2015-2021年徐州市呼吸道合胞病毒感染与气象因素的关联分析与预测

Analysis and prediction of the association between respiratory syncytial virus infection and meteorological factors in Xuzhou city from 2015 to 2021

目的分析呼吸道合胞病毒感染与气象因素的关联,预测并解释其变化趋势。方法收集2015—2021年徐州市住院儿童的严重急性呼吸道感染病例。通过实时荧光聚合酶链式反应检测呼吸道合胞病毒(RSV),使用SPSS 26.0软件对结果进行统计分析。使用2015—2019年数据构建负二项回归模型,探究对RSV感染有影响的气象因素。使用多变量时间序列模型预测2020—2021年RSV检出率。结果2015—2021年共收集1663例严重急性呼吸道感染患儿样本,其中RSV检出阳性样本218例(13.1%)。RSV季节效应明显,以1年为周期出现冬季(12月至次年2月)为高峰,夏季(6—8月)为低谷。负二项回归分析表明,月平均气温、月平均相对湿度和月总日照时数可能是本地区RSV感染的影响因素。以日照时数为协变量的时间序列模型预测结果表明,2020年预测效果较好,实际值与预测值接近。2021年的预测变化趋势一致,但实际值高于预测值。结论月平均气温降低、月平均相对湿度升高或月总日照时数延长可能是本地区儿童中RSV感染增加的影响因素。可以使用2015—2019年数据建立预测模型,其中2021年预测值的偏差,体现了疾病的流行是多因素相关,提示未来RSV的流行趋势可能会上升。

Objective To analyze the association of respiratory syncytial virus infection with meteorological factors and to predict and explain the trends.Methods Data of cases with severe acute respiratory infections in hospitalized children in Xuzhou City were collected from 2015-2021.Respiratory syncytial virus(RSV)was detected by real-time fluorescence polymerase chain reaction.The result were statistically analyzed using SPSS 26.0 software,including constructing a negative binomial regression model to explore meteorological factors that impact RSV detection and a multivariate time series model to predict its epidemiological trend from 2020 to 2021.Results A total of 1663 samples of children with severe acute respiratory infections were collected from 2015to 2021,of which 218(13.1%)were positive for RSV.Seasonal effects on RSV detection were evident:there was a 1-year cycle with a peak in winter(December-February)and a trough in summer(June-August).The negative binomial regression analysis showed that monthly mean temperature,monthly mean relative humidity,and monthly total sunshine hours may influenced RSV detection.The prediction result of the time series model with sunshine hours as the covariate showed that the prediction was better for 2020,and the actual values were close to the predicted values.The expected trends in 2021 were consistent,but the actual values were higher than predicted.Conclusions Monthly mean temperature,monthly mean relative humidity,and monthly total sunshine hours may influence RSV detection in the Xuzhou region.A prediction model can be built using data from 2015-2019,where deviations in the predicted values for 2021,reflecting that disease prevalence is multifactorial correlated,suggest a possible rise in RSV prevalence in the future.