Related Experiment Video
Updated: May 29, 2025

An In vitro Model to Study Immune Responses of Human Peripheral Blood Mononuclear Cells to Human Respiratory Syncytial Virus Infection
Published on: December 10, 2013
Relationship between RSV-hospitalized children and meteorological factors: a time series analysis from 2017 to 2023
Shuying Wang1,2, Yifan Wang1,2, Yingxue Zou3
1Department of Pulmonology, Tianjin Children's Hospital (Children's Hospital of Tianjin University), Machang Compus, 225 Machang Road, Hexi District, Tianjin, 300074, China.
Insights
Higher temperatures correlate with fewer infant hospitalizations for respiratory syncytial virus (RSV). This study analyzed RSV trends and weather in Tianjin, informing prevention strategies and resource allocation.
Area of Science:
- Epidemiology
- Environmental Health
- Virology
Background:
- Respiratory syncytial virus (RSV) is a major cause of infant hospitalizations globally.
- RSV incidence shows seasonal patterns influenced by meteorological factors.
- Understanding these patterns is crucial for public health interventions.
Purpose of the Study:
- To analyze RSV hospitalization seasonality in Tianjin.
- To investigate the association between meteorological factors and RSV hospitalizations.
- To inform RSV prevention and resource allocation strategies.
Main Methods:
- Analysis of 6222 children hospitalized with RSV.
- Collection of meteorological data (temperature, pressure, wind, humidity, precipitation).
- Application of seasonal ARIMA and Generalized Additive Models (GAM).
Main Results:
- Seasonal ARIMA (1,0,0) (0,1,2)12 model accurately predicted RSV admissions.
- A significant negative correlation was found between monthly average temperature and RSV hospitalizations.
- Higher average temperatures were linked to decreased RSV hospitalizations.
Conclusions:
- Meteorological factors significantly impact RSV hospital admissions.
- Predictive models can enhance RSV prevention strategies and resource management.
- Continued development of vaccines and therapeutics is essential for public health.
Objectives:
Respiratory syncytial virus (RSV) is a leading cause of hospitalization for lower respiratory tract infections amongst infants under 1 year, posing a significant global health challenge. The incidence of RSV exhibits marked seasonality and is influenced by various meteorological factors, which vary across regions and climates. This study aimed to analyze seasonal trends in RSV-related hospitalization in Tianjin, a region with a semi-arid and semi-humid monsoon climate, and to explore the relationship between these trends and meteorological factors. This research intends to inform RSV prevention strategies, optimize public health policies and medical resource allocation while also promoting vaccine and therapeutic drug development.
Methods:
This study analyzed data from a cohort of 6222 children hospitalized with RSV-related infections. Meteorological data were collected from the Tianjin Binhai International Airport meteorological station, encompassing temperature (℃), air pressure (mmHg), wind speed (m/s), humidity (%), and precipitation (mm). We employed seasonal ARIMA and GAM models to investigate the association between meteorological factors and RSV-related hospitalizations.
Results:
The SARIMA (1,0,0) (0,1,2)12 model effectively predicted RSV-related hospital admissions. Spearman correlation and GAM analysis revealed a significant negative association between the monthly average temperature and RSV hospitalizations.
Conclusions:
Our findings indicated that meteorological factors influence RSV infection-related hospital admissions, with higher monthly average temperatures associated with fewer hospitalizations. The predictive capabilities of the SARIMA model bolster the formulation of targeted RSV prevention strategies, enhancing public health policy and medical resource allocation. Furthermore, continued research into vaccines and therapeutic drugs remains indispensable for augmenting public health outcomes.
Related Concept Videos
Steps in Outbreak Investigation
Interpreting Run Charts
Regression Analysis
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
What is Weather?
Precipitation and Co-precipitation
Statistical Methods for Analyzing Epidemiological Data

