GIS和遥感应用于传染病流行病学研究
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摘要
据美国联邦地理数据委员会(Federal Geographic Data Committee，FGDC)的评估，政府部门全部数据的80-90％是带有地理位置信息的，这自然也包括卫生部门的数据。这种情况对于其它国家也是相似的。而地理信息系统(Geographic Information Systems，GIS)和遥感(Remote Sensing，RS)具有强大的地理空间数据获取、管理、处理、分析和显示的能力，这使得GIS、遥感等空间信息技术在卫生领域上的应用具有先天的优势。遥感能够快速、大范围获取空间数据，随着遥感领域的发展，新的卫星和传感器系统被送入轨道，依靠这些种类繁多的空间探测器和日益完善的参数反演方法，丰富的大气、海洋、陆面信息能够被获取，为卫生领域中疾病发生与各类环境相关性分析、疾病时空建模等提供了数据支持。而GIS除了继续完善和发展其传统的基于地图的空间分析功能外，也开始弥补其由于历史原因所造成的空间数据分析功能的不足。在GIS与遥感的集成方面也有长足的发展，目前的主流GIS和遥感软件基本上可以综合分析常用栅格和矢量格式的地理数据。本研究以乙肝(Hepatitis B)和高致病性禽流感(Highly Pathogenic Avian Influenza，HPAI)两种疾病为例，研究了疾病制图(disease mapping)、聚类分析(clustering analysis)、生态分析(ecological analysis)等传染病流行病学研究主题。综合本文的研究工作，主要取得以下几方面的成果：
(1) 探索性空间数据分析(Exploratory Spatial Data Analysis，ESDA)技术和Kriging插值方法相结合应用于我国乙肝地理分布研究Kriging插值方法是GIS地统计(Geostatistics)分析技术的核心，其在地质学、土壤学、气候学等领域具有广泛的应用。而其应用于疾病制图却不多见。本研究首先对我国1994-1998乙肝发病率数据进行了探索性空间数据分析，在此基础上成功应用Kriging方法制作了这5年我国乙肝发病地理分布图。结果显示，我国乙肝发病率地理分布特点为：西北内蒙和甘肃一带、中南和华东部分地区和东北部分省市为高发地区。长江以南高于长江以北。东部沿海高于西部边疆。通过发病率地理分布图，可以直观地了解我国乙肝疾病发生情况，为决策提供了初步依据。也为进一步的病因分析，环境相关研究提供了重要的
In terms of the evaluation of Federal Geographic Data Committee (FGDC), about 80-90 percent of government data, which naturally include those from health department, are georeferenced. This situation makes no differences in other countries. Geographic Information Systems (GIS) and Remote Sensing (RS) have strong ability of acquirement, management, processing, analyzing and visualization of georeferenced data. So they have inherent advantages in health applications. RS is capable of obtaining spatial data rapidly and widely. With the advance in RS research, new satellites and sensors have been launched into space. Resort to various spaceborne tools, plenty of atmospheric, oceanic and terrestrial information can be acquired, which provides data support for heath research. In addition to perfect and develop traditional map based analysis, GIS also begins to improve its spatial statistic analysis ability, which is currently weak in GIS due to historical causes of GIS development. In the integration of GIS and RS, great improvements have been made. The current mainstream GIS and RS software packages are basically able to comprehensively analyze raster and vector data with most commonly used formats. Taking Hepatitis B and Highly Pathogenic Avian Influenza (HPAI) as examples, this paper researches on disease mapping, clustering analysis, ecological analysis etc. issues of epidemiological research of infectious diseases. To sum up the work of this paper, the following achievements have been gained:
(1) Disease mapping of Hepatitis B with Exploratory Spatial Data Analysis (ESDA) techniques and Kriging methods.
(1) 探索性空间数据分析(Exploratory Spatial Data Analysis，ESDA)技术和Kriging插值方法相结合应用于我国乙肝地理分布研究Kriging插值方法是GIS地统计(Geostatistics)分析技术的核心，其在地质学、土壤学、气候学等领域具有广泛的应用。而其应用于疾病制图却不多见。本研究首先对我国1994-1998乙肝发病率数据进行了探索性空间数据分析，在此基础上成功应用Kriging方法制作了这5年我国乙肝发病地理分布图。结果显示，我国乙肝发病率地理分布特点为：西北内蒙和甘肃一带、中南和华东部分地区和东北部分省市为高发地区。长江以南高于长江以北。东部沿海高于西部边疆。通过发病率地理分布图，可以直观地了解我国乙肝疾病发生情况，为决策提供了初步依据。也为进一步的病因分析，环境相关研究提供了重要的
In terms of the evaluation of Federal Geographic Data Committee (FGDC), about 80-90 percent of government data, which naturally include those from health department, are georeferenced. This situation makes no differences in other countries. Geographic Information Systems (GIS) and Remote Sensing (RS) have strong ability of acquirement, management, processing, analyzing and visualization of georeferenced data. So they have inherent advantages in health applications. RS is capable of obtaining spatial data rapidly and widely. With the advance in RS research, new satellites and sensors have been launched into space. Resort to various spaceborne tools, plenty of atmospheric, oceanic and terrestrial information can be acquired, which provides data support for heath research. In addition to perfect and develop traditional map based analysis, GIS also begins to improve its spatial statistic analysis ability, which is currently weak in GIS due to historical causes of GIS development. In the integration of GIS and RS, great improvements have been made. The current mainstream GIS and RS software packages are basically able to comprehensively analyze raster and vector data with most commonly used formats. Taking Hepatitis B and Highly Pathogenic Avian Influenza (HPAI) as examples, this paper researches on disease mapping, clustering analysis, ecological analysis etc. issues of epidemiological research of infectious diseases. To sum up the work of this paper, the following achievements have been gained:
(1) Disease mapping of Hepatitis B with Exploratory Spatial Data Analysis (ESDA) techniques and Kriging methods.
引文
1. Ahearn, S.C., and De Rooy, C. Monitoring the effects of dracunculiasis remediation on agricultural productivity using satellite data. International Journal of Remote Sensing, 1996(17) : 917-29.
2. Alt, K.W., and Vach, W. The reconstruction of 'genetic kinshi p' in prehistoric burial complexes - problems and. statistics. In: Bock, H.H., and Ihm, P. Classification, data analysis, a nd knowledge organization. Berlin: Springer Verlag, 1990.
3. Anselin, L. GIS Research infrastructure for spatial analysis of real estate markets. Journal of housing Research, 1998(9):113-33.
