JMIR Publications

ABSTRACT

Background:

Seasonal influenza is a major global public health concern, leading to escalated morbidity and mortality rates. Traditional early warning models rely on binary (0/1) classification methods, which issue alerts only when predefined thresholds are crossed. However, these models exhibit inflexibility, often leading to false alarms or missed warnings, and fail to provide granular risk assessments essential for decision-making. Therefore, we propose a probability-based early warning system using machine learning to mitigate these limitations and to offer continuous risk estimations of alerts (0–1 variable) instead of rigid threshold-based alerts. Based on probabilistic prediction, public health experts can make more flexible decisions in combination with the actual situation, significantly reducing the uncertainty and pressure in the decision-making process and reducing the waste of public health resources and the risk of social panic.

Objective:

The main aim of this study is to devise an innovative approach for early warning systems focused on influenza-like cases. Therefore, a Dense Residual Network (Dense ResNet), a supervised deep learning model, was developed. The model's training involved fitting the ILI multiply Positive label, which enabled the early detection and warning of signals of changes occurring in the activity level of influenza-like cases. This departure from conventional methodologies underscores the transformative potential of machine learning, particularly in providing advanced capabilities for timely and proactive warnings in the context of influenza outbreaks.

Methods:

We developed a Dense Residual Network (Dense ResNet) machine learning model trained on influenza surveillance data from Northern and Southern China (2014–2024). This model generates early warning signs 3, 5, and 7 days in advance of time, providing a probability-based risk assessment represented as a continuous variable ranging from (0–1 continuous variable) in contrast to the traditional binary (0/1) warning systems. We evaluated the performance of this model using AUC scores, accuracy, recall, and F1 scores, then compared it with support vector machines (SVM), random forests, XGBoost (Extreme Gradient Boosting), and LSTM (Long Short-Term Memory) models.

Results:

The Dense ResNet model demonstrated the best performance with characterized by 5-day lead warnings and a 50th percentile probability threshold, achieving AUC scores of 0.94 (Northern China) and 0.95 (Southern China). Relative to traditional models, probability-based warning signals improved early detection, reduced false alarms, and facilitated tiered public health responses.

Conclusions:

This study presented a novel probability-based machine learning model essential for early warning signals of influenza, demonstrating superior accuracy, flexibility, and practical applicability compared to other techniques. This approach enhances preparedness for influenza among the population and promotes the utilization of automated AI-driven public health responses by replacing binary warnings with probability-driven risk assessments. Future research should integrate real-time surveillance data and dynamic transmission models to improve the precision of early warning.