Science China Earth Sciences

, Volume 53, Supplement 1, pp 8–15 | Cite as

Spatial prediction and analysis of Himalayan marmot plague natural epidemic foci in China based on HJ-1 satellite data

  • MengXu Gao
  • XiaoWen LiEmail author
  • ChunXiang Cao
  • Hao Zhang
  • Qun Li
  • Hang Zhou
  • QiSheng He
  • Min Xu
  • Jian Zhao
  • Sheng Zheng
  • Wei Chen
Research Paper


Plague, caused by the gram-negative bacterium Yersinia pestis, is a serious and rapidly progressing illness in humans that can be fatal if not treated effectively. The Qinghai-Tibet Plateau is the largest area of natural Himalayan marmot (Marmota himalayana) plague foci in China and covers more than 630000 km2. Akesai County in Gansu Province is a part of this natural focus of plague and was chosen as a study area. Our study used an ecological niche modeling (ENM) approach to predict the potential distribution of the Himalayan marmot. Environment and Disaster Monitor Satellite (HJ-1) data was used to investigate environment factors that affect plague host animal activity. Host animal point data from active surveillance was combined with environmental variables from the HJ-1 satellite and other databases, and the models of the potential distribution of Himalayan marmot were produced with the Genetic Algorithm for Rule-Set Production (GARP). The probability of marmot presence was divided into 0–5%, 5%–20%, 20%–40%, 40%–80%, and 80%–100% subgroups. Areas with 80%-100% probability exhibited the greatest potential for the presence of Himalayan marmot. According to the predicted potential distribution of Himalayan marmot in the study area, active surveillance of plague hosts and plague control and prevention could be more efficient.


Himalayan marmot plague spatial prediction GARP HJ-1 satellite 


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Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • MengXu Gao
    • 1
    • 4
  • XiaoWen Li
    • 1
    • 2
    Email author
  • ChunXiang Cao
    • 1
  • Hao Zhang
    • 1
  • Qun Li
    • 3
  • Hang Zhou
    • 3
  • QiSheng He
    • 1
    • 4
  • Min Xu
    • 1
    • 4
  • Jian Zhao
    • 1
    • 4
  • Sheng Zheng
    • 1
    • 4
  • Wei Chen
    • 1
    • 4
  1. 1.State Key Laboratory of Remote Sensing ScienceJointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal UniversityBeijingChina
  2. 2.School of GeographyBeijing Normal UniversityBeijingChina
  3. 3.Office for Disease Control and Emergency ResponseChinese Center for Disease Control and PreventionBeijingChina
  4. 4.Graduate University of Chinese Academy of SciencesBeijingChina

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