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Updated: Jun 22, 2025
High-throughput Detection Method for Influenza Virus
Published on: February 4, 2012
Comparison Between Threshold Method and Artificial Intelligence Approaches for Early Warning of Respiratory
Ting Zhang1,2,3, Liuyang Yang4,5, Ziliang Fan6
1School of Population Medicine and Public Health, Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical College (PUMC), Beijing, China.
Introduction:
Respiratory infectious diseases, such as influenza and coronavirus disease 2019 (COVID-19), present significant global public health challenges. The emergence of artificial intelligence (AI) and big data offers opportunities to improve traditional disease surveillance and early warning systems.
Methods:
The study analyzed data from January 2020 to May 2023, comprising influenza-like illness (ILI) statistics, Baidu index, and clinical data from Weifang. Three methodologies were evaluated: the adaptive dynamic threshold method (ADTM) for dynamic threshold adjustments, the machine learning supervised method (MLSM), and the machine learning unsupervised method (MLUM) utilizing anomaly detection. The comparison focused on sensitivity, specificity, timeliness, and warning consistency.
Results:
ADTM issued 37 warnings with a sensitivity of 71% and a specificity of 85%. MLSM generated 35 warnings, with a sensitivity of 82% and a specificity of 87%. MLUM produced 63 warnings with a sensitivity of 100% and specificity of 80%. The initial warnings from ADTM and MLUM preceded those from MLSM by five days. The Kappa coefficient indicated moderate agreement between the methods, with values ranging from 0.52 to 0.62 (P<0.05).
Discussion:
The study explores the comparison between traditional methods and two machine learning approaches for early warning systems. It emphasizes the validation of machine learning's reliability and underscores the unique advantages of each method. Furthermore, it stresses the significance of integrating machine learning models with various data sources to enhance public health preparedness and response, alongside acknowledging limitations and the need for broader validation.
