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[Study of school influenza epidemic prediction based on Bayesian Structural Time Series model and multi-source data
1School of Public Health, University of South China, Hengyang 421000, China Department of Infectious Disease Prevention and Control, Shenzhen Center for Disease Control and Prevention, Shenzhen 518000, China.
Summary
This study found a strong temporal correlation between medical surveillance data and school absenteeism for influenza. The Bayesian Structural Time Series model accurately predicted student influenza epidemics, aiding early warning and control efforts.
Area of Science:
- Epidemiology
- Public Health
- Biostatistics
Context:
- Influenza surveillance in schools is crucial for public health.
- Existing surveillance methods may have limitations in timely detection.
- School absenteeism data offers a complementary indicator of disease spread.
Purpose:
- To analyze the spatiotemporal correlation between influenza surveillance data and school absenteeism.
- To evaluate the efficacy of the Bayesian Structural Time Series (BSTS) model for predicting school influenza epidemics.
- To assess the predictive accuracy of BSTS using data from medical institutions and school health systems.
Summary:
- A strong temporal correlation (r=0.93) was observed between influenza incidence reported by medical institutions and school absenteeism data, with a 1-day lag.
- The BSTS model demonstrated good prediction accuracy for both long-term (RMSE=0.35, MAE=0.28) and short-term (RMSE=0.33-0.34, MAE=0.26-0.28) influenza epidemics.
- No significant spatial correlation was found in influenza outbreaks across Shenzhen schools, indicating random distribution.
Impact:
- The findings highlight the value of integrating multiple data sources for robust influenza surveillance.
- Accurate prediction of school influenza dynamics using BSTS can enhance early warning systems.
- This research provides a technical foundation for effective prevention and control strategies against school influenza epidemics.
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