Evaluation of the spatial patterns and risk factors, including backyard pigs, for classical swine fever occurrence in Bulgaria using a Bayesian model

Submitted: 10 December 2014
Accepted: 10 December 2014
Published: 1 May 2014
Abstract Views: 1849
PDF: 1007
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

The spatial pattern and epidemiology of backyard pig farming and other low bio-security pig production systems and their role in the occurrence of classical swine fever (CSF) is described and evaluated. A spatial Bayesian model was used to explore the risk factors, including human demographics, socioeconomic and environmental factors. The analyses were performed for Bulgaria, which has a large number of backyard farms (96% of all pig farms in the country are classified as backyard farms), and it is one of the countries for which both backyard pig and farm counts were available. Results reveal that the high-risk areas are typically concentrated in areas with small family farms, high numbers of outgoing pig shipments and low levels of personal consumption (i.e. economically deprived areas). Identification of risk factors and high-risk areas for CSF will allow to targeting risk-based surveillance strategies leading to prevention, control and, ultimately, elimination of the disease in Bulgaria and other countries with similar socio-epidemiological conditions.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

Citations

How to Cite

Martínez-Lòpez, B., Alexandrov, T., Mur, L., Sánchez-Vizcaíno, F., & Sánchez-Vizcaíno, J. M. (2014). Evaluation of the spatial patterns and risk factors, including backyard pigs, for classical swine fever occurrence in Bulgaria using a Bayesian model. Geospatial Health, 8(2), 489–501. https://doi.org/10.4081/gh.2014.38