Prediction of potential geographic distribution of Lyme disease in Qinghai province with Maximum Entropy model
10.3760/cma.j.issn.0254-6450.2016.01.020
- VernacularTitle:基于最大熵模型预测青海省莱姆病的地理分布
- Author:
Lin ZHANG
1
;
Xuexia HOU
;
Huixin LIU
;
Wei LIU
;
Kanglin WAN
;
Qin HAO
Author Information
1. 中国疾病预防控制中心传染病预防控制所传染病预防控制国家重点实验室
- Keywords:
Lyme disease;
Maximum Entropy models;
Prediction;
Hot spot area
- From:
Chinese Journal of Epidemiology
2016;37(1):94-97
- CountryChina
- Language:Chinese
-
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 area Ⅱ,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.
