Journal of Veterinary Clinics (한국임상수의학회지)

The Korean Society of Veterinary Clinics (한국임상수의학회)
권호 표지
DOI QR코드

DOI QR Code

Application of Species Distribution Model for Predicting Areas at Risk of Highly Pathogenic Avian Influenza in the Republic of Korea

종 분포 모형을 이용한 국내 고병원성 조류인플루엔자 발생 위험지역 추정

  • Kim, Euttm (College of Veterinary Medicine and Institute of Veterinary Science, Kangwon National University) ;
  • Pak, Son-Il (College of Veterinary Medicine and Institute of Veterinary Science, Kangwon National University)
  • 김으뜸 (강원대학교 수의과대학 및 동물의학종합연구소) ;
  • 박선일 (강원대학교 수의과대학 및 동물의학종합연구소)
  • Received : 2019.01.09
  • Accepted : 2019.02.01
  • Published : 2019.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

While research findings suggest that the highly pathogenic avian influenza (HPAI) is the leading cause of economic loss in Korean poultry industry with an estimated cumulative impact of $909 million since 2003, identifying the environmental and anthropogenic risk factors involved remains a challenge. The objective of this study was to identify areas at high risk for potential HPAI outbreaks according to the likelihood of HPAI virus detection in wild birds. This study integrates spatial information regarding HPAI surveillance with relevant demographic and environmental factors collected between 2003 and 2018. The Maximum Entropy (Maxent) species distribution modeling with presence-only data was used to model the spatial risk of HPAI virus. We used historical data on HPAI occurrence in wild birds during the period 2003-2018, collected by the National Quarantine Inspection Agency of Korea. The database contains a total of 1,065 HPAI cases (farms) tied to 168 unique locations for wild birds. Among the environmental variables, the most effective predictors of the potential distribution of HPAI in wild birds were (in order of importance) altitude, number of HPAI outbreaks at farm-level, daily amount of manure processed and number of wild birds migrated into Korea. The area under the receiver operating characteristic curve for the 10 Maxent replicate runs of the model with twelve variables was 0.855 with a standard deviation of 0.012 which indicates that the model performance was excellent. Results revealed that geographic area at risk of HPAI is heterogeneously distributed throughout the country with higher likelihood in the west and coastal areas. The results may help biosecurity authority to design risk-based surveillance and implementation of control interventions optimized for the areas at highest risk of HPAI outbreak potentials.

