The timely detection of disease outbreaks through reliable early warning signals (EWSs) is indispensable for effective public health mitigation strategies. Nevertheless, the intricate dynamics of real-world disease spread, often influenced by diverse sources of noise and limited data in the early stages of outbreaks, pose a significant challenge in developing reliable EWSs, as the performance of existing indicators varies with extrinsic and intrinsic noises. Here, we address the challenge of modeling disease when the measurements are corrupted by additive white noise, multiplicative environmental noise, and demographic noise into a standard epidemic mathematical model. To navigate the complexities introduced by these noise sources, we employ a deep learning algorithm that provides EWS in infectious disease outbreak by training on noise-induced disease-spreading models. The indicator's effectiveness is demonstrated through its application to real-world COVID-19 cases in Edmonton and simulated time series derived from diverse disease spread models affected by noise. Notably, the indicator captures an impending transition in a time series of disease outbreaks and outperforms existing indicators. This study contributes to advancing early warning capabilities by addressing the intricate dynamics inherent in real-world disease spread, presenting a promising avenue for enhancing public health preparedness and response efforts.

翻译：通过可靠的早期预警信号及时检测疾病暴发，对于实施有效的公共卫生缓解策略至关重要。然而，现实世界中疾病传播的复杂动态往往受到多种噪声源的影响，且在暴发初期数据有限，这给开发可靠的早期预警信号带来了重大挑战，因为现有指标的性能会随外在和内在噪声的变化而波动。本文研究在标准流行病学数学模型中，当测量数据受到加性白噪声、乘性环境噪声和人口统计噪声干扰时的疾病建模问题。为应对这些噪声源带来的复杂性，我们采用深度学习算法，通过在噪声诱导的疾病传播模型上进行训练，提供传染病暴发的早期预警信号。该指标的有效性通过其在加拿大埃德蒙顿市真实COVID-19病例数据及受噪声影响的不同疾病传播模型生成的模拟时间序列中的应用得到验证。值得注意的是，该指标能够捕捉疾病暴发时间序列中即将发生的转变，且性能优于现有指标。本研究通过解决现实疾病传播中固有的复杂动态，提升了早期预警能力，为加强公共卫生准备和响应工作提供了有前景的途径。