4. Anselin, L. Local indicators of spatial association. Geogr. A nal., 1995(27): 93-115.
5. Anselin, L. Spatial Economics: Methods and Models. Dordrecht, The Metherlands: Kluwer Academic Publishers, 1988: 284.
6. Bailey, T.C. Spatial statistical methods in health. Cad Saude Publ, 2001(17): 1083-1098.
7. Beck, L. R., Lobitz, B. M., and Wood, B. L. Remote Sensing and H uman Health: new sensors and new opportunities. Emerg infect dis, 2000(6): 217-226.
8. Beck, L.R., Rodriguez, M. H., Dister, S. W., Rodriguez, A. D., Washino, R. K., and Roberts, D.R. Assessment of a remote sensing based model for predicting malaria transmission risk in villages of Chiapas, Mexico. Am J Trop Med Hyg, 1997(56): 99-106.
9. Besag, J. and Newell, J. The detection of clusters in rare di seases, Journal of the Royal Statistical. Society, Series A 1991(154): 143-155.
10. Besag, J. Nearest-neighbor systems and the auto-logistic mode 1 for binary data. Journal of the Royal Statistics Society, S eries B, 1972(36): 75-83.
11. Besag, J. Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistics Society, Series B, 1974(36): 192-236.
12. Bithell, J.F. The choice of test for detecting raised disease risk near a point source. Stat Med, 1995(14): 2309-2322.
13. Boulos, M. N. K. Towards evidence-based, GIS-driven national sp atial health information infrastructure and surveillance services in the United Kingdom. International Journal of Health Geographics, 2004(3): Available online at: http://www. ij-healt hgeographics. com/content/3/1/1.
14. Carrat, F., and Valleron, A. J. Epidemiologic mapping using th??e "kriging" method: application to an influenza-like illness epidemic in France. American Journal of Epidemiology, 1992(1 35): 1293-1300.
15. Center for Space Research. University of Texas, Austin; 1996.Archived data available at the following URL: http://www.csr.utexas. edu.
16. Chen, H., Smith, G. J., Zhang, S. Y., Qin, K., Wang, J., and Li,K. S. Avian flu: H5N1 virus outbreak in migratory waterfowl.Nature. 2005(436): 191-2.
17. Clarke, K. C., Osleeb, J. R., Sherry, J. M., Meert, J. P., and Larsson, R. W. The use of remote sensing and geographic information systems in UNICEF's dracunculiasis (Guinea worm) eradication effort. Prev Vet Med, 1990(11): 229-35.
18. Cliff, A. D., and Ord, J. K. Spatial processes: models and appl ications. London: Pion, 1981.
19. Committee on Earth Observation Satellites. Coordination for the Next Decade (1995 CEOS Yearbook). UK: European Space Agency. Smith System Engineering Ltd, 1995:
20. Crist, E. P., and Cicone, R. C. A physically based transformation of Thematic Mapper data - The TM Tasseled Cap. In: IEEE Trans Geosciences and Remote Sensing. 1984(22): 256-63.
21. Croner, C.M., and De Cola, L. visualization of Disease survei llance data with geostatistics. Presented at UNECE (United Na tions Economic Commission for Europe) work session on methodo logyical issues involving integration of statistics and geogr aphy. 2001: available at: http://www.unece.org/stats/document s/2001/09/gis/25.e. pdf.
22. Cross, E. R., Newcomb, W. W., and Tucker, C. J. Use of weather data and remote sensing to predict the geographic and seasonal distribution of Phlebotomus paptasi in Southwest Asia. Am J Trop Med Hyg, 1996(54): 530-6.
23. Curran, P. J., Atkinson, P. M., and Foody, G. M., Linking remote sensing, land cover and disease. Adv Parasitol, 2000(47): 37-80.
24. Cuzick, J., and Edwards, R. Spatial clustering for inhomogene ous populations. J. Roy. Statist. Soc. B, 1990(52): 73-104.
25. Daley, D. J., and Gani, J. Epidemic Modelling. UK: Cambridge U niv. Press, 1999.
26. Diggle, P., Chetwynd, A., Haggkvist, R. and Morris, S. Second-order analysis of space-time clustering. Statistical Methods in Medical Research, 1995(4): 124-136.
27. Diggle, P.J. Point process modelling in environmental epidemi ology. In: Barnett, V. and Turkman, K. F. Statistics for the E nvironment. Chichester: John Wiley, 1993.
28. Diggle, P.J., and Chetwynd, A. G. Second-order analysis of spa??tial clustering for inhomogeneous populations. Biometrics, 19 91(47): 1155-1163.
29. Dister, S. W., Beck, L. R., Wood, B.L., Falco, R., and Fish, D. The use of GIS and remote sensing technologies in a landscape approach to the study of Lyme disease transmission risk. In: Proceedings of GIS'93: Geographic Information Systems in Forestry, Environmental and Natural Resource Management. Vancouver, Canada, 1993.
30. Dister, S.W., Beck, L. R., Wood, B. L., Falco, R., and Fish, D. The use of GIS and remote sensing technologies in a landscape approach to the study of Lyme disease transmission risk. In: Proceedings of GIS'93: Geographic Information Systems in Forestry, Environmental and Natural Resource Management. Vancouver, Canada, 1993.
31. Dister, S. W., Fish, D., Bros, S., Frank, D. H., and Wood, B. L. Landscape characterization of peridomestic risk for Lyme disease using satellite imagery. Am J Trop Med Hyg, 1997(57): 687-92.
32. Dister, S. W., Fish, D., Bros, S., Frank, D. H., and Wood, B. L. Landscape characterization of peridomestic risk for Lyme disease using satellite imagery. Am J Trop Med Hyg, 1997(57): 687-92.
33. Elliott, P., and Wartenberg, D. Spatial epidemiology: current approaches and future challenges. Environ Health Perspect, 2004(112): 998-1006.
34. Fang, L. Q., Cao, C. X., and Chen, G. S. Studies on the spatial distribution and environmental factors of highly pathogenic a vian influenza in China using geographic information system t echnology. Chin J Epidemiol, 2005(26): 839-842
35. Freier, J.W., Eastern equine encephalomyelitis. Lancet, 1993(342): 1281-3.
36. Geary, R. The continuity ratio and statistical mapping. Incor porated Stat., 1954(5): 115-145.
37. Getis, A., and Ord, J. K. local spatial statistics: an overvie w. In: Lonley, P., and Batty, M. Spatial Analysis: modeling i n a GIS environment. Geoinformation International, UK: Cambri dge, 1996: 261-277.
38. Getis, A., and Ord, K. the analysis of spatial association by use of distance statistics. Geogr. Anal., 1992(24): 189-206.
39. Griffith, D.A. Spatial regression analysis on the PC: spatial statistics using SAS. Washington, DC: Association of America n Geographers, 1993.
