Cui Jinzhao, Zhang Ting, Shen Yifeng, Wang Xiaoli, Yang Liuyang, Huang Xuefeng, Huang Qiang, Yang Yu, Yang Weizhong, Li Zhongjie
School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 31, Beijigesantiao street, Dongcheng District, Beijing, 102206, China, 86 18612690539.
Shanghai Pudong New Area Center for Disease Control and Prevention (Shanghai Pudong New Area Health Supervision Institute), Shanghai, China.
JMIR Med Inform. 2025 Aug 6;13:e73631. doi: 10.2196/73631.
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 failing 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.
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 influenza-like illness positive rate, 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.
We developed a Dense ResNet machine learning model trained on influenza surveillance data from Northern and Southern China (2014-2024). This model generates early warning signals 3, 5, and 7 days in advance, providing a probability-based risk assessment represented as a continuous variable ranging from 0 to 1, in contrast to the traditional binary (0/1) warning systems. We evaluated the performance of this model using area under the curve scores, accuracy, recall, and F1-scores, then compared it with support vector machine (SVM), random forests, XGBoost (Extreme Gradient Boosting), and LSTM (long short-term memory) models.
The Dense ResNet model demonstrated the best performance, characterized by 5-day lead warnings and a 50th percentile probability threshold, achieving area under the curve 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.
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 use of automated artificial intelligence-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.
季节性流感是全球主要的公共卫生问题，导致发病率和死亡率不断上升。传统的早期预警模型依赖二元（0/1）分类方法，只有在超过预定义阈值时才发出警报。然而，这些模型缺乏灵活性，常常导致误报或漏报，并且无法提供决策所需的详细风险评估。因此，我们提出一种基于概率的早期预警系统，利用机器学习来缓解这些局限性，并提供对警报（0-1变量）的连续风险估计，而不是基于固定阈值的警报。基于概率预测，公共卫生专家可以结合实际情况做出更灵活的决策，显著降低决策过程中的不确定性和压力，减少公共卫生资源的浪费以及社会恐慌的风险。
本研究的主要目的是为类流感病例的早期预警系统设计一种创新方法。因此，开发了一种密集残差网络（Dense ResNet），这是一种有监督的深度学习模型。该模型的训练涉及拟合类流感疾病阳性率，从而能够早期检测和预警类流感病例活动水平变化的信号。这种与传统方法的不同突出了机器学习的变革潜力，特别是在流感爆发背景下提供及时主动预警的先进能力。
我们开发了一种基于中国北方和南方（2014 - 2024年）流感监测数据训练的密集残差网络机器学习模型。该模型提前3天、5天和7天生成早期预警信号，提供基于概率的风险评估，以0到1的连续变量表示，这与传统的二元（0/1）预警系统不同。我们使用曲线下面积得分、准确率、召回率和F1得分评估该模型的性能，然后将其与支持向量机（SVM）、随机森林、XGBoost（极端梯度提升）和LSTM（长短期记忆）模型进行比较。
密集残差网络模型表现出最佳性能，其特点是提前5天发出预警，概率阈值为第50百分位数，在中国北方的曲线下面积得分为0.94，在中国南方为0.95。相对于传统模型，基于概率的预警信号提高了早期检测能力，减少了误报，并促进了分级公共卫生应对措施。
本研究提出了一种对流感早期预警信号至关重要的新型基于概率的机器学习模型，与其他技术相比，具有更高的准确性、灵活性和实际适用性。这种方法增强了人群对流感的防范能力，并通过用概率驱动的风险评估取代二元预警，促进了自动化人工智能驱动的公共卫生应对措施的应用。未来研究应整合实时监测数据和动态传播模型，以提高早期预警的精度。
