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Locational Characteristics of Highly Pathogenic Avian Influenza(HPAI) Outbreak Farm

고병원성 조류인플루엔자(HPAI) 발생농가 입지특성

  • KIM, Dong-Hyeon (Dept. of smart regional innovation, Kangwon National University) ;
  • BAE, Sun-Hak (Dept. of Geography Education, Kangwon National University)
  • 김동현 (강원대학교 일반대학원 스마트지역혁신학과) ;
  • 배선학 (강원대학교 지리교육과)
  • Received : 2020.09.14
  • Accepted : 2020.12.03
  • Published : 2020.12.31

Abstract

This study was conducted to identify the location characteristics of infected farms in the areas where livestock diseases were clustered(southern Gyeonggi-do and Chungcheong-do), analyze the probability of disease occurrence in poultry farms, find out the areas corresponding to the conditions, and use them as the basis for prevention of livestock diseases, selection of discriminatory prevention zones, and establishment of prevention strategies and as the basic data for complementary measures. The increase of one poultry farm within a radius of 3-kilometers increases the risk of HPAI infection by 10.9% compared to the previous situation. The increase of 1m in distance from major roads with two lanes or more reduces the probability of HPAI infection by 0.001% compared to the previous situation. If the distance of the poultry farm located with 15 kilometers from a major migratory bird habitat increases by 15 to 30 kilometers, the chance of infection with HPAI is reduced by 46.0%. And if the distance of the same poultry farm increase by more than 30 kilometers, the chances of HPAI infection are reduced by 88.5%. Based on the results of logistic regression, the predicted probability was generated and the actual area of the location condition with 'more than 15 poultry farms within 3km a radius of, within 1km distance from major roads, and within 30km distance from major migratory birds habitat was determined and the infection rate was measured. It is expected that the results of this study will be used as basic data for preparing the data and supplementary measures when the quarantine authorities establish discriminatory quarantine areas and prevention strategies, such as preventive measures for the target areas and farms, or control of vehicles, by identifying the areas where livestock diseases are likely to occur in the region.

본 연구는 가축질병이 밀집되어 발생했던 지역인 경기도 남부-충청도의 감염농가 입지 특성을 파악하여 가금농가의 질병 발생 확률을 분석하고 조건에 해당하는 지역을 도출하여 가축질병 발생예방과 차별적인 방역지역 선정 및 방역전략 설정의 근거와 보완대책의 기초자료로 사용하기 위해 수행되었다. 로지스틱 회귀분석 결과, 반경 3km내 가금농가 1개가 증가하면 HPAI에 감염될 확률이 전 단위에 비해 10.9% 증가한다. 2차선 이상 주요 도로와의 거리 1m가 증가하면 HPAI에 감염될 확률이 전 단위에 비해 0.001% 감소한다. 주요 철새도래지와의 15km 이내에 가금농가가 위치한 경우에서 15~30km로 변화하면 HPAI에 감염될 확률이 46.0% 감소한다. 주요 철새도래지와의 거리가 15km 이내에 가금농가가 위치한 경우에서 30km 이상으로 변화하면 HPAI에 감염될 확률이 88.5% 감소한다. 로지스틱 회귀분석 결과를 바탕으로 예측확률을 생성하고 도출된 입지요인인' 반경 3km내 가금농가 15개 초과, 주요 도로와의 거리 1km이내, 주요 철새도래지와의 거리 30 km이내'의 실제 지역을 도출하고 감염 비율을 측정하였다. 본 연구의 결과가 지역 내에서 가축질병이 발생할 확률이 높은 지역을 판별하여, 방역 주체가 대상 지역과 농가에 대해 선제적 방역을 실시하거나 차량을 통제하는 등의 차별적인 방역지역과 방역전략을 설정할 때, 그 근거와 보완대책 마련에 기초자료로 활용될 수 있을 것이라 기대한다.

Keywords

Acknowledgement

본 논문은 2020년 8월 강원대학교 일반대학원 지리정보학석사학위논문 "가금농가 입지특성과 고병원성 조류인플루엔자(HPAI) 발생과의 관계"의 내용을 일부 수정·보완하여 작성되었습니다. 본 연구는 농림축산식품부의 재원으로 농림식품기술기획평가원(가축질병대응기술개발사업)의 지원을 받아 연구되었습니다(No.318045-3).

