School of Public Health, Southeast University, Nanjing, China.
Department of Remote Sensing Imagery, Provincial Geomatics Center of Jiangsu, Nanjing, China.
Vector Borne Zoonotic Dis. 2019 Oct;19(10):758-766. doi: 10.1089/vbz.2018.2425. Epub 2019 Apr 17.
Severe fever with thrombocytopenia syndrome (SFTS) is an emerging natural focus, tick-borne disease caused by a novel bunyavirus named SFTS virus (SFTSV). The main purpose of this study was to analyze the environmental risk factors and geographic distribution of SFTS natural foci in Jiangsu Province. A retrospective space-time analysis by SaTScan software was used to detect clusters at the town level. The maximum entropy modeling method was applied to construct the ecological niche model and analyze the environmental risk factors, and then to draw the predicted risk map. The performance of the model was assessed using the area under the curve (AUC) and known occurrence locations. During the years 2010-2016, a total of 140 laboratory-confirmed indigenous SFTS cases occurred in Jiangsu Province, with 66 occurrence locations. The reported number of SFTS cases increased year by year and SFTS cases occurred from April to October with a peak between May and August each year. Three clusters detected by space-time scan statistical analysis were connected together and shared the similar ecological environmental characteristic of hilly landscape. Fifteen environmental variables with different percent contribution can influence the ecological niche model in different degrees, whereas slope (suitable range: 0.1-4) and maximum temperature of warmest month (suitable range: 32.8-34.2°C) as the key environmental factors contributed 46.1% and 23.2%, respectively. The model had high accuracy on prediction with the averaged training AUC of 0.926. Within a predicted risk map, potential areas at high risk and 10 previously unidentified endemic regions were recognized. The distribution of SFTS natural foci was under the influence of multidimensional environmental factors. Slope and maximum temperature of warmest month were the key environmental risk factors. These results provide a valuable basis for the selection of prevention and control strategies, and the identification of potential risk areas.
严重发热伴血小板减少综合征(SFTS)是一种由新型布尼亚病毒(SFTSV)引起的新兴自然疫源地、蜱传疾病。本研究旨在分析江苏省 SFTS 自然疫源地的环境风险因素和地理分布。采用 SaTScan 软件进行回顾性时空分析,以镇为单位检测聚集区。应用最大熵建模方法构建生态位模型,分析环境风险因素,并绘制预测风险图。采用曲线下面积(AUC)和已知发病地点评估模型性能。2010-2016 年,江苏省共报告实验室确诊本地 SFTS 病例 140 例,发病地点 66 个。报告病例数呈逐年上升趋势,SFTS 病例发病时间为 4 月至 10 月,每年 5-8 月为发病高峰。时空扫描统计分析检测到的 3 个聚集区相互连接,具有相似的丘陵地貌生态环境特征。15 个环境变量以不同的百分比贡献影响生态位模型,其中坡度(适宜范围:0.1-4)和最热月最高温度(适宜范围:32.8-34.2°C)作为关键环境因素,分别贡献了 46.1%和 23.2%。模型对预测的准确率较高,平均训练 AUC 为 0.926。在预测风险图中,识别出了潜在的高风险区域和 10 个以前未识别的地方性区域。SFTS 自然疫源地的分布受多维环境因素的影响。坡度和最热月最高温度是关键的环境风险因素。这些结果为选择防控策略和识别潜在风险区域提供了有价值的依据。