Predicting influenza with pandemic-awareness via Dynamic Virtual Graph Significance Networks

Every year, influenza spreads worldwide and burdens people's health substantially. We need a reliable model to help hospitals, pharmaceutical companies, and governments better prepare for influenza outbreaks in a timely manner. However, the domain knowledge for such public health events, such as the variable influenza seasonality and occasional pandemics, poses significant challenges in predicting influenza outbreaks. The existing methods use current and historical values in a user-defined time window as input to predict future values but lack considering the situations outside the window. To address these limitations, we proposed Dynamic Virtual Graph Significance Networks (DVGSN). The graph-based algorithm can supervisedly and dynamically learn the implied knowledge from similar "infection situations" in all the historical timepoints without the limitation of time window. Furthermore, representation learning on the dynamic virtual graph can tackle the varied seasonality with pandemic-awareness without requiring domain knowledge input. The extensive experiments on real-world influenza data demonstrate that DVGSN significantly outperforms the state-of-the-art methods. To the best of our knowledge, this is the first attempt to supervisedly learn a dynamic virtual graph for time-series prediction tasks. Moreover, the proposed method has rich interpretabilities, which makes the method more acceptable in the fields of public health, life sciences, and so on. Our source code and dataset are available at https://github.com/aI-area/DVGSN.