40. Guo, J. P., Xue, Y., Cao, C. X., Cao, W. C., Zhong, S. B., Cai, G. Y., Li, X.W., and Fang, L. Q. Study on the Highly Pathogenic A vian Influenza Epidemic Using Land Surface Temperature from M ODIS Data. In Proceedings of IEEE/IGARSS held at Soul, Korea,??25-29 July 2005, pp. 3599- 3602.
41. Guo, J.P., Xue, Y., Zhong, S.B., Cao, C. X., Cao, W. C., Li, X.W., and Fang, L. Q. Preliminary Study of Avian Influenza A Inf ection Using Remote Sensing and GIS Techniques. Lecture Notes in Computer Science(In press), 2006.
42. Guo, J.P.,. Xue, Y., Zhong, S. B., Cao, C. X., Cao, W. C., Li, X. W., and Fang, L.Q. Risk Factors Analysis of High Pathogenic A vian Influenza in Mainland China Using GIS and Remote Sensing. In Proceedings of IEEE/IGARSS to be held at Denver, Colorado,USA, 31 July-04 August 2006, (Accepted).
43. Haining, R. P., Wise, S. M., and Ma, J. Exploratory spatial data analysis in a geographic information system environment. The Statistician, 1998(47): 457-469.
44. Hassan, A. N., Beck, L. R., and Dister, S. Prediction of villages at risk for filariasis transmission in the Nile Delta using remote sensing and geographic information system technologies. J Egypt Soc Parasitol, 1998(28): 75-87.
45. Hassan, A.N., Dister, S., and Beck, L. Spatial analysis of lymphatic filariasis distribution in the Nile Delta in relation to some environmental variables using geographic information system technology. J Egypt Soc Parasitol, 1998(28): 119-31.
46. Holm, D. M. , and Maslia, M. L. Geographic information systems: A critical resource in exposure assessment. SUPERFUND XVI Conference and Exhibition, Washington, D. C., 1995.
47. Huq, A., and Colwell, R. R. Vibrios in the marine and estuarine environments. J Marine Biotechnology, 1995(3): 60-3.
48. Jacquez, G. M. Ak nearest neighbor test for space-time interaction. Statistics. in Medicine, 1996(15): 1935-1949.
49. Jacquez, G. M. GIS as an enabling technology. In: Gatrell, A.,and Loytonen, M. GIS and Health. Philadelphia: Taylor and Francis, Inc, 1998: 17-28.
50. King, M. D., Menzel, W. P., Kaufman, Y. J., Tanre, D., Gao, B.C.,Platnick, S., Ackerman, S. A., Remer, L. A., Pincus, R., and H ubanks, P.A. Cloud and Aerosol Properties, Precipitable Water,and Profiles of Temperature and Humidity from MODIS. IEEE Trans. Geosci. Remote Sensing, 2003: 442-458.
51. Kitron, U., and Kazmierczak, J. J. Spatial analysis of the distribution of Lyme disease in Wisconsin. Am J Epidemiol,1997(145): 558-66.
52. Kitron, U., Otieno, L. H., Hungerford, L. L., Odulaja, A., Brigham,W. U., and Okello, O.O. Spatial analysis of the distribution of tsetse flies in the Lambwe Valley, Kenya, using Landsat TM satellite imagery and GIS. Journal of Animal Ecology, 1996(65):371-80.53. Kleinschmidt, I., Bagayoko, M., Clarke, G. P. Y., and Craig, M.,and Le Sueur, D. A spatial statistical approach to malaria mapping. International Journal of Epidemiology, 2000(29): 355-361.
54. Knox, G. The detection of space-time interactions. Applied St atistics, 1964(13): 25-29.
55. Krige, Danie G. A statistical approach to some basic mine valuation problems on the Witwatersrand. J. of the Chem., Metal.and Mining Soc. of South Africa 1951(52): 119-139.
56. Kulldorff, M. and Nagarwalla, N. spatial disease clusters: de tection and inference. Statistics in Medicine, 1995(14): 799-810.
57. Kulldorff, M. Statistical methods for spatial epidemiology: Tests for randomness. In: Gatrell, T. and Loytonen, M. GIS and Health. Taylor and Francis, 1998.
58. Lawson, A. B. and Clark, A.B. space-time cluster modeling of s mall area data via MCMC methods. In: Proceedings of the biome trics Section of the ASA. American Statistical Association, W ashington, 1998: 1-9.
59. Lawson, A. B. On the analysis of mortality events around a pre specified fixed point. Journal of the Royal Statistical Socie ty, Series A, 1993(156): 363-377.
60. Lawson, A.B. Statistical Methods in Spatial Epidemiology. New York: Wiley, 2001.
61. Linthicum, K.J., Bailey, C. L., Davies, F. G., and Tucker, C. J. Detection of Rift Valley Fever viral activity in Kenya by satellite remote sensing imagery. Science, 1987(235): 1656-9.
62. Linthicum, K. J., Bailey, C. L., Tucker, C. J., Gordon, S. W., Logan, T. M., and Peters, C. J. Man-made ecological alterations of Senegal River basin on Rift Valley Fever transmission. Sistema Terra, 1994: 45-7.
63. Linthicum, K. J., Bailey, C. L., Tucker, C. J., Mitchell, K. D., Logan, T.M., and Davies, F. G. Applications of polar-orbiting, meteorological satellite data to detect flooding in Rift Valley Fever virus vector mosquito habitats in Kenya. Med Vet Entomol, 1990(4): 433-8.
64. Lobitz, B., Beck, L., Huq, A., Wood, B., Fuchs, G., and Faroque, A. S.G. Climate and infectious disease: Use of remote sensing for detection of Vibrio cholerae by indirect measurement. Proc National Academy Sciences, 2000(97): 1438-43.
65. Lumley, T. efficient execution of Stone's likelihood ratio te st for disease clustering. Computational Statistics and Data Analysis, 1995(20): 499-510.
66. Malone, J. B., Huh, O.K., Fehler, D. P., Wilson, P. A., Wilensky,??D. E., and Holmes, R. A. Temperature data from satellite images and the distribution of schistosomiasis in Egypt. Am J Trop Med Hyg, 1994(50): 714-22.
67. Mantel, N. the Detection of disease clustering and a generali zed regression approach. Cancer Research, 1967(27): 201-220.
68. Marbury, M. GIS: new tool or new toy? Health and Environment Digest, 1996(9): 88-89.
69. Moran, P. A. P. notes on continuous stochastic phenomena, Biome trika, 1950(37): 17-23.
70. NASA Goddard Distributed Active Archive Center, Greenbelt, Ma ryland; 1996. Archived data available at the following URL: h ttp://seawifs.gsfc. nasa. gov/cgibrs/level3. pl
71. NASA Jet Propulsion Laboratory, Physical Oceanography Distrib uted Active Archive Center. Pasadena, California; 1996. Archi ved data available at the following URL: http://podaac.jpl.na sa. gov
72. O'Hear, A. Karl Popper, Philosophy and Problems. Cambridge: C ambridge University Press, 1996.
73. Odland, J. Spatial autocorrelation. Scientific Geography seri es. 9. Newbury Park: Sage, 1988.
74. Openshaw, S., Charlton, M., Wymer, C., and Craft, A. A mark I geographical analysis machine for the automated analysis of point data sets, International Journal of Geographical Inform ation Systems, 1987(1): 335-358.
