• 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.

    • Search Google Scholar
    • Export Citation
  • 4

    Elliott P, Wartenberg D, 2004. Spatial epidemiology: current approaches and future challenges. Environ Health Perspect 112 :998–1006.

  • 5

    Ostfeld RS, Glass GE, Keesing F, 2005. Spatial epidemiology: an emerging (or re-emerging) discipline. Trends Ecol Evol 20 :328–335.

  • 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.

    • Search Google Scholar
    • Export Citation
  • 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.

    • Search Google Scholar
    • Export Citation
  • 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.

    • Search Google Scholar
    • Export Citation
  • 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.

    • Search Google Scholar
    • Export Citation
  • 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.

    • Search Google Scholar
    • Export Citation
  • 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.

    • Search Google Scholar
    • Export Citation
  • 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.

    • Search Google Scholar
    • Export Citation
  • 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.

    • Search Google Scholar
    • Export Citation
  • 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.

  • 21

    Assuncao RM, Reis EA, 1999. A new proposal to adjust Moran’s I for population density. Stat Med 18 :2147–2162.

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Spatio-Temporal Distribution of Malaria in Yunnan Province, China

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  • 1 State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, People’s Republic of China; College of Global Change and Earth System Science, Beijing Normal University, Beijing, People’s Republic of China; State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, People’s Republic of China; Department of Environmental Science and Engineering, Tsinghua University, Beijing, People’s Republic of China; Yunnan Institute of Parasitic Diseases, Pu’er, People’s Republic of China; National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People’s Republic of China
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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 Plasmodium vivax and Plasmodoium falciparum 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.

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