title: [Prediction of potential geographic distribution of Lyme disease in Qinghai province with Maximum Entropy model].
abstract: OBJECTIVE
  To predict the potential geographic distribution of Lyme disease in Qinghai by using Maximum Entropy model (MaxEnt).
  
  
  METHODS
  The sero-diagnosis data of Lyme disease in 6 counties (Huzhu, Zeku, Tongde, Datong, Qilian and Xunhua) and the environmental and anthropogenic data including altitude, human footprint, normalized difference vegetation index (NDVI) and temperature in Qinghai province since 1990 were collected. By using the data of Huzhu Zeku and Tongde, the prediction of potential distribution of Lyme disease in Qinghai was conducted with MaxEnt. The prediction results were compared with the human sero-prevalence of Lyme disease in Datong, Qilian and Xunhua counties in Qinghai.
  
  
  RESULTS
  Three hot spots of Lyme disease were predicted in Qinghai, which were all in the east forest areas. Furthermore, the NDVI showed the most important role in the model prediction, followed by human footprint. Datong, Qilian and Xunhua counties were all in eastern Qinghai. Xunhua was in hot spot areaⅡ, Datong was close to the north of hot spot area Ⅲ, while Qilian with lowest sero-prevalence of Lyme disease was not in the hot spot areas. The data were well modeled in MaxEnt (Area Under Curve=0.980).
  
  
  CONCLUSIONS
  The actual distribution of Lyme disease in Qinghai was in consistent with the results of the model prediction. MaxEnt could be used in predicting the potential distribution patterns of Lyme disease. The distribution of vegetation and the range and intensity of human activity might be related with Lyme disease distribution.
authors.affiliation: State Key Laboratory for Communicable Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Zhang
authors.forename: Xuexia
authors.initials: X
authors.lastname: Hou
authors.forename: Huixin
authors.initials: H
authors.lastname: Liu
authors.forename: Wei
authors.initials: W
authors.lastname: Liu
authors.forename: Kanglin
authors.initials: K
authors.lastname: Wan
authors.forename: Qin
authors.initials: Q
authors.lastname: Hao
mesh_terms: D000531:Altitude; D002681:China / Q000453:epidemiology; D019277:Entropy; D004777:Environment; D005843:Geography; D006801:Humans; D008193:Lyme Disease / Q000453:epidemiology*; D008962:Models, Theoretical; D013696:Temperature