Enhancing Online Epidemic Supervising System by Compartmental and GRU Fusion Model

Abstract

The global pandemic, COVID-19, is an acute respiratory infectious disease caused by the 2019 novel coronavirus. Building the online epidemic supervising system to provide COVID-19 dynamic prediction and analysis has attracted the attention of the industry and applications community. In previous studies, the compartmental models and deep neural networks (DNNs) played important roles in predicting and analyzing the dynamics of the pandemic. Nevertheless, the compartmental model has limited ability to fit historical data and thus leads to unsatisfactory prediction accuracy due to the difficulty in parameter estimation. For DNNs, the lack of interpretability makes it difficult to explain the prediction results; thus, it cannot provide an in-depth understanding of the transmission mechanism of the pandemic. We propose a fusion model to leverage the merits of both models and resolve their shortcomings. The fusion model extracts epidemic-related knowledge from the state-of-the-art SEIDR compartmental model to guide the training of the GRU model, which can preserve the interpretability and achieve a good performance in predicting epidemic dynamics. This model can help to enhance the online epidemic supervising system by providing more accurate prediction results and deeper analysis. Our extensive experiments across multiple epidemic datasets from six European countries demonstrate that our model outperforms existing state-of-the-art baselines in predicting the active confirmed cases. More importantly, by analyzing the effective reproductive number, our method can reveal the risk of the second wave of the epidemic in Europe and justify the importance of social distancing to control the outbreak of the epidemic.