Volume 81, Issue 3
  • ISSN: 0002-9637
  • E-ISSN: 1476-1645


The spatio-temporal distribution pattern of malaria in Yunnan Province, China was studied using a geographic information system technique. Both descriptive and temporal scan statistics revealed seasonal fluctuation in malaria incidences in Yunnan Province with only one peak during 1995–2000, and two apparent peaks from 2001 to 2005. Spatial autocorrelation analysis indicated that malaria incidence was not randomly distributed in the province. Further analysis using spatial scan statistics discovered that the high risk areas were mainly clustered at the bordering areas with Myanmar and Laos, and in Yuanjiang River Basin. There were obvious associations between and malaria incidences and climatic factors with a clear 1-month lagged effect, especially in cluster areas. All these could provide information on where and when malaria prevention and control measures would be applied. These findings imply that countermeasures should target high risk areas at suitable times, when climatic factors facilitate the transmission of malaria.


Article metrics loading...

The graphs shown below represent data from March 2017
Loading full text...

Full text loading...



  1. Ministry of Health, 2005. Malaria Surveillance Project in China.
  2. Hui FM, 2007. A spatio-temporal analysis on the patterns of malaria epidemic in Yunnan Province using GIS [dissertation]. Nanjing: Nanjing University, 29–30.
  3. Li ZH, 2006. Analysis of main factors associated with the prevalence of malaria in Yunnan Province at present. China Trop Med 6 : 1383–1384.
    [Google Scholar]
  4. Elliott P, Wartenberg D, 2004. Spatial epidemiology: current approaches and future challenges. Environ Health Perspect 112 : 998–1006.
    [Google Scholar]
  5. Ostfeld RS, Glass GE, Keesing F, 2005. Spatial epidemiology: an emerging (or re-emerging) discipline. Trends Ecol Evol 20 : 328–335.
    [Google Scholar]
  6. Ernst KC, Adoka SO, Kowuor DO, Wilson ML, John CC, 2006. Malaria hotspot areas in a highland Kenya site are consistent in epidemic and non-epidemic years and are associated with ecological factors. Malar J 5 : 78.
    [Google Scholar]
  7. Zhou GF, Sirichaisinthop J, Sattabongkot J, Jones J, Bjornstad ON, Yan GY, Cui LW, 2005. Spatio-temporal distribution of Plasmodium falciparum and P. vivax malaria in Thailand. Am J Trop Med Hyg 72 : 256–262.
    [Google Scholar]
  8. Kleinschmidt I, Sharp BL, Clarke GPY, Curtis B, Fraser C, 2001. Use of generalized linear mixed models in the spatial analysis of small-area malaria incidence rates in KwaZulu Natal, South Africa. Am J Epidemiol 153 : 1213–1221.
    [Google Scholar]
  9. Kleinschmidt I, Bagayoko M, Clarke GP, Craig M, Le Sueur D, 2000. A spatial statistical approach to malaria mapping. Int J Epidemiol 29 : 355–361.
    [Google Scholar]
  10. Zhou GF, Minakawa N, Githeko A, Yan GY, 2004. Spatial distribution patterns of malaria vectors and sample size determination in spatially heterogeneous environments: a case study in the west Kenyan highland. J Med Entomol 41 : 1001–1009.
    [Google Scholar]
  11. Zhou GF, Munga S, Minakawa N, Githeko A, Yan GY, 2007. Spatial relationship between adult malaria vector abundance and environmental factors in Western Kenya Highlands. Am J Trop Med Hyg 77 : 29–35.
    [Google Scholar]
  12. Poncon N, Tran A, Toty C, Luty AJF, Fontenille D, 2008. A quantitative risk assessment approach for mosquito-borne diseases: malaria re-emergence in southern France. Malar J 7 : 147.
    [Google Scholar]
  13. Introductions of Yunnan Province, 2008. Available at: http://www.yn.gov.cn/yunnan,china/74590868828323840/index.html. Accessed August 1, 2008.
  14. WHO, 1998. WHO Expert Committee on Malaria: Twentieth Report. (WHO Technical Report Series No.892), 18–24.
  15. Chinese Natural Resources Database, 2008. Available at: http://www.naturalresources.csdb.cn/index.asp. Accessed August 1, 2008.
  16. China Meteorological Data Sharing Service System, 2008. Available at: http://cdc.cma.gov.cn. Accessed August 1, 2008.
  17. Kulldorff M, 2006. SaTScan User Guide for vesion 7.0. Available at: http://www.satscan.org/. Accessed August 1, 2008.
  18. Kulldorff M, Heffernan R, Hartman J, Assun ão R, Mostashari F, 2005. A space-time permutation scan statistic for the early detection of disease outbreaks. PLoS Med 2 : 216–224.
    [Google Scholar]
  19. Ansenlin L, 2003. GeoDa 0.9 User’s Guide: Spatial Analysis Laboratory (SAL). Department of Agricultural and Consumer Economics, University of Illinois, Urbana-Champaign, IL.
  20. Meza JL, 2003. Empirical Bayes estimation smoothing of relative risks in disease mapping. J Stat Plan Infer 112 : 43–62.
    [Google Scholar]
  21. Assuncao RM, Reis EA, 1999. A new proposal to adjust Moran’s I for population density. Stat Med 18 : 2147–2162.
    [Google Scholar]

Data & Media loading...

  • Received : 13 Sep 2008
  • Accepted : 20 May 2009
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error