ESTIMATING TIME-VARYING REPRODUCTION NUMBER BY DEEP LEARNING TECHNIQUES
Pengfei Song, Yanni Xiao · Journal of Applied Analysis & Computation · 2022
Estimating time-varying reproduction number $ \mathcal{R}_{t} $ is important for quantifying the transmission ability, capturing the trend of infectious disease and assessing the effectiveness of public health intervention measures. However, accurate estimation of $ \mathcal{R}_{t} $ remains a challenging work. Deep neural networks are uniform approximators and have an unreasonable and counterintuitive effectiveness in learning unknown functions, thus can be applied to represent $ \mathcal{R}_{t} $. In this paper, we will estimate $ \mathcal{R}_{t} $ by universal differential equation method which embeds neural network $ \mathcal{R}_{t} $ into a differential equation. Compared with other methods such as state space, EpiEstim and EpiNow2 methods, deep learning method can achieve better performance with fewer data sources.