Keywords

References

  1. Abdrakhmanov SK, Mukhanbetkaliyev YY, Korennoy FI, Sultanov AA, Kadyrov AS, Kushubaev DB, Bakishev TG. Maximum entropy modeling risk of anthrax in the Republic of Kazakhstan. Prev Vet Med 2017; 144: 149-157. https://doi.org/10.1016/j.prevetmed.2017.06.003
  2. Belkhiria J, Alkhamis MA, Martinez-Lopez B. Application of species distribution modeling for avian influenza surveillance in the United States considering the north America migratory flyways. Sci Rep 2016; 6: 33161. https://doi.org/10.1038/srep33161
  3. Belkhiria J, Hijmans RJ, Boyce W, Crossley BM, Martinez-Lopez B. Identification of high risk areas for avian influenza outbreaks in California using disease distribution models. PLoS One 2018; 13: e0190824. https://doi.org/10.1371/journal.pone.0190824
  4. Boyce WM, Sandrock C, Kreuder-Johnson C, Kelly T, Cardona C. Avian influenza viruses in wild birds: a moving target. Comp Immunol Microbiol Infect Dis 2009; 32: 275-286. https://doi.org/10.1016/j.cimid.2008.01.002
  5. Caron A, Gaidet N, de Garine-Wichatitsky M, Morand S, Cameron EZ. Evolutionary biology, community ecology and avian influenza research. Infect Genet Evol 2009; 9: 298-303. https://doi.org/10.1016/j.meegid.2008.12.001
  6. Carpenter TE. Stochastic, spatially-explicit epidemic models. Rev Sci Tech 2011; 30: 417-424. https://doi.org/10.20506/rst.30.2.2044
  7. de Jong MD, Bach VC, Phan TQ, Vo MH, Tran TT, Nguyen BH, Beld M, Le TP, Truong HK, Nguyen VV, Tran TH, Do QH, Farrar J. Fatal avian influenza A (H5N1) in a child presenting with diarrhea followed by coma. N Engl J Med 2005; 352: 686-691. https://doi.org/10.1056/NEJMoa044307
  8. Elith J, Phillips SJ, Hastie T, Dudik M, Chee YE, Yates CJ. A statistical explanation of MaxEnt for ecologists. Divers Distrib 2011; 17: 43-57. https://doi.org/10.1111/j.1472-4642.2010.00725.x
  9. Gilbert M, Prosser DJ, Zhang G, Artois J, Dhingra MS, Tildesley M, Newman SH, Guo F, Black P, Claes F, Kalpradvidh W, Shin Y, Jeong W, Takekawa JY, Lee H, Xiao X. Could Changes in the Agricultural Landscape of Northeastern China Have Influenced the Long-Distance Transmission of Highly Pathogenic Avian Influenza H5Nx Viruses? Front Vet Sci 2017; 4: 225. https://doi.org/10.3389/fvets.2017.00225
  10. Greiner M, Pfeiffer D, Smith RD. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev Vet Med 2000; 45: 23-41. https://doi.org/10.1016/S0167-5877(00)00115-X
  11. Groepper SR, DeLiberto TJ, Vrtiska MP, Pedersen K, Swafford SR, Hygnstrom SE. Avian influenza virus prevalence in migratory waterfowl in the United States, 2007-2009. Avian Dis 2014; 58: 531-540. https://doi.org/10.1637/10849-042214-Reg.1
  12. Hijmans RJ. Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model. Ecology 2012; 93: 679-688. https://doi.org/10.1890/11-0826.1
  13. Hill NJ, Takekawa JY, Cardona CJ, Meixell BW, Ackerman JT, Runstadler JA, Boyce WM. Cross-seasonal patterns of avian influenza virus in breeding and wintering migratory birds: a flyway perspective. Vector Borne Zoonotic Dis 2012; 12: 243-253. https://doi.org/10.1089/vbz.2010.0246
  14. Kang HM, Jeong OM, Kim MC, Kwon JS, Paek MR, Choi JG, Lee EK, Kim YJ, Kwon JH, Lee YJ. Surveillance of avian influenza virus in wild bird fecal samples from South Korea, 2003-2008. J Wildl Dis 2010; 46: 878-888. https://doi.org/10.7589/0090-3558-46.3.878
  15. Kim H, Lee DK, Mo Y, Kil S, Park C, Lee S. Prediction of landslides occurrence probability under climate change using MaxEnt model. J Environ Impact Assess 2013; 22: 39-50. https://doi.org/10.14249/eia.2013.22.1.039
  16. Korennoy FI, Gulenkin VM, Malone JB, Mores CN, Dudnikov SA, Stevenson MA. Spatio-temporal modeling of the African swine fever epidemic in the Russian Federation, 2007-2012. Spat Spatiotemporal Epidemiol 2014; 11: 135-141. https://doi.org/10.1016/j.sste.2014.04.002
  17. Lee EK, Kang HM, Song BM, Lee YN, Heo GB, Lee HS, Lee YJ, Kim JH. Surveillance of avian influenza viruses in South Korea between 2012 and 2014. Virol J. 2017; 14: 54. https://doi.org/10.1186/s12985-017-0711-y
  18. Li Q, Ren H, Zheng L, Cao W, Zhang A, Zhuang D, Lu L, Jiang H. Ecological niche modeling identifies fine-scale areas at high risk of dengue fever in the pearl river delta, China. Int J Environ Res Public Health 2017; 14: 619. https://doi.org/10.3390/ijerph14060619
  19. Merow C, Smith MJ, Silander JA. A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter. Ecography 2013; 36: 1058-1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x
  20. Meyer A, Dinh TX, Nhu TV, Pham LT, Newman S, Nguyen TTT, Pfeiffer DU, Vergne T. Movement and contact patterns of long-distance free-grazing ducks and avian influenza persistence in Vietnam. PLoS One 2017; 12: e0178241. https://doi.org/10.1371/journal.pone.0178241
  21. Mischler P, Kearney M, McCarroll JC, Scholte RG, Vounatsou P, Malone JB. Environmental and socio-economic risk modelling for Chagas disease in Bolivia. Geospat Health 2012; 6: S59-66. https://doi.org/10.4081/gh.2012.123
  22. NIBR. National Institute of Biological Resources. 2016-2017 Winter waterbird census of Korea. 2017.
  23. Pandit PS, Bunn DA, Pande SA, Aly SS. Modeling highly pathogenic avian influenza transmission in wild birds and poultry in West Bengal, India. Sci Rep 2013; 3: 2175. https://doi.org/10.1038/srep02175
  24. Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions. Ecol Model 2006; 190: 231-259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
  25. Serratosa J, Ribo O, Correia S, Pittman M. EFSA scientific risk assessment on animal health and welfare aspects of avian influenza (EFSA-Q-2004-075). Avian Dis 2007; 51: 501-503. https://doi.org/10.1637/7574-040106R.1
  26. Shiravand B, Tafti AAD, Hanafi-Bojd AA, Almodaresi SA, Mirzaei M, Abai MR. Modeling spatial risk of zoonotic cutaneous leishmaniasis in Central Iran. Acta Trop 2018; 185: 327-335. https://doi.org/10.1016/j.actatropica.2018.06.015
  27. Sturm-Ramirez KM, Ellis T, Bousfield B, Bissett L, Dyrting K, Rehg JE, Poon L, Guan Y, Peiris M, Webster RG. Reemerging H5N1 influenza viruses in Hong Kong in 2002 are highly pathogenic to ducks. J Virol 2004; 78: 4892-4901. https://doi.org/10.1128/JVI.78.9.4892-4901.2004
  28. Tarrant J, Cilliers D, du Preez LH, Weldon C. Spatial assessment of amphibian chytrid fungus (Batrachochytrium dendrobatidis) in South Africa confirms endemic and widespread infection. PLoS One 2013; 8: e69591. https://doi.org/10.1371/journal.pone.0069591