HierST: A Unified Hierarchical Spatial-temporal Framework for COVID-19 Trend Forecasting

The outbreak of the COVID-19 pandemic has largely influenced the world and our normal daily lives. To combat this pandemic efficiently, governments usually need to coordinate essential resources across multiple regions and adjust intervention polices at the right time, which all call for accurate and robust forecasting of future epidemic trends. However, designing such a forecasting system is non-trivial, since we need to handle all kinds of locations at different administrative levels, which include pretty different epidemic-evolving patterns. Moreover, there are dynamic and volatile correlations of pandemic conditions among these locations, which further enlarge the difficulty in forecasting. With these challenges in mind, we develop a novel spatial-temporal forecasting framework. First, to accommodate all kinds of locations at different administrative levels, we propose a unified hierarchical view, which mimics the aggregation procedure of pandemic statistics. Then, this view motivates us to facilitate joint learning across administrative levels and inspires us to design the cross-level consistency loss as an extra regularization to stabilize model training. Besides, to capture those dynamic and volatile spatial correlations, we design a customized spatial module with adaptive edge gates, which can both reinforce effective messages and disable irrelevant ones. We put this framework into production to help the battle against COVID-19 in the United States. A comprehensive online evaluation across three months demonstrates that our projections are the most competitive ones among all results produced by dozens of international group and even surpass the official ensemble in many cases. We also visualize our unique edge gates to understand the evolvement of spatial correlations and present intuitive case studies. Besides, we open source our implementation at https://github.com/dolphin-zs/HierST to facilitate future research towards better epidemic modeling.