Zhang Lin, Hou Xuexia, Liu Huixin, Liu Wei, Wan Kanglin, Hao Qin
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.
Zhonghua Liu Xing Bing Xue Za Zhi. 2016 Jan;37(1):94-7. doi: 10.3760/cma.j.issn.0254-6450.2016.01.020.
To predict the potential geographic distribution of Lyme disease in Qinghai by using Maximum Entropy model (MaxEnt).
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.
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).
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.
运用最大熵模型（MaxEnt）预测青海莱姆病的潜在地理分布。
收集了青海省6个县（互助、泽库、同德、大通、祁连和循化）莱姆病的血清学诊断数据以及1990年以来的环境和人为数据，包括海拔、人类足迹、归一化植被指数（NDVI）和温度。利用互助、泽库和同德的数据，采用MaxEnt对青海莱姆病的潜在分布进行预测。将预测结果与青海大通、祁连和循化三县人群莱姆病血清学患病率进行比较。
预测出青海有3个莱姆病热点地区，均位于东部林区。此外，NDVI在模型预测中作用最为重要，其次是人类足迹。大通、祁连和循化三县均位于青海东部。循化处于热点区域Ⅱ，大通靠近热点区域Ⅲ北部，而莱姆病血清学患病率最低的祁连不在热点区域。数据在MaxEnt中得到了良好建模（曲线下面积=0.980）。
青海莱姆病的实际分布与模型预测结果一致。MaxEnt可用于预测莱姆病的潜在分布模式。植被分布以及人类活动范围和强度可能与莱姆病分布有关。