References

  1. An, M.L., Ji, I.B., Bae, S.H., Pak, S.I. and Kim, S.T. 2019. An Analysis of HPAI Risk Factors by Characteristics of Poultry Farm. Journal of Rural Development 42(3):173-192.
  2. Anselin, L. 1995. Local indicators of spatial association-LISA. Geographical analysis. 27(2):93-115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
  3. Anselin, L., Bera, A. K. 1998. Spatial Dependence in Linear Regression Models with an Introduction to Spatial Econometrics, in: Ullah, A. and Giles, D. (eds.). 237-289.
  4. Bae, S.H., Jeong, H.Y. and Eom, C.H. 2016. Social Network Type Analysis of Highly Pathogenic Avian Influenza(HPAI) Outbreaks in South Korea, 2014-2016. Journal of the Korean Association of Geographic Information Studies 19(3):114-126. https://doi.org/10.11108/kagis.2016.19.3.114
  5. Bae, S.H., Shin, Y.K., Kim, B.H. and Pak, S.I. 2013. Temporospatial clustering analysis of foot-and-mouth disease transmission in South Korea, 2010-2011. Korean J Vet Res 53(1):49-54. https://doi.org/10.14405/kjvr.2013.53.1.049
  6. Chen, H. X., Shen, H. G., Li, X. L., Zhou, J. Y., Hou, Y. Q., Guo, J. Q., Hu, J. Q. 2006. Seroprevalance and identification of influenza A virus infection from migratory wild waterfowl in China (2004-2005). Journal of Veterinary Medicine Series B 53(4):166-170. https://doi.org/10.1111/j.1439-0450.2006.00940.x
  7. Cho, J.H., Kim, B.E. and Bae, M.K. 2017. A Study on the Characteristic Diagnosis of AI Damage in Chungbuk and the Improvement of Preventive Quarantine Management System. CHUNGBUK FOCUS 134:1-25.
  8. Choi, S.H. and Pak S.I. 2019. Application of a Geographically Weighted Poisson Regression Analysis to Explore Spatial Varying Relationship Between Highly Pathogenic Avian Influenza Incidence and Associated Determinants. Journal of Veterinary Clinics 36(1):7-14. https://doi.org/10.17555/jvc.2019.02.36.1.7
  9. Dunford, M. and Greco, L. 2006. After the three Italies: wealth, inequality and industrial change. Oxford. Blackwell. pp.1-376.
  10. Eom, C.H., Pak, S.I. and Bae, S.H. 2017. Analysis of Potential Infection Site by Highly Pathogenic Avian Influenza Using Model Patterns of Avian Influenza Outbreak Area in Republic of Korea. Journal of the Korean Association of Geographic Information Studies 20(2):60-74. https://doi.org/10.11108/kagis.2017.20.2.060
  11. Hong, S.J., Pak, S.I., Lee, K.N., Cho, K.H. and Lee, G.J. 2018. Spatial Variations of Risk Factors Associated with the Diffusion of Highly Infectious Animal Diseases. Journal of the Korean Cartographic Association 18(1):81-91. https://doi.org/10.16879/jkca.2018.18.1.081
  12. Hosmer, David W., Stanley Lemeshow. 2000. Applied Logistic Regression. Interpretation of the Fitted Logistic Regression Model. John Wiley & Sons, New York, pp.47-90.
  13. Lee, G.J., Pak, S.I, Lee, K.N., Park, J.H. and Hong, S.J. 2019. Animal Infectious Disease Preventive Zone Based on Livestock Vehicle Movement Network. Journal of the Korea Academia-Industrial cooperation Society 20(1):189-199. https://doi.org/10.5762/KAIS.2019.20.1.189
  14. Lee, S.I., Cho, D.H., Sohn, H.G. and Chae M.O. 2010. A GIS-Based Method for Delineating Spatial Clusters: A Modified AMOEBA Technique. Journal of the Korean Geographical Society 45(4):502-520.