75. Pope, K.O., Rejmánková, E., Savage, H. M., Arredondo-Jimenez, J. I., Rodriguez, M. H., and Roberts, D. R. Remote sensing of tropical wetlands for malaria control in Chiapas, Mexico. Ecological Applications, 1993(4): 81-90.
76. Pope, K. O., Sheffner, E. J., Linthicum, K. J., Bailey, C. L., Logan, T. M., and Kasischke, E. S. Identification of central Kenyan Rift Valley Fever virus vector habitats with Landsat TM and evaluation of their flooding status with Airborne Imaging Radar. Remote Sensing Environment, 1992(40): 185-96.
77. Proceedings 1st International Symposium on Avian Influenza. 1 981. Certer Composition Corp., Richmond, VA.
78. Proceedings 2st International Symposium on Avian Influenza. 1 987. United States Animal Health Association, Athens, GA.
79. Proceedings 3st International Symposium on Avian Influenza. 1 992. United States Animal Health Association, Athens, GA.
80. Proceedings of the 5th International Symposium on Avian Influ enza. Athens, Georgia, USA. April 14-17, 2002.
81. Ranta, J., Pitkaniemi, J., Karvonen, M. Virtala, E. Rusanen, J. Colpaert, A. Naukkarinen, A. and Tuomilehto, J. Detection of overall space-time clustering in a non-uniformly distribut??ed population. Stat. Med. 1996(15): 2561-2572.
82. Rejmdnkovd, E., Roberts, D. R., Pawley, A., Manguin, S., and Polanco, J. Predictions of adult Anopheles albimanus densities in villages based on distance to remotely sensed larval habitats. Am J Trop Meg Hyg, 1995(53): 482-488.
83. Richards, T. B., Croner, C.M., Rushton, G., Brown, C. K., and F owler L. Geographic information systems and public health: ma pping the future. Public Health Rep 1999, 114 (4) : 359-73.
84. Roberts, D. R., Paris, J. F., Manguin, S., Harbach, R. E., Woodruff, R., and Rejmdnkovd, E. Predictions of malaria vector distribution in Belize based on multispectral satellite data. Am J Trop Med Hyg, 1996(54): 304-8.
85. Robinson, T. P., Rogers, D. J., and Williams, B. Mapping tsetse habitat suitability in the common fly belt of Southern Africa using multivariate analysis of climate and remotely sensed data. Med Vet Entomol, 1997(11): 235-45.
86. Rodriguez, A. D., Rodriguez, M. H., Herndndez, J. E., Dister, S. W., Beck, L. R., and Rejmdnkovd, E. Landscape surrounding human settlements and malaria mosquito abundance in southern Chiapas, Mexico. J Med Entomol, 1996(33): 39-48.
87. Rogers, D. J. Satellite imagery,tsetse and trypanosomiasis. Prev Vet Med, 1991(11): 201-20.
88. Rogers, D. J., and Randolph, S. E. Mortality rates and population density of tsetse flies correlated with satellite imagery. Nature, 1991(351): 739-41.
89. Rogers, D. J., and Williams, B. G. Monitoring trypanosomiasis in space and time. Parasitology, 1993(106 Suppl): 77-92.
90. Rushton, G. Improving the geographic basis of health surveill ance using GIS. In: Gatrell, A., and Loytonen, M. GIS and Hea lth. Philadelphia: Taylor and Francis, Inc, 1998: 63-80.
91. Rushton, G., Lolonis, P. Exploratory spatial analysis of birt h defect rates in an urban population. Stat Med, 1996(15): 71 7-726.
92. Salomonson, V., Guenther, B., and Masuoka, E. A summary of the status of the EOS Terra Mission Moderate Resolution Imaging Spectroradiometer (MODIS) and attendant data product develop ment after one year of on-orbit performance. In: proceedings of the International Geoscience and Remote Sensing Symposium/IGARSS' 2001, Sydney, Australia, 2001.
93. Schumm, S. A., and Lichty, R. W. Time, space, and causality in geomorphology. American Journal of Science, 1965(263): 110-119.
94. Stone, R. A. investigation of excess environmental risk around putative sources: statistical problems and a proposed test.??Statistics in Medicine, 1988(7): 649-660.
95. Stoney, W. E. The Pecora legacy-land observation satellites in the next century. In: Proceedings of the Pecora 13 Symposium. 1996 Aug 20-22; Sioux Falls, SD; Bethesda, MD: American Society for Photogrammetry and Remote Sensing; 1998: 260-74.
96. Talbot, T.O., Kulldorff, M., Forand, S. P., and Haley, V. B. evaluation of spatial filters to create smoothed maps of health data. Stat Med, 2000(19): 2399-408.
97. Teutsch, S.M., and Churchill, R. E. Principles and Practice of Public Health Surveillance. Oxford: Oxford University Press,1994.
98. Thompson, D. F., Malone, J. B., Harb, M., Faris, R., Huh, O.K., and Buck, A. A. Bancroftian filariasis distribution and diurnal temperature differences in the southern Nile Delta. Emerg Infect Dis, 1996(2): 234-5.
99. Thomson, M. C., Connor, S. J., Milligan, P. J. M., and Flasse, S. P.Mapping malaria risk in Africa: What can satellite data contribute? Parsitology Today, 1997(13): 313-8.
100. Thomson, M. C., Connor, S. J., Milligan, P. J. M., and Flasse, S. P.The ecology of malaria-as seen from Earth-observation satellites.Ann Trop Med Parasitol, 1996(90): 243-64.
101. Tobler, W. R. A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 1970(46): 234-240.
102. Tobler, W. R. Linear operators applied to areal data. In: Davis,J. C. and McCullagh, M.J. Display and analysis of spatial data. London: John Wiley, 1975: 14-38.
103. Torok, T. J., Kilgore, P. E., Clarke, M. J., Holman, R. C., Bresee, J.S., and Glass. Visualizing geographic and temporal trends in rotavirus activity in the United States, 1991 to 1996. Pediatric Infectious Disease Journal, 1997(16): 941-46.
104. Tracey, J. P., Woods, R., Roshier, D., West, P., and Saunders,G. R., the role of wild birds in the transmission of avian in fluenza for Australia: an ecological perspective. Emu, 2004(104): 109-124.
