Analysis and prediction of the association between respiratory syncytial virus infection and meteorological factors in Xuzhou city from 2015 to 2021
10.3760/cma.j.cn-112866-20230202-00007
- VernacularTitle: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
- Author:
Rundong CAO
1
;
Dong XIA
;
Qinqin SONG
;
Juan SONG
;
Zhiqiang XIA
;
Mi LIU
;
Haijun DU
;
Renhe ZHU
;
Jun HAN
;
Chen GAO
Author Information
1. 中国疾病预防控制中心病毒病预防控制所，北京 102206
- Keywords:
Respiratory syncytial virus;
Meteorological factors;
Negative binomial regression;
Multivariate time series model
- From:
Chinese Journal of Experimental and Clinical Virology
2023;37(2):152-158
- CountryChina
- Language:Chinese
-
Abstract:
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 1 663 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.
