한국측량학회지 (Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography)

  • 제31권3호
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  • Pages.209-219
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  • 2013
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  • 1598-4850(pISSN)
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  • 2288-260X(eISSN)
한국측량학회 (Korean Society of Surveying, Geodesy, Photogrammetry and Cartography)
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행위자 기반 공간 모델을 이용한 구제역 확산 시뮬레이션

Foot-and-mouth disease spread simulation using agent-based spatial model

  • 투고 : 2013.04.18
  • 심사 : 2013.06.14
  • 발행 : 2013.06.30
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초록

역학 모델은 질병 확산에 대한 시뮬레이션 및 관련 방역대책을 수립하는데 유용하며, 개체들의 접촉을 통해 전파되는 질병의 공간 확산에 대한 자세한 이해를 가능하게 한다. 이 연구에서는 공간에서 개체 간의 상호작용에 의한 결과로 구제역 전염병의 확산을 시뮬레이션하기 위해 GIS와 통합된 행위자 기반 공간 모델을 제안하고자 한다. 설계된 모델은 모집단, 시간, 공간이라는 세 요소를 고려하여 축산장 간의 간접접촉을 묘사하였다. 모집단의 역학관계는 2010년 경상북도 안동시에서 발생한 구제역 사례를 기준으로 하였으며, 도로를 주행하는 차량에 의한 간접접촉으로 전염병이 전파하는 것으로 설계하였다. 확산 모델은 구제역 전파 확률, 질병에 대한 여러 상태, 질병의 확산 시간, 감염률, 잠복기 및 기타 매개변수 간의 관계를 수식으로 표현하였다. 모델을 이용하여 구제역 발생 상황을 예측하면서 다양한 시나리오를 적용해서 모의실험하였다. 구제역 발생 상황에서 방역 전략을 선정하기 위해 제시된 방법을 이용하여 방역조치를 다양하게 실험하는 것은 구제역 확산을 통제하는 데 중요한 역할을 할 것으로 기대된다.

Epidemiological models on disease spread attempt to simulate disease transmission and associated control processes and such models contribute to greater understanding of disease spatial diffusion through of individual's contacts. The objective of this study is to develop an agent-based modeling(ABM) approach that integrates geographic information systems(GIS) to simulate the spread of FMD in spatial environment. This model considered three elements: population, time and space, and assumed that the disease would be transmitted between farms via vehicle along the roads. The model is implemented using FMD outbreak data in Andong city of South Korea in 2010 as a case study. In the model, FMD is described with the mathematical model of transmission probability, the distance of the two individuals, latent period, and other parameters. The results show that the GIS-agent based model designed for this study can be easily customized to study the spread dynamics of FMD by adjusting the disease parameters. In addition, the proposed model is used to measure the effectiveness of different control strategies to intervene the FMD spread.

