Article
- Comparative molecular epidemiology of two closely related coronaviruses, bovine coronavirus (BCoV) and human coronavirus OC43 (HCoV-OC43), reveals a different evolutionary pattern.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2016Article
- Bovine respiratory syncytial virus and bovine coronavirus in Swedish organic and conventional dairy herds.Acta veterinaria Scandinavica · 2015Article
- Bioinformatics and Molecular Analysis of the Evolutionary Relationship between Bovine Rhinitis A Viruses and Foot-And-Mouth Disease Virus.Bioinformatics and biology insights · 2015Article
- Patterns of between-farm contacts via professionals in Sweden.Acta veterinaria Scandinavica · 2014Article
- Occurrence and phylogenetic analysis of bovine respiratory syncytial virus in outbreaks of respiratory disease in Norway.BMC veterinary research · 2014Article
- Epidemiology, molecular epidemiology and evolution of bovine respiratory syncytial virus.Viruses · 2012Review
- Application of network analysis parameters in risk-based surveillance - examples based on cattle trade data and bovine infections in Sweden.Preventive veterinary medicine · 2012Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBoth bovine coronavirus (BCV) and bovine respiratory syncytial virus (BRSV) infections are currently wide-spread in the Swedish dairy cattle population. Surveys of antibody levels in bulk tank milk have shown very high nationwide prevalences of both BCV and BRSV, with large variations between regions. In the Swedish beef cattle population however, no investigations have yet been performed regarding the prevalence and geographical distribution of BCV and BRSV. A cross-sectional serological survey for BCV and BRSV was carried out in Swedish beef cattle to explore any geographical patterns of these infections.
methodsBlood samples were collected from 2,763 animals located in 2,137 herds and analyzed for presence of antibodies to BCV and BRSV. Moran's I was calculated to assess spatial autocorrelation, and identification of geographical cluster was performed using spatial scan statistics.
resultsAnimals detected positive to BCV or BRSV were predominately located in the central-western and some southern parts of Sweden. Moran's I indicated global spatial autocorrelation. BCV and BRSV appeared to be spatially related: two areas in southern Sweden (Skaraborg and Skåne) had a significantly higher prevalence of BCV (72.5 and 65.5% respectively); almost the same two areas were identified as being high-prevalence clusters for BRSV (69.2 and 66.8% respectively). An area in south-east Sweden (Kronoberg-Blekinge) had lower prevalences for both infections than expected (23.8 and 20.7% for BCV and BRSV respectively). Another area in middle-west Sweden (Värmland-Dalarna) had also a lower prevalence for BRSV (7.9%). Areas with beef herd density > 10 per 100 km(2) were found to be at significantly higher risk of being part of high-prevalence clusters.
conclusionThese results form a basis for further investigations of between-herds dynamics and risk factors for these infections in order to design effective control strategies.
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