An early warning indicator trained on stochastic disease-spreading
  models with different noises
Amit K. Chakraborty Shan Gao Reza Miry Pouria Ramazi Russell Greiner Mark A. Lewis Hao Wang
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.