DOI QR코드

DOI QR Code

공간가중 포아송 회귀모형을 이용한 고병원성 조류인플루엔자 발생에 영향을 미치는 결정인자의 공간이질성 분석

Application of a Geographically Weighted Poisson Regression Analysis to Explore Spatial Varying Relationship Between Highly Pathogenic Avian Influenza Incidence and Associated Determinants

  • 최성현 (강원대학교 수의과대학 및 동물의학종합연구소) ;
  • 박선일 (강원대학교 수의과대학 및 동물의학종합연구소)
  • Choi, Sung-Hyun (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)
  • 투고 : 2018.06.12
  • 심사 : 2018.06.23
  • 발행 : 2019.02.28

초록

In South Korea, six large outbreaks of highly pathogenic avian influenza (HPAI) have occurred since the first confirmation in 2003 from chickens. For the past 15 years, HPAI outbreaks have become an annual phenomenon throughout the country and has extended to wider regions, across rural and urban environments. An understanding of the spatial epidemiology of HPAI occurrence is essential in assessing and managing the risk of the infection; however, local spatial variations of relationship between HPAI incidences in Korea and related risk factors have rarely been derived. This study examined whether spatial heterogeneity exists in this relationship, using a geographically weighted Poisson regression (GWPR) model. The outcome variable was the number of HPAI-positive farms at 252 Si-Gun-Gu (administrative boundaries in Korea) level notified to government authority during the period from January 2014 to April 2016. This response variable was regressed to a set of sociodemographic and topographic predictors, including the number of wild birds infected with HPAI virus, the number of wintering birds and their species migrated into Korea, the movement frequency of vehicles carrying animals, the volume of manure treated per day, the number of livestock farms, and mean elevation. Both global and local modeling techniques were employed to fit the model. From 2014 to 2016, a total of 403 HPAI-positive farms were reported with high incidence especially in western coastal regions, ranging from 0 to 74. The results of this study show that local model (adjusted R-square = 0.801, AIC = 954.5) has great advantages over corresponding global model (adjusted R-square = 0.408, AIC = 2323.1) in terms of model fitting and performance. The relationship between HPAI incidence in Korea and seven predictors under consideration were significantly spatially non-stationary, contrary to assumptions in the global model. The comparison between global Poisson and GWPR results indicated that a place-specific spatial analysis not only fit the data better, but also provided insights into understanding the non-stationarity of the associations between the HPAI and associated determinants. We demonstrated that an empirically derived GWPR model has the potential to serve as a useful tool for assessing spatially varying characteristics of HPAI incidences for a given local area and predicting the risk area of HPAI occurrence. Considering the prominent burden of HPAI this study provides more insights into spatial targeting of enhanced surveillance and control strategies in high-risk regions against HPAI outbreaks.

