Analyzing Spatial Dependency of the 2016-2017 Korean HPAI Outbreak to Determine the Effective Culling Radiusopen access
- Authors
- Han, Kwideok; An, Meilan; Ji, Inbae
- Issue Date
- Sep-2021
- Publisher
- MDPI
- Keywords
- highly pathogenic avian influenza; HPAI; spatial random effects logistic model; spatial dependency; spatial autocorrelation; effective culling radius
- Citation
- INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, v.18, no.18
- Indexed
- SCIE
SSCI
SCOPUS
- Journal Title
- INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
- Volume
- 18
- Number
- 18
- URI
- https://scholarworks.dongguk.edu/handle/sw.dongguk/4528
- DOI
- 10.3390/ijerph18189643
- ISSN
- 1661-7827
1660-4601
- Abstract
- Highly pathogenic avian influenza (HPAI) outbreaks are a threat to human health and cause extremely large financial losses to the poultry industry due to containment measures. Determining the most effective control measures, especially the culling radius, to minimize economic impacts yet contain the spread of HPAI is of great importance. This study examines the factors influencing the probability of a farm being infected with HPAI during the 2016-2017 HPAI outbreak in Korea. Using a spatial random effects logistic model, only a few factors commonly associated with a higher risk of HPAI infection were significant. Interestingly, most density-related factors, poultry and farm, were not significantly associated with a higher risk of HPAI infection. The effective culling radius was determined to be two ranges: 0.5-2.2 km and 2.7-3.0 km. This suggests that the spatial heterogeneity, due to local characteristics and/or the characteristics of the HPAI virus(es) involved, should be considered to determine the most effective culling radius in each region. These findings will help strengthen biosecurity control measures at the farm level and enable authorities to quickly respond to HPAI outbreaks with effective countermeasures to suppress the spread of HPAI.
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Collections - College of the Social Science > Department of Food Industrial Management > 1. Journal Articles

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