105. Turnbull, B. W., Iwano, E. J., Burnett, W. S., Howe, H. L., and Clark, L. C. Monitoring for clusters of disease: application to leukemia incidence in upstate New York. Am J Epidemiol, 1990(132): S136-143.
106. US Federal Geographic Data Committee. Homeland security and Geographic Information Systems - How GIS and mapping technology can save lives and protect property in post-September 11th America. Public Health GIS News and Information, 2003(52): 20-23.
107. Waller, L. A., and Turnbull, B. W., Clark, L. C., and Nasca, P.??Chronic disease surveillance and testing of clustering of dis ease and exposure: application to leukemia incidence and TCEcontaminated dumpsites in upstate New York. Environmetrics, 1995(3): 281-300.
108. Whittemore, A. S., Friend, N., Brown, B. W., and Holly, E. A. A test to detect clusters of disease. Biometrika, 1987(74): 631-635.
109．陈福勇．防控禽流感．北京：化学工业出版社，2004．
110．戴志澄，齐国明．中国病毒性肝炎：血清流行病学调查(上卷)．北京：科学技术文献出版社，1997．
111．范根成，张怀保，刘彦．禽流感的临床特征．中国家禽，2000(22)：42—43．
112．甘孟候．禽流感．北京：北京农业大学出版社，1995．
113．龚建华，林珲．虚拟地理环境—在线虚拟现实的地理学透视．北京：高等教育出版社，2001．
114．黄印尧．禽流感与人类健康．福建畜牧兽医，1999(21)：40．
115．王劲峰，韩卫国．空间分析研究进展．中国科学院地理科学与资源研究所，2004：
116．连志浩．流行病学．北京：人民卫生出版社，1997．
117．廖党金，宋斌．禽流感的回顾与流行病学．中国兽医寄生虫病，2004(12)：43-47．
118．罗开健，廖明，任涛．鸭鹅流感．养禽与禽病防治，2001(11)：6-9．
119．农业部．《禽流感等重大动物疫病防控行动工作方案》，2006．
120．邬伦，刘瑜．地理信息系统—原理、方法和应用．北京：科学出版社，2001．
121．许锐恒、余德文、邓卓晖．二十一世纪新发现传染病—非典型肺炎．广东医学，2003(24)：24-26．
122. Avian Influenza Infection in Humans, http://www.cdc.gov/flu/avian/gen-info/avian-flu-humans.htm.
123. ESRI, 2002, Environmental Research Systems Institute Inc., http://www.esri.com/.
124. http://free.vgt.vito.be/.
125. http://geo.arc.nasa.gov/sge/health/chaart.html.
126. http://www.cdc.gov/flu/avian/gen-info/avian-flu-humans.htm.
127. http://www.cnm21.com/xinwen2/040924_004.htm.
128. http://www.itl.nist.gov/div898/handbook/eda/.
129. http://www.nlm.nih.gov/mesh.
130. http://www.spatialecology.com/htools/tooldesc.php.
131．公共卫生领域空间信息技术应用研究中心网站，http://www.irsa.ac.cn/health/．
132．世界卫生组织网站，http://www.who.int/.
2. Alt, K.W., and Vach, W. The reconstruction of 'genetic kinshi p' in prehistoric burial complexes - problems and. statistics. In: Bock, H.H., and Ihm, P. Classification, data analysis, a nd knowledge organization. Berlin: Springer Verlag, 1990.
3. Anselin, L. GIS Research infrastructure for spatial analysis of real estate markets. Journal of housing Research, 1998(9):113-33.
4. Anselin, L. Local indicators of spatial association. Geogr. A nal., 1995(27): 93-115.
5. Anselin, L. Spatial Economics: Methods and Models. Dordrecht, The Metherlands: Kluwer Academic Publishers, 1988: 284.
6. Bailey, T.C. Spatial statistical methods in health. Cad Saude Publ, 2001(17): 1083-1098.
7. Beck, L. R., Lobitz, B. M., and Wood, B. L. Remote Sensing and H uman Health: new sensors and new opportunities. Emerg infect dis, 2000(6): 217-226.
8. Beck, L.R., Rodriguez, M. H., Dister, S. W., Rodriguez, A. D., Washino, R. K., and Roberts, D.R. Assessment of a remote sensing based model for predicting malaria transmission risk in villages of Chiapas, Mexico. Am J Trop Med Hyg, 1997(56): 99-106.
9. Besag, J. and Newell, J. The detection of clusters in rare di seases, Journal of the Royal Statistical. Society, Series A 1991(154): 143-155.
10. Besag, J. Nearest-neighbor systems and the auto-logistic mode 1 for binary data. Journal of the Royal Statistics Society, S eries B, 1972(36): 75-83.
11. Besag, J. Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistics Society, Series B, 1974(36): 192-236.
12. Bithell, J.F. The choice of test for detecting raised disease risk near a point source. Stat Med, 1995(14): 2309-2322.
13. Boulos, M. N. K. Towards evidence-based, GIS-driven national sp atial health information infrastructure and surveillance services in the United Kingdom. International Journal of Health Geographics, 2004(3): Available online at: http://www. ij-healt hgeographics. com/content/3/1/1.
14. Carrat, F., and Valleron, A. J. Epidemiologic mapping using th??e "kriging" method: application to an influenza-like illness epidemic in France. American Journal of Epidemiology, 1992(1 35): 1293-1300.
15. Center for Space Research. University of Texas, Austin; 1996.Archived data available at the following URL: http://www.csr.utexas. edu.
16. Chen, H., Smith, G. J., Zhang, S. Y., Qin, K., Wang, J., and Li,K. S. Avian flu: H5N1 virus outbreak in migratory waterfowl.Nature. 2005(436): 191-2.
17. Clarke, K. C., Osleeb, J. R., Sherry, J. M., Meert, J. P., and Larsson, R. W. The use of remote sensing and geographic information systems in UNICEF's dracunculiasis (Guinea worm) eradication effort. Prev Vet Med, 1990(11): 229-35.
18. Cliff, A. D., and Ord, J. K. Spatial processes: models and appl ications. London: Pion, 1981.
19. Committee on Earth Observation Satellites. Coordination for the Next Decade (1995 CEOS Yearbook). UK: European Space Agency. Smith System Engineering Ltd, 1995:
20. Crist, E. P., and Cicone, R. C. A physically based transformation of Thematic Mapper data - The TM Tasseled Cap. In: IEEE Trans Geosciences and Remote Sensing. 1984(22): 256-63.
21. Croner, C.M., and De Cola, L. visualization of Disease survei llance data with geostatistics. Presented at UNECE (United Na tions Economic Commission for Europe) work session on methodo logyical issues involving integration of statistics and geogr aphy. 2001: available at: http://www.unece.org/stats/document s/2001/09/gis/25.e. pdf.
