【摘要】 目的:探讨气象因素对手足口病(HFMD)流行的影响,为该地区HFMD防控与政策制定提供依据。方法:收集河南省郑州市二七区2008年5月至2014年6月气象资料(气温、气压、相对湿度、平均风速、降雨量、平均日照时间)和HFMD疫情资料。采用Spearman秩相关分析气象参数与HFMD的相关性,采用互相关分析气象参数对HFMD流行的滞后效应,采用时间序列分析构建该地区HFMD季节性自回归移动平均(SARIMA)模型,比较引入气象参数前后模型的拟合优度和预测精度。结果:该地区HFMD流行集中于3~7月份,4~5月份达到高峰。HFMD周发病人数与每周日平均气温滞后2周(rS=0.248,P<0.05)、最高气温滞后2周(rS=0.170,P<0.05)、最低气温滞后2周(rS=-0.223,P<0.05)相关。每周日平均气温滞后2周纳入HFMD周发病人数SARIMA(1,1,0)(0,1,0)52预测模型。引入气象参数前、后模型的拟合度为0.797、0.833,预测精度为11.573、10.611。结论:平均气温可影响HFMD的流行,引入平均气温构建的SARIMA模型能较好地拟合和预测HFMD的流行。更多还原

【Abstract】 Aim: To examine the potential effects of meteorological factors on the hand-foot-mouth disease( HFMD)epidemic,so as to explore the method to prevent and control HFMD. Methods: The daily incidence and weather surveillance data were provided by Erqi Center for Disease Control and Prevention and Zhengzhou Meteorological Administration respectively. The correlation between the meteorological parameters and the weekly number of HFMD in Erqi District were analyzed with Spearman’s rank correlation tests with and without time-lag. Time series modeling was carried out using multivariate SARIMA models when there was significant predictor meteorological variable. Results: HFMD was prevalent from March to July and peaks in April and May. Spearman’s rank correlation test indicated that average atmospheric temperature at lag 2 weeks( rS= 0. 248,P < 0. 05),maximum atmospheric temperature at lag 2 weeks( rS= 0. 170,P < 0. 05) and minimum atmospheric temperature at lag 2 weeks( rS=- 0. 223,P < 0. 05) were correlated with the weekly number of HFMD. Average atmospheric temperature at lag 2 weeks was identified as a significant predictor for the weekly number of HFMD. SARIMA( 1,1,0)( 0,1,0)52associated with average atmospheric temperature at lag 2 weeks were developed and validated for description and predication of the weekly number of HFMD. The goodness of fit [stationary square R( R2) =0. 797] and the predictive power [root mean square error( RMSE) = 11. 573]of the model were enhanced by the inclusion of climatic variable to forecast the number of HFMD in the year 2014 compared with the univariate model( R2= 0. 833,RMSE = 10. 611). Conclusion: Temperatures affect the occurrence of HFMD,and might be used as meteorological predictors for the epidemic of HFMD in this region.更多还原