| 吴昊澄,徐校平,吴晨,鲁琴宝,丁哲渊,林君芬.浙江省2011-2015年发热伴血小板减少综合征发病空间预测[J].中华流行病学杂志,2016,37(11):1485-1490 |
| 浙江省2011-2015年发热伴血小板减少综合征发病空间预测 |
| Spatial analysis and prediction of severe fever with thrombocytopenia syndrome in Zhejiang province，2011-2015 |
| 收稿日期:2016-04-07 出版日期:2016-11-10 |
| DOI：10.3760/cma.j.issn.0254-6450.2016.11.011 |
| 中文关键词: 发热伴血小板减少综合征 空间自相关 Kriging插值 预测 |
| 英文关键词: Severe fever with thrombocytopenia syndrome Spatial autocorrelation Kriging interpolation Prediction |
| 基金项目:浙江省医药卫生科技计划（2015RCB008，2014ZDA003，2015ZHA003） |
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| 中文摘要: |
| 目的 了解浙江省发热伴血小板减少综合征（SFTS）发病空间分布特征，预测发病区域及聚集性疫情发生概率。方法 收集2011-2015年浙江省SFTS发病个案数据，使用ArcGIS 10.0软件进行空间分析，利用Moran's I和G统计量探索空间自相关；采用趋势面分析发病趋势；以Kriging插值进行预测。结果 2011-2015年浙江省报告SFTS发病数逐年上升，报告发病地区扩大，发病时间和人群特征与既往研究结果相似；SFTS发病具有地区聚集性（P<0.001），全省发病自东北向西南方向呈下降趋势；采用Kriging预测精度较好，预测发病区域较为广泛，以安吉、岱山、天台县发生聚集性病例概率相对更高，内部地区预测误差小于边缘地区。结论 Kriging插值预测SFTS发病较为准确，浙江省SFTS病例发生区域和水平均高于目前报告水平，以安吉、岱山、宁海、天台、三门、临海等地区发生聚集性疫情风险较高。 |
| 英文摘要: |
| Objective To understand the distribution of the severe fever with thrombocytopenia syndrome (SFTS) in Zhejiang province, and predict the incidence and the probability of SFTS outbreak. Methods Based on the cases of SFTS from 2011-2015, software ArcGIS 10.0 was used to analyze the spatial distribution, Moran's I and Getis-Ord Gi were used to analyze the spatial autocorrelation. The incidence trend was explored by trend surface analysis, and the prediction was made by Kriging interpolation. Results The incidence of SFTS increased and the distribution expanded in Zhejiang from 2011 to 2015, the seasonal and the demographic characteristics of SFTS were similar to the previous research; there were regional clustering of the cases (P<0.001); a downward trend was observed from northeast to southwest in terms of incidence of SFTS; the second-order disjunctive Kriging interpolation based on circular model and the indicator Kriging interpolation based on exponential model had higher prediction accuracy, the probabilities of outbreak in Anji, Daishan and Tiantai were high, the prediction deviation of inland was less than that of edge area. Conclusion The prediction of SFTS by Kriging interpolation had high accuracy, the incidence of SFTS was higher and the distribution of SFTS was larger than the results of surveillance, the risk areas for epidemic were Anji, Daishan, Ninghai,Tiantai, Sanmen and Linhai. |
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