22. Cross, E. R., Newcomb, W. W., and Tucker, C. J. Use of weather data and remote sensing to predict the geographic and seasonal distribution of Phlebotomus paptasi in Southwest Asia. Am J Trop Med Hyg, 1996(54): 530-6.
23. Curran, P. J., Atkinson, P. M., and Foody, G. M., Linking remote sensing, land cover and disease. Adv Parasitol, 2000(47): 37-80.
24. Cuzick, J., and Edwards, R. Spatial clustering for inhomogene ous populations. J. Roy. Statist. Soc. B, 1990(52): 73-104.
25. Daley, D. J., and Gani, J. Epidemic Modelling. UK: Cambridge U niv. Press, 1999.
26. Diggle, P., Chetwynd, A., Haggkvist, R. and Morris, S. Second-order analysis of space-time clustering. Statistical Methods in Medical Research, 1995(4): 124-136.
27. Diggle, P.J. Point process modelling in environmental epidemi ology. In: Barnett, V. and Turkman, K. F. Statistics for the E nvironment. Chichester: John Wiley, 1993.
28. Diggle, P.J., and Chetwynd, A. G. Second-order analysis of spa??tial clustering for inhomogeneous populations. Biometrics, 19 91(47): 1155-1163.
29. Dister, S. W., Beck, L. R., Wood, B.L., Falco, R., and Fish, D. The use of GIS and remote sensing technologies in a landscape approach to the study of Lyme disease transmission risk. In: Proceedings of GIS'93: Geographic Information Systems in Forestry, Environmental and Natural Resource Management. Vancouver, Canada, 1993.
30. Dister, S.W., Beck, L. R., Wood, B. L., Falco, R., and Fish, D. The use of GIS and remote sensing technologies in a landscape approach to the study of Lyme disease transmission risk. In: Proceedings of GIS'93: Geographic Information Systems in Forestry, Environmental and Natural Resource Management. Vancouver, Canada, 1993.
31. Dister, S. W., Fish, D., Bros, S., Frank, D. H., and Wood, B. L. Landscape characterization of peridomestic risk for Lyme disease using satellite imagery. Am J Trop Med Hyg, 1997(57): 687-92.
32. Dister, S. W., Fish, D., Bros, S., Frank, D. H., and Wood, B. L. Landscape characterization of peridomestic risk for Lyme disease using satellite imagery. Am J Trop Med Hyg, 1997(57): 687-92.
33. Elliott, P., and Wartenberg, D. Spatial epidemiology: current approaches and future challenges. Environ Health Perspect, 2004(112): 998-1006.
34. Fang, L. Q., Cao, C. X., and Chen, G. S. Studies on the spatial distribution and environmental factors of highly pathogenic a vian influenza in China using geographic information system t echnology. Chin J Epidemiol, 2005(26): 839-842
35. Freier, J.W., Eastern equine encephalomyelitis. Lancet, 1993(342): 1281-3.
36. Geary, R. The continuity ratio and statistical mapping. Incor porated Stat., 1954(5): 115-145.
37. Getis, A., and Ord, J. K. local spatial statistics: an overvie w. In: Lonley, P., and Batty, M. Spatial Analysis: modeling i n a GIS environment. Geoinformation International, UK: Cambri dge, 1996: 261-277.
38. Getis, A., and Ord, K. the analysis of spatial association by use of distance statistics. Geogr. Anal., 1992(24): 189-206.
39. Griffith, D.A. Spatial regression analysis on the PC: spatial statistics using SAS. Washington, DC: Association of America n Geographers, 1993.
40. Guo, J. P., Xue, Y., Cao, C. X., Cao, W. C., Zhong, S. B., Cai, G. Y., Li, X.W., and Fang, L. Q. Study on the Highly Pathogenic A vian Influenza Epidemic Using Land Surface Temperature from M ODIS Data. In Proceedings of IEEE/IGARSS held at Soul, Korea,??25-29 July 2005, pp. 3599- 3602.
41. Guo, J.P., Xue, Y., Zhong, S.B., Cao, C. X., Cao, W. C., Li, X.W., and Fang, L. Q. Preliminary Study of Avian Influenza A Inf ection Using Remote Sensing and GIS Techniques. Lecture Notes in Computer Science(In press), 2006.
42. Guo, J.P.,. Xue, Y., Zhong, S. B., Cao, C. X., Cao, W. C., Li, X. W., and Fang, L.Q. Risk Factors Analysis of High Pathogenic A vian Influenza in Mainland China Using GIS and Remote Sensing. In Proceedings of IEEE/IGARSS to be held at Denver, Colorado,USA, 31 July-04 August 2006, (Accepted).
43. Haining, R. P., Wise, S. M., and Ma, J. Exploratory spatial data analysis in a geographic information system environment. The Statistician, 1998(47): 457-469.
44. Hassan, A. N., Beck, L. R., and Dister, S. Prediction of villages at risk for filariasis transmission in the Nile Delta using remote sensing and geographic information system technologies. J Egypt Soc Parasitol, 1998(28): 75-87.
45. Hassan, A.N., Dister, S., and Beck, L. Spatial analysis of lymphatic filariasis distribution in the Nile Delta in relation to some environmental variables using geographic information system technology. J Egypt Soc Parasitol, 1998(28): 119-31.
46. Holm, D. M. , and Maslia, M. L. Geographic information systems: A critical resource in exposure assessment. SUPERFUND XVI Conference and Exhibition, Washington, D. C., 1995.
47. Huq, A., and Colwell, R. R. Vibrios in the marine and estuarine environments. J Marine Biotechnology, 1995(3): 60-3.
48. Jacquez, G. M. Ak nearest neighbor test for space-time interaction. Statistics. in Medicine, 1996(15): 1935-1949.
49. Jacquez, G. M. GIS as an enabling technology. In: Gatrell, A.,and Loytonen, M. GIS and Health. Philadelphia: Taylor and Francis, Inc, 1998: 17-28.
50. King, M. D., Menzel, W. P., Kaufman, Y. J., Tanre, D., Gao, B.C.,Platnick, S., Ackerman, S. A., Remer, L. A., Pincus, R., and H ubanks, P.A. Cloud and Aerosol Properties, Precipitable Water,and Profiles of Temperature and Humidity from MODIS. IEEE Trans. Geosci. Remote Sensing, 2003: 442-458.
51. Kitron, U., and Kazmierczak, J. J. Spatial analysis of the distribution of Lyme disease in Wisconsin. Am J Epidemiol,1997(145): 558-66.