키워드

참고문헌

  1. Bates, T.W., Thurmond, M.C., and Carpenter, T.E. (2003), Description of an epidemic simulation model for use in evaluating strategies to control an outbreak of foot-andmouth disease, Am J Vet Res, Vol. 64, pp. 195-204. https://doi.org/10.2460/ajvr.2003.64.195
  2. Carpenter, C.V. (2004), Agent-based modeling of seasonal population movement and the spread of the 1918-1919 flu, American Journal of Human Biology, Vol. 16, pp. 197-198.
  3. Chae, S.B., Jo, H.H., and Moon , H.T. (2007), Understanding and Application of Agent-Based Model, Physics and High Technology, Vol. 16, pp. 10-14.
  4. Crosetto, M., Tarantola, S., and Saltelli, A. (2000), Sensitivity and uncer tainty ana;ysis in spatial modelling based on GIS, Agriculture, Ecosystems and Environment, Vol. 81, pp. 71-79. https://doi.org/10.1016/S0167-8809(00)00169-9
  5. Gong, J.H., Sun, Z.L., and Li, X.W. (2003), Simulation and analysis of control of severe acute respiratory syndrome, Journal of Remote Sensing, Vol. 21, pp. 260-265.
  6. Deng, H.Z., Chi, Y. and Tang, Y.J. (2004) Multi agentbased simulation of disease infection. Computer simulation, Vol. 21, pp.167-175.
  7. Diggle, P.J. (2006), Spatio-temporal point processes, partial likelihood, foot and mouth disease, Statistical Methods in Medical Research, Vol. 15, pp. 325-336. https://doi.org/10.1191/0962280206sm454oa
  8. Dion, E., Van Schalkwyk, L., and Lambin, E.F. (2011), The landscape epidemiology of foot-and-mouth disease in South Africa: A spatially-explicit multi-agent simulation, JEcolModel. Vol. 222, pp. 2059-2072. https://doi.org/10.1016/j.ecolmodel.2011.03.026
  9. Dube, C., Stevenson, M.A., Garner, M.G., Sanson, R.L., Corso, B.A., Harve,y N., Griffin, J., Wilesmith, J.W., and Estrada, C. (2007), A comparison of predictions made by three simulation models of foot-and-mouth disease, N Z Vet J, Vol. 55, pp. 280-288. https://doi.org/10.1080/00480169.2007.36782
  10. Hagenaars, T.J., A. Dekker, de Jong, M.C., and Eble, P.L. (2011), Estimation of foot and mouth disease transmission parameters, using outbreak data and transmission experiments, RevSciTech, Vol. 30, pp. 467- 477.
  11. Iman, R.L. and Helten, J.C. (1988), An Investication of Uncertainly and Sensitivity Ana;ysis Techiquies for Computer Models, Risk Analysis, Vol. 8. pp. 71-90. https://doi.org/10.1111/j.1539-6924.1988.tb01155.x
  12. Keeling, M.J. (2005), Models of foot-and-mouth disease, ProcBiolSci, Vol. 272, pp. 1195-1202.
  13. Kim, T., Hwang, W., Zhang, A., Surajit, S., and Murali, R. (2010), Multi-Agent Modeling of the South Korean Avian Influenza Epidemic, BMC Infectious Diseases, 10:236. https://doi.org/10.1186/1471-2334-10-236
  14. Ko, Y. (2011), A Study of Cellular Auttomata, Master's thesis, Chonnam National University, Korea, 48p.
  15. Kostova, K., (2004), On The Use Of Models To Assess Foot-And-Mouth Disease Transmission And Control. Lawrence Livermore National Laboratory.
  16. Wang, J., Xiong, J., Yang, K., Peng, S., and Xu, Q. (2009), Use of GIS Agent-based modeling to simulate the Spread of Influenza, in Proceedings of Geoinformatics, pp. 1-6.
  17. Lee, S.W. (2011) Time-series analysis of epidemic model and consideration on follow-up model, Master's thesis, Korea University, Seoul, Korea, 86p,
  18. Morris, R.S., Wilesmith, J.W., Stern, M.W., Sanson, R.L., and Stevenson, M.A. (2001), Predictive spatial modelling of alternative control strategies for the footand- mouth disease epidemic in Great Britain, Vet Rec, Vol. 149, pp. 137-144. https://doi.org/10.1136/vr.149.5.137
  19. NetLogo, http://ccl.northwestern.edu/netlogo/ (Accessed Feb. 15, 2013).
  20. Perez, L. and Dragicevic, S. (2009), An agent-based approach for modeling dynamics of contagious disease spread. International Journal of Health Geographics, Vol. 8, p. 50. https://doi.org/10.1186/1476-072X-8-50
  21. QIA(Animal, Plant and Fisheries Quarantine and Inspection Agency). (2011) Epidemiological report of the 2010-2011 foot-and-mouth disease epidemic, QIA, pp. 209-274.
  22. Silverman, B.W. (1986), Density estimation for statistics and data analysis, Vol. 26 of Monographs on Statistics and Applied Probability, Chapman and Hall, London.
  23. Statistics Korea, http://kostat.go.kr/ (Accessed Jan. 15, 2013).
  24. Tildesley, M.J. and Keeling, M.J. (2008), Modelling footand- mouth disease: A comparison between the UK and Denmark, Prev Vet Med, Vol. 85, pp. 107-124. https://doi.org/10.1016/j.prevetmed.2008.01.008
  25. Wilensky, U. (1999) NetLogo 5.0.2. User Manual, http://ccl.northwestern.edu/netlogo/ (Accessed Feb. 15, 2013).
  26. Yeon, N. (2011), A Study on Scenario Screening for Stochastic Disease Spreading Models using Simulation, Master's thesis., KAIST, Seoul, Korea, 56p.

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