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Updated: Jan 11, 2026
High-throughput Detection Method for Influenza Virus
Published on: February 4, 2012
Developing Machine Learning Prediction Model for Daily Influenza Reported Cases Using Multichannel Surveillance Data
Xinyue Zhang1, Xinyi Sang1, Beibei Liu1
1Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan City, Hubei Province, China.
Introduction:
Public health surveillance is crucial for decision-making. Given the significant threat of influenza to public health, developing predictive models using multichannel surveillance systems is imperative.
Methods:
Data were collected from multichannel surveillance systems, including hospitals, search engines, and climatological and air pollutant surveillance systems, in a southern Chinese city from January 2023 to January 2025. Spearman's correlation analysis assessed the relationships between variables and reported influenza cases. Several machine learning models were used to predict trends in reported cases.
Results:
Correlation analysis showed that all four surveillance systems were related to influenza, with 27 variables correlated with daily reported cases. The Long Short-Term Memory model, established based on variables with the highest lagged correlations (5-day to 7-day lag) through combined surveillance systems, outperformed other models for 5-day forecasts (R2=0.92; mean absolute error=156.92; mean absolute percentage error=79.95%; root Mean Squared Error=292.33).
Conclusions:
Data from various surveillance systems effectively track influenza epidemics. The model shows potential for infectious disease surveillance and epidemic preparedness.
