An early warning indicator trained on stochastic disease-spreading models with different noises

准备 计算机科学 噪音(视频) 预警系统 爆发 传染病(医学专业) 疾病 疾病监测 机器学习 人工智能 数据科学 风险分析(工程) 医学 电信 病理 图像(数学) 法学 政治学
作者
Amit K. Chakraborty,Shan Gao,Reza Miry,Pouria Ramazi,Russell Greiner,Mark A. Lewis,Hao Wang
出处
期刊:Journal of the Royal Society Interface [Royal Society]
卷期号:21 (217) 被引量:2
标识
DOI:10.1098/rsif.2024.0199
摘要

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 modelling 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 outbreaks 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.
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