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[2011 - 2015年浙江省血小板减少综合征伴严重发热的空间分析与预测]

[Spatial analysis and prediction of severe fever with thrombocytopenia syndrome in Zhejiang province, 2011-2015].

作者信息

Wu H C, Xu X P, Wu C, Lu Q B, Ding Z Y, Lin J F

机构信息

Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310051, China.

出版信息

Zhonghua Liu Xing Bing Xue Za Zhi. 2016 Nov 10;37(11):1485-1490. doi: 10.3760/cma.j.issn.0254-6450.2016.11.011.

Abstract

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. Based on the cases of SFTS from 2011-2015, software ArcGIS 10.0 was used to analyze the spatial distribution, Moran's 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. 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 (<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. 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.

摘要

为了解浙江省发热伴血小板减少综合征(SFTS)的分布情况,并预测SFTS的发病情况及暴发概率。基于2011 - 2015年SFTS病例,运用ArcGIS 10.0软件分析空间分布,采用Moran's指数和Getis - Ord Gi指数分析空间自相关性。通过趋势面分析探索发病趋势,并采用克里金插值法进行预测。2011年至2015年浙江省SFTS发病率上升且分布范围扩大,SFTS的季节和人口学特征与以往研究相似;病例存在区域聚集性(<0.001);SFTS发病率从东北向西南呈下降趋势;基于圆形模型的二阶析取克里金插值法和基于指数模型的指示克里金插值法预测精度较高,安吉、岱山和天台暴发概率较高,内陆地区预测偏差小于边缘地区。克里金插值法对SFTS的预测精度较高,SFTS发病率高于监测结果且分布范围更大,疫情风险地区为安吉、岱山、宁海、天台、三门和临海。

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