GIS-based spatial, temporal, and space-time analysis of haemorrhagic fever with renal syndrome

Epidemiology and Infection

Volumn 137, Issue 12, 2009, Pages 1766-1775

  Wu, W ^a   Guo, J Q ^b   Yin, Z H ^a   Wang, P ^c   Zhou, B S ^a  

^a CHINA MEDICAL UNIVERSITY   (China)

^b Liaoning Provincial Center for Disease Control and Prevention   (China)

^c Shenyang Center for Disease Control and Prevention   (China)

Author keywords

Cluster analysis; Geographical information system; HFRS

Indexed keywords

ARTICLE; CHINA; CLUSTER ANALYSIS; CRUSH SYNDROME; ENDEMIC DISEASE; GEOGRAPHIC DISTRIBUTION; GEOGRAPHIC INFORMATION SYSTEM; HANTAVIRUS; HUMAN; INCIDENCE; INFECTION RISK; VIRUS HEMORRHAGIC FEVER; TIME;

CHINA; CLUSTER ANALYSIS; GEOGRAPHIC INFORMATION SYSTEMS; HEMORRHAGIC FEVER WITH RENAL SYNDROME; HUMANS; TIME FACTORS;

EID: 70449701823     PISSN: 09502688     EISSN: 14694409     Source Type: Journal    
DOI: 10.1017/S0950268809002659     Document Type: Article

References (29)

1. Luo CW, Chen HX. Study on the factors influenced epidemic of hemorrhagic fever with renal syndrome. Chinese Journal of Vector Biology and Control 2003; 14: 451-454.
2. Nkhoma ET, et al.. Detecting spatiotemporal clusters of accidental poisoning mortality among Texas counties, U.S., 1980-2001. International Journal of Health Geographics 2004; 3: 25-37.
3. Zeman P. Objective assessment of risk maps of tickborne encephalitis and Lyme borreliosis based on spatial patterns of located cases. International Journal of Epidemiology 1997; 26: 1121-1129.
4. Frank C, et al.. Mapping Lyme disease incidence for diagnostic and preventive decisions, Maryland. Emerging Infectious Diseases 2002; 8: 427-429.
5. Wu J, et al.. Exploratory spatial data analysis for the identification of risk factors to birth defects. BMC Public Health 2004; 4: 23-32.
6. Odoi A, et al.. Investigation of clusters of giardiasis using GIS and a spatial scan statistic. International Journal of Health Geographics 2004; 3: 11-21.
7. Anselin L, Lozano N, Koschinsky J. Rate transformations and smoothing, 2006 (http://geodacenter.asu.edu/ pdf/smoothing-06.pdf). Accessed 5 March 2009.
8. Rey SJ, Janikas MV. STARS: Space-time analysis of regional systems. Geographical Analysis 2006; 38: 67-86.
9. Cousens S, et al.. Geographical distribution of variant Creutzfeldt-Jakob disease in Great Britain, 1994-2000. Lancet 2001; 357: 1002-1007.
10. Chaput EK, Meek JI, Heimer R. Spatial analysis of human granulocytic ehrlichiosis near Lyme, Connecticut. Emerging Infectious Diseases 2002; 8: 943-948.
11. Sauders BD, et al.. Molecular subtyping to detect human listeriosis clusters. Emerging Infectious Diseases 2003, 9: 672-680.
12. Gosselin PL, et al.. The integrated system for public health monitoring of West Nile virus (ISPHM-WNV): a real-time GIS for surveillance and decision-making. International Journal of Health Geographics 2005; 4: 21-32.
13. Fang L, et al.. Spatial analysis of hemorrhagic fever with renal syndrome in China. BMC Infectious Diseases 2006; 6: 77-86.
14. Kulldorff M, et al.. Breast cancer in northeastern United States : geographical analysis. American Journal of Epidemiology 1997; 146: 161-170.
15. Zhan FB, Lin H. Geographic patterns of cancer mortality clusters in Texas, 1990 to 1997. Texas Medicine 2003; 99: 58-64.
16. Thomas AJ, Carlin BP. Late detection of breast and colorectal cancer in Minnesota counties : an application of spatial smoothing and clustering. Statistics in Medicine 2003; 22: 113-127.
17. Fukuda Y, et al.. Variations in societal characteristics of spatial disease clusters : examples of colon, lung and breast cancer in Japan. International Journal of Health Geographics 2005; 4: 16-28.
18. Kuehl KS, Loffredo CA. A cluster of hypoplastic left heart malformation in Baltimore, Maryland. Pediatric Cardiology 2006; 27: 25-31.
19. Donnan PT, et al.. Multiple sclerosis in Tayside, Scotland: detection of clusters using a spatial scan statistic. Multiple Sclerosis 2005; 11: 403-408.
20. Ala A, et al.. Increased prevalence of primary biliary cirrhosis near superfund toxic waste sites. Hepatology 2006; 43: 525-531.
21. Ward MP. Blowfly strike in sheep flocks as an example of the use of a time-space scan statistic to control confounding. Preventive Veterinary Medicine 2001; 49: 61-69.
22. Falconi F, Ochs H, Deplazes P. Serological crosssectional survey of psoroptic sheep scab in Switzerland. Veterinary Parasitology 2002; 109: 119-127.
23. Sheridan HA, et al.. A temporal-spatial analysis of bovine spongiform encephalopathy in Irish cattle herds, from 1996 to 2000. Canadian Journal of Veterinary Research 2005; 69: 19-25.
24. Guerin MT, et al.. A temporal study of Salmonella serovars in animals in Alberta between 1990 and 2001. Canadian Journal of Veterinary Research 2005; 69: 88-89.
25. Kulldorff M. SaTScanTM user guide for version 7.0, 2006 (http://www.satscan.org/). Accessed 8 November 2008.
26. Anselin L. Exploring spatial data with GeoDaTM: a workbook, 2005. (http://www.csiss.org). Accessed 6 November 2008.
27. Zhang K, et al.. Landscape ecological studies on autumnal Chinese cabbage fields in Shenyang suburb. I. Features of autumna1 Chinese cabbage field patches. Journal of Applied Ecology 2002; 13: 1615-1618.
28. Bi P, et al.. Seasonal rainfall variability, the incidence of hemorrhagic fever with renal syndrome, and prediction of the disease in low-lying areas of China. American Journal of Epidemiology 1998; 148: 276-281.
29. Bi P, et al.. Climatic, reservoir and occupational variables and the transmission of hemorrhagic fever with renal syndrome in China. International Journal of Epidemiology 2002; 31: 189-193.