A Predictive Risk Model for A(H7N9) Human Infections Based on Spatial-Temporal Autocorrelation and Risk Factors: China, 2013-2014.
A Predictive Risk Model for A(H7N9) Human Infections Based on Spatial-Temporal Autocorrelation and Risk Factors: China, 2013-2014.
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DOI：
10.3390/ijerph121214981
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发表时间：
2015-12-01
影响因子：
--
通讯作者：
Yang YL
中科院分区：
文献类型：
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作者：
Dong W;Yang K;Xu QL;Yang YL
本研究分析了2013年3月至2014年12月中国H7N9人间疫情的空间分布、空间自相关、时间聚集性、时空自相关和可能的危险因素。结果表明，疫情传播具有显著的时空自相关性。为了描述H7N9的时空自相关性，引入时空因子，建立了一种改进的时空自相关模型。使用逻辑回归分析来调查与其分布相关的风险因素，9个风险因素与A（H7N9）人感染的发生显著相关：时空因子φ（OR = 2546669.382，p < 0.001），迁移途径（OR = 0.993，p < 0.01），河流（OR = 0.861，p < 0.001），湖（OR = 0.992，p < 0.001），公路（OR = 0.906，p < 0.001），铁路（OR = 0.980，p < 0.001）、温度（OR = 1.170，p < 0.01）、降水量（OR = 0.615，p < 0.001）和相对湿度（OR = 1.337，p < 0.001）。改进后的模型比传统模型具有更好的预测性能和更高的拟合精度：在改进的模型中，2014年2月期间90.1%（91/101）的病例发生在高风险地区（预测风险> 0.70）的预测风险图，其中44.6%（45/101）覆盖在高风险地区（预测风险> 0.70）对于传统模型，改进模型的拟合准确率为91.6%，优于传统模型的86.1%（上级）。基于改进模型生成的预测风险地图显示，2014年2月我国东部和东南部地区为甲型H7N9流感疫情的高发区。这些结果为控制和预防未来的人类感染提供了基线数据。
This study investigated the spatial distribution, spatial autocorrelation, temporal cluster, spatial-temporal autocorrelation and probable risk factors of H7N9 outbreaks in humans from March 2013 to December 2014 in China. The results showed that the epidemic spread with significant spatial-temporal autocorrelation. In order to describe the spatial-temporal autocorrelation of H7N9, an improved model was developed by introducing a spatial-temporal factor in this paper. Logistic regression analyses were utilized to investigate the risk factors associated with their distribution, and nine risk factors were significantly associated with the occurrence of A(H7N9) human infections: the spatial-temporal factor φ (OR = 2546669.382, p < 0.001), migration route (OR = 0.993, p < 0.01), river (OR = 0.861, p < 0.001), lake(OR = 0.992, p < 0.001), road (OR = 0.906, p < 0.001), railway (OR = 0.980, p < 0.001), temperature (OR = 1.170, p < 0.01), precipitation (OR = 0.615, p < 0.001) and relative humidity (OR = 1.337, p < 0.001). The improved model obtained a better prediction performance and a higher fitting accuracy than the traditional model: in the improved model 90.1% (91/101) of the cases during February 2014 occurred in the high risk areas (the predictive risk > 0.70) of the predictive risk map, whereas 44.6% (45/101) of which overlaid on the high risk areas (the predictive risk > 0.70) for the traditional model, and the fitting accuracy of the improved model was 91.6% which was superior to the traditional model (86.1%). The predictive risk map generated based on the improved model revealed that the east and southeast of China were the high risk areas of A(H7N9) human infections in February 2014. These results provided baseline data for the control and prevention of future human infections.