키워드

참고문헌

  1. Alves AT, Nobre FF, Waller LA. Exploring spatial patterns in the associations between local AIDS incidence and socioeconomic and demographic variables in the state of Rio de Janeiro, Brazil. Spat Spatiotemporal Epidemiol 2016; 17: 85-93. https://doi.org/10.1016/j.sste.2016.04.008
  2. Brunsdon C, Fotheringham AS, Charlton ME. Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr Anal 1996; 28: 281-298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
  3. Cahill M, Mulligan G. Using geographically weighted regression to explore local crime patterns. Soc Sci Comput Rev 2007; 25: 174-193. https://doi.org/10.1177/0894439307298925
  4. Carrel M, Escamilla V, Messina J, Giebultowicz S, Winston J, Yunus M, Streatfield PK, Emch M. Diarrheal disease risk in rural Bangladesh decreases as tubewell density increases: a zero-inflated and geographically weighted analysis. Int J Health Geogr 2011; 10: 41. https://doi.org/10.1186/1476-072X-10-41
  5. Charlton M, Fotheringham A. Geographically weighted regression white paper. National Centre for Geocomputation, National University of Ireland Maynooth, 2009.
  6. Chen VY, Yang TC. SAS macro programs for geographically weighted generalized linear modeling with spatial point data: applications to health research. Comput Methods Programs Biomed 2012; 107: 262-273. https://doi.org/10.1016/j.cmpb.2011.10.006
  7. Chen Q, Mei K, Dahlgren RA, Wang T, Gong J, Zhang M. Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression. Sci Total Environ 2016; 572: 450-466. https://doi.org/10.1016/j.scitotenv.2016.08.052
  8. Diniz-Filho JAF, Bini LM, Hawkins BA. Spatial autocorrelation and red herrings in geographical ecology. Glob Ecol Biogeogr 2003; 12: 53-64. https://doi.org/10.1046/j.1466-822X.2003.00322.x
  9. Fotheringham AS, Brunsdon C, Charlton M. Geographically weighted regression: The analysis of spatially varying relationships. Chichester: John Wiley & Sons Ltd. 2012.
  10. Gao J, Li S. Detecting spatially non-stationary and scaledependent relationships between urban landscape fragmentation and related factors using geographically weighted regression. Appl Geogr 2011; 31: 292-302. https://doi.org/10.1016/j.apgeog.2010.06.003
  11. Graham MH. Confronting multicollinearity in ecological multiple regression. Ecology 2003; 84: 2809-2815. https://doi.org/10.1890/02-3114
  12. Guo L, Ma Z, Zhang L. Comparison of bandwidth selection in application of geographically weighted regression: a case study. Can J For Res 2008; 38: 2526-2534. https://doi.org/10.1139/X08-091
  13. Guo C, Du Y, Shen SQ, Lao XQ, Qian J, Ou CQ. Spatiotemporal analysis of tuberculosis incidence and its associated factors in mainland China. Epidemiol Infect 2017; 145: 2510-2519. https://doi.org/10.1017/S0950268817001133
  14. Jones JP III, Hanham RQ. Contingency, realism and the expansion method. Geogr Anal 1995; 27: 185-207. https://doi.org/10.1111/j.1538-4632.1995.tb00905.x
  15. Lee G, Pak SI. A GIS-based mapping to identify locations at risk for highly pathogenic avian influenza virus outbreak in Korea. J Vet Clin 2017; 34: 146-151. https://doi.org/10.17555/jvc.2017.04.34.2.146
  16. Lin CH, Wen TH. Using geographically weighted regression (GWR) to explore spatial varying relationships of immature mosquitoes and human densities with the incidence of dengue. Int J Environ Res Public Health 2011; 8: 2798-2815. https://doi.org/10.3390/ijerph8072798
  17. Lu J, Zhang L. Geographically local linear mixed models for tree height-diameter relationship. For Sci 2012; 58: 75-84. https://doi.org/10.5849/forsci.09-123
  18. Ma Z, Zuckerberg B, Porter WF, Zhang L. Use of localized descriptive statistics for exploring the spatial pattern changes of bird species richness at multiple scales. Appl Geogr 2012; 32: 185-194. https://doi.org/10.1016/j.apgeog.2011.05.005
  19. Matthews SA, Yang TC. Mapping the results of local statistics: Using geographically weighted regression. Demogr Res 2012; 26: 151-166. https://doi.org/10.4054/DemRes.2012.26.6
  20. Nakaya T, Fotheringham AS, Brunsdon C, Charlton M. Geographically weighted poisson regression for disease association mapping. Stat Med 2005; 24: 2695-2717. https://doi.org/10.1002/sim.2129
  21. NIBR. 2014-2015 winter waterbird census of Korea. National Institute of Biological Resources. Incheon, 2015.
  22. Tu J, Xia Z. Examining spatially varying relationships between land use and water quality using geographically weighted regression I: model design and evaluation. Sci Total Environ 2008; 407: 358-378. https://doi.org/10.1016/j.scitotenv.2008.09.031
  23. Wei W, Yuan-Yuan J, Ci Y, Ahan A, Ming-Qin C. Local spatial variations analysis of smear-positive tuberculosis in Xinjiang using geographically weighted regression model. BMC Public Health 2016; 16: 1058. https://doi.org/10.1186/s12889-016-3723-4
  24. Yang TC, Shoff C, Matthews SA. Examining the spatially non-stationary associations between the second demographic transition and infant mortality: A poisson GWR approach. Spat Demogr 2013; 1: 17-40. https://doi.org/10.1007/BF03354885
  25. Yu DL. Spatially varying development mechanisms in the Greater Beijing Area: a geographically weighted regression investigation. Ann Reg Sci 2006; 40: 173-190. https://doi.org/10.1007/s00168-005-0038-2
  26. Zhang L, Bi H, Cheng P, Davis CJ. Modeling spatial variation in tree diameter-height relationships. For Ecol Manag 2004; 189: 317-329. https://doi.org/10.1016/j.foreco.2003.09.004
  27. Zhang L, Gove JH. Spatial assessment of model errors from four regression techniques. For Sci 2005; 51: 334-346.
  28. Zhen Z, Li F, Liu Z, Liu C, Zhao Y, Ma Z, Zhang L. Geographically local modeling of occurrence, count, and volume of downwood in Northeast China. Appl Geogr 2013; 37: 114-126. https://doi.org/10.1016/j.apgeog.2012.11.003

피인용 문헌

  1. 고병원성 조류인플루엔자(HPAI) 발생농가 입지특성 vol.23, pp.4, 2020, https://doi.org/10.11108/kagis.2020.23.4.140