52. Kitron, U., Otieno, L. H., Hungerford, L. L., Odulaja, A., Brigham,W. U., and Okello, O.O. Spatial analysis of the distribution of tsetse flies in the Lambwe Valley, Kenya, using Landsat TM satellite imagery and GIS. Journal of Animal Ecology, 1996(65):371-80.53. Kleinschmidt, I., Bagayoko, M., Clarke, G. P. Y., and Craig, M.,and Le Sueur, D. A spatial statistical approach to malaria mapping. International Journal of Epidemiology, 2000(29): 355-361.
54. Knox, G. The detection of space-time interactions. Applied St atistics, 1964(13): 25-29.
55. Krige, Danie G. A statistical approach to some basic mine valuation problems on the Witwatersrand. J. of the Chem., Metal.and Mining Soc. of South Africa 1951(52): 119-139.
56. Kulldorff, M. and Nagarwalla, N. spatial disease clusters: de tection and inference. Statistics in Medicine, 1995(14): 799-810.
57. Kulldorff, M. Statistical methods for spatial epidemiology: Tests for randomness. In: Gatrell, T. and Loytonen, M. GIS and Health. Taylor and Francis, 1998.
58. Lawson, A. B. and Clark, A.B. space-time cluster modeling of s mall area data via MCMC methods. In: Proceedings of the biome trics Section of the ASA. American Statistical Association, W ashington, 1998: 1-9.
59. Lawson, A. B. On the analysis of mortality events around a pre specified fixed point. Journal of the Royal Statistical Socie ty, Series A, 1993(156): 363-377.
60. Lawson, A.B. Statistical Methods in Spatial Epidemiology. New York: Wiley, 2001.
61. Linthicum, K.J., Bailey, C. L., Davies, F. G., and Tucker, C. J. Detection of Rift Valley Fever viral activity in Kenya by satellite remote sensing imagery. Science, 1987(235): 1656-9.
62. Linthicum, K. J., Bailey, C. L., Tucker, C. J., Gordon, S. W., Logan, T. M., and Peters, C. J. Man-made ecological alterations of Senegal River basin on Rift Valley Fever transmission. Sistema Terra, 1994: 45-7.
63. Linthicum, K. J., Bailey, C. L., Tucker, C. J., Mitchell, K. D., Logan, T.M., and Davies, F. G. Applications of polar-orbiting, meteorological satellite data to detect flooding in Rift Valley Fever virus vector mosquito habitats in Kenya. Med Vet Entomol, 1990(4): 433-8.
64. Lobitz, B., Beck, L., Huq, A., Wood, B., Fuchs, G., and Faroque, A. S.G. Climate and infectious disease: Use of remote sensing for detection of Vibrio cholerae by indirect measurement. Proc National Academy Sciences, 2000(97): 1438-43.
65. Lumley, T. efficient execution of Stone's likelihood ratio te st for disease clustering. Computational Statistics and Data Analysis, 1995(20): 499-510.
66. Malone, J. B., Huh, O.K., Fehler, D. P., Wilson, P. A., Wilensky,??D. E., and Holmes, R. A. Temperature data from satellite images and the distribution of schistosomiasis in Egypt. Am J Trop Med Hyg, 1994(50): 714-22.
67. Mantel, N. the Detection of disease clustering and a generali zed regression approach. Cancer Research, 1967(27): 201-220.
68. Marbury, M. GIS: new tool or new toy? Health and Environment Digest, 1996(9): 88-89.
69. Moran, P. A. P. notes on continuous stochastic phenomena, Biome trika, 1950(37): 17-23.
70. NASA Goddard Distributed Active Archive Center, Greenbelt, Ma ryland; 1996. Archived data available at the following URL: h ttp://seawifs.gsfc. nasa. gov/cgibrs/level3. pl
71. NASA Jet Propulsion Laboratory, Physical Oceanography Distrib uted Active Archive Center. Pasadena, California; 1996. Archi ved data available at the following URL: http://podaac.jpl.na sa. gov
72. O'Hear, A. Karl Popper, Philosophy and Problems. Cambridge: C ambridge University Press, 1996.
73. Odland, J. Spatial autocorrelation. Scientific Geography seri es. 9. Newbury Park: Sage, 1988.
74. Openshaw, S., Charlton, M., Wymer, C., and Craft, A. A mark I geographical analysis machine for the automated analysis of point data sets, International Journal of Geographical Inform ation Systems, 1987(1): 335-358.
75. Pope, K.O., Rejmánková, E., Savage, H. M., Arredondo-Jimenez, J. I., Rodriguez, M. H., and Roberts, D. R. Remote sensing of tropical wetlands for malaria control in Chiapas, Mexico. Ecological Applications, 1993(4): 81-90.
76. Pope, K. O., Sheffner, E. J., Linthicum, K. J., Bailey, C. L., Logan, T. M., and Kasischke, E. S. Identification of central Kenyan Rift Valley Fever virus vector habitats with Landsat TM and evaluation of their flooding status with Airborne Imaging Radar. Remote Sensing Environment, 1992(40): 185-96.
77. Proceedings 1st International Symposium on Avian Influenza. 1 981. Certer Composition Corp., Richmond, VA.
78. Proceedings 2st International Symposium on Avian Influenza. 1 987. United States Animal Health Association, Athens, GA.
79. Proceedings 3st International Symposium on Avian Influenza. 1 992. United States Animal Health Association, Athens, GA.
80. Proceedings of the 5th International Symposium on Avian Influ enza. Athens, Georgia, USA. April 14-17, 2002.
81. Ranta, J., Pitkaniemi, J., Karvonen, M. Virtala, E. Rusanen, J. Colpaert, A. Naukkarinen, A. and Tuomilehto, J. Detection of overall space-time clustering in a non-uniformly distribut??ed population. Stat. Med. 1996(15): 2561-2572.
82. Rejmdnkovd, E., Roberts, D. R., Pawley, A., Manguin, S., and Polanco, J. Predictions of adult Anopheles albimanus densities in villages based on distance to remotely sensed larval habitats. Am J Trop Meg Hyg, 1995(53): 482-488.
83. Richards, T. B., Croner, C.M., Rushton, G., Brown, C. K., and F owler L. Geographic information systems and public health: ma pping the future. Public Health Rep 1999, 114 (4) : 359-73.
84. Roberts, D. R., Paris, J. F., Manguin, S., Harbach, R. E., Woodruff, R., and Rejmdnkovd, E. Predictions of malaria vector distribution in Belize based on multispectral satellite data. Am J Trop Med Hyg, 1996(54): 304-8.
85. Robinson, T. P., Rogers, D. J., and Williams, B. Mapping tsetse habitat suitability in the common fly belt of Southern Africa using multivariate analysis of climate and remotely sensed data. Med Vet Entomol, 1997(11): 235-45.
86. Rodriguez, A. D., Rodriguez, M. H., Herndndez, J. E., Dister, S. W., Beck, L. R., and Rejmdnkovd, E. Landscape surrounding human settlements and malaria mosquito abundance in southern Chiapas, Mexico. J Med Entomol, 1996(33): 39-48.