  15. Lee, S.S., and Wong, N.S. 2011. The clustering and transmission dynamics of pandemic influenza A (H1N1) 2009 cases in Hong Kong. Journal of Infection 63(4):274-280. https://doi.org/10.1016/j.jinf.2011.03.011
  16. Lee, Y.J., Park, G.A. and Kim, S.J., 2006. Analysis of Landslide Hazard Area using Logistic Regression Analysis and AHP (Analytical Hierarchy Process) Approach. JOURNAL OF THE KOREAN SOCIETY OF CIVIL ENGINEERS D 26(5D):861-867.
  17. Liu, J., Xiao, H., Lei, F., Zhu, Q., Qin, K., Zhang, X. W. and Ma, J. 2005. Highly pathogenic H5N1 influenza virus infection in migratory birds. Science 309(5738):1206-1206. https://doi.org/10.1126/science.1115273
  18. Long, J. Scott. 1997. Regression models for categorical and limited dependent variables. Advanced quantitative techniques in the social sciences(7), Indiana University, USA, pp.1-328.
  19. MAFRA(Ministry of Agriculture, Food and Rural Affairs). 2017. 2016-2017 Highly Pathogenic Avian Influenza(HPAI) epidemiology reports. pp.1-234.
  20. Moon, O.K., Cho, S.B. and Bae, S.K. 2015. Spatio-Temporal Clustering Analysis of HPAI Outbreaks in South Korea, 2014. Journal of the Korean Association of Geographic Information Studies 18(3):89-101. https://doi.org/10.11108/kagis.2015.18.3.089
  21. Ord, J. K. and Getis, A. 1995. Local spatial autocorrelation statistics: distributional issues and an application. Geographical analysis 27(4):286-306. https://doi.org/10.1111/j.1538-4632.1995.tb00912.x
  22. Pak, S.I. and Bae, S.H. 2016. Link Between Service Coverage of Slaughterhouse and the Potential Disease Transmission: Analyzing the Livestock Movements Data for Simulation Exercise (CPX). Journal of the Korean Cartographic Association 16(1)67-77. https://doi.org/10.16879/jkca.2016.16.1.067
  23. Pak, S.I., Jheong, W.H. and Lee, K.N. 2019. A GIS-Based Spatial Analysis for Enhancing Classification of the Vulnerable Geographical Region of Highly Pathogenic Avian Influenza Outbreak in Korea. Journal of Veterinary Clinics 36(1):15-22. https://doi.org/10.17555/jvc.2019.02.36.1.15
  24. Park, H., Bae, S.H. and Pak, S.I. 2016. Properties of a Social Network Topology of Livestock Movements to Slaughterhouse in Korea. Journal of Veterinary Clinics 33(5):278-285. https://doi.org/10.17555/jvc.2016.10.33.5.278
  25. Subbarao, K. and Katz, J. 2000. Avian influenza viruses infecting humans. Cellular and Molecular Life Sciences 57(12):1770-1784. https://doi.org/10.1007/PL00000657
  26. UCLA, LOGIT REGRESSION | R DATA ANALYSIS EXAMPLES. https://stats.idre.ucla.edu/r/dae/logit-regression/. (Accessed May 6, 2020).
  27. WHO(World Health Organization). 2013. H5N1 Highly Pathogenic Avian Influenza: timeline of major events. http://www.who.int/influenza/human_animal_interface/en/. (Accessed May 6, 2020).
  28. Yeon, Y.K. 2011. Evaluation and Analysis of Gwangwon-do Landslide Susceptibility Using Logistic Regression. Journal of the Korean Association of Geographic Information Studies 14(4):116-127. https://doi.org/10.11108/kagis.2011.14.4.116
  29. Yoo, B.O., Park, J.H., Park, Y.B., Jung, S.Y. and Lee, K.S. 2016. Assessment of the Distributional Probability for Evergreen Broad-Leaved Forests(EBLFs) Using a Logistic Regression Model. Journal of the Korean Association of Geographic Information Studies 19(1):94-105. https://doi.org/10.11108/kagis.2016.19.1.094