87. Rogers, D. J. Satellite imagery,tsetse and trypanosomiasis. Prev Vet Med, 1991(11): 201-20.
88. Rogers, D. J., and Randolph, S. E. Mortality rates and population density of tsetse flies correlated with satellite imagery. Nature, 1991(351): 739-41.
89. Rogers, D. J., and Williams, B. G. Monitoring trypanosomiasis in space and time. Parasitology, 1993(106 Suppl): 77-92.
90. Rushton, G. Improving the geographic basis of health surveill ance using GIS. In: Gatrell, A., and Loytonen, M. GIS and Hea lth. Philadelphia: Taylor and Francis, Inc, 1998: 63-80.
91. Rushton, G., Lolonis, P. Exploratory spatial analysis of birt h defect rates in an urban population. Stat Med, 1996(15): 71 7-726.
92. Salomonson, V., Guenther, B., and Masuoka, E. A summary of the status of the EOS Terra Mission Moderate Resolution Imaging Spectroradiometer (MODIS) and attendant data product develop ment after one year of on-orbit performance. In: proceedings of the International Geoscience and Remote Sensing Symposium/IGARSS' 2001, Sydney, Australia, 2001.
93. Schumm, S. A., and Lichty, R. W. Time, space, and causality in geomorphology. American Journal of Science, 1965(263): 110-119.
94. Stone, R. A. investigation of excess environmental risk around putative sources: statistical problems and a proposed test.??Statistics in Medicine, 1988(7): 649-660.
95. Stoney, W. E. The Pecora legacy-land observation satellites in the next century. In: Proceedings of the Pecora 13 Symposium. 1996 Aug 20-22; Sioux Falls, SD; Bethesda, MD: American Society for Photogrammetry and Remote Sensing; 1998: 260-74.
96. Talbot, T.O., Kulldorff, M., Forand, S. P., and Haley, V. B. evaluation of spatial filters to create smoothed maps of health data. Stat Med, 2000(19): 2399-408.
97. Teutsch, S.M., and Churchill, R. E. Principles and Practice of Public Health Surveillance. Oxford: Oxford University Press,1994.
98. Thompson, D. F., Malone, J. B., Harb, M., Faris, R., Huh, O.K., and Buck, A. A. Bancroftian filariasis distribution and diurnal temperature differences in the southern Nile Delta. Emerg Infect Dis, 1996(2): 234-5.
99. Thomson, M. C., Connor, S. J., Milligan, P. J. M., and Flasse, S. P.Mapping malaria risk in Africa: What can satellite data contribute? Parsitology Today, 1997(13): 313-8.
100. Thomson, M. C., Connor, S. J., Milligan, P. J. M., and Flasse, S. P.The ecology of malaria-as seen from Earth-observation satellites.Ann Trop Med Parasitol, 1996(90): 243-64.
101. Tobler, W. R. A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 1970(46): 234-240.
102. Tobler, W. R. Linear operators applied to areal data. In: Davis,J. C. and McCullagh, M.J. Display and analysis of spatial data. London: John Wiley, 1975: 14-38.
103. Torok, T. J., Kilgore, P. E., Clarke, M. J., Holman, R. C., Bresee, J.S., and Glass. Visualizing geographic and temporal trends in rotavirus activity in the United States, 1991 to 1996. Pediatric Infectious Disease Journal, 1997(16): 941-46.
104. Tracey, J. P., Woods, R., Roshier, D., West, P., and Saunders,G. R., the role of wild birds in the transmission of avian in fluenza for Australia: an ecological perspective. Emu, 2004(104): 109-124.
105. Turnbull, B. W., Iwano, E. J., Burnett, W. S., Howe, H. L., and Clark, L. C. Monitoring for clusters of disease: application to leukemia incidence in upstate New York. Am J Epidemiol, 1990(132): S136-143.
106. US Federal Geographic Data Committee. Homeland security and Geographic Information Systems - How GIS and mapping technology can save lives and protect property in post-September 11th America. Public Health GIS News and Information, 2003(52): 20-23.
107. Waller, L. A., and Turnbull, B. W., Clark, L. C., and Nasca, P.??Chronic disease surveillance and testing of clustering of dis ease and exposure: application to leukemia incidence and TCEcontaminated dumpsites in upstate New York. Environmetrics, 1995(3): 281-300.
108. Whittemore, A. S., Friend, N., Brown, B. W., and Holly, E. A. A test to detect clusters of disease. Biometrika, 1987(74): 631-635.
109．陈福勇．防控禽流感．北京：化学工业出版社，2004．
110．戴志澄，齐国明．中国病毒性肝炎：血清流行病学调查(上卷)．北京：科学技术文献出版社，1997．
111．范根成，张怀保，刘彦．禽流感的临床特征．中国家禽，2000(22)：42—43．
112．甘孟候．禽流感．北京：北京农业大学出版社，1995．
113．龚建华，林珲．虚拟地理环境—在线虚拟现实的地理学透视．北京：高等教育出版社，2001．
114．黄印尧．禽流感与人类健康．福建畜牧兽医，1999(21)：40．
115．王劲峰，韩卫国．空间分析研究进展．中国科学院地理科学与资源研究所，2004：
116．连志浩．流行病学．北京：人民卫生出版社，1997．
117．廖党金，宋斌．禽流感的回顾与流行病学．中国兽医寄生虫病，2004(12)：43-47．
118．罗开健，廖明，任涛．鸭鹅流感．养禽与禽病防治，2001(11)：6-9．
119．农业部．《禽流感等重大动物疫病防控行动工作方案》，2006．
120．邬伦，刘瑜．地理信息系统—原理、方法和应用．北京：科学出版社，2001．
121．许锐恒、余德文、邓卓晖．二十一世纪新发现传染病—非典型肺炎．广东医学，2003(24)：24-26．
122. Avian Influenza Infection in Humans, http://www.cdc.gov/flu/avian/gen-info/avian-flu-humans.htm.
123. ESRI, 2002, Environmental Research Systems Institute Inc., http://www.esri.com/.
124. http://free.vgt.vito.be/.
125. http://geo.arc.nasa.gov/sge/health/chaart.html.
126. http://www.cdc.gov/flu/avian/gen-info/avian-flu-humans.htm.
127. http://www.cnm21.com/xinwen2/040924_004.htm.
128. http://www.itl.nist.gov/div898/handbook/eda/.
129. http://www.nlm.nih.gov/mesh.
130. http://www.spatialecology.com/htools/tooldesc.php.
131．公共卫生领域空间信息技术应用研究中心网站，http://www.irsa.ac.cn/health/．
132．世界卫生组织网站，http://www.who.int/.