An inner-inter city graph neural network for predicting the course of COVID-19 cases

Abstract

Highly infectious diseases like COVID-19 have affected millions worldwide, with transmission clearly linked to crowd mobility. However, research on the predictive power of city-level mobility remains limited. Most existing forecasting methods focus on state or national level, making it difficult for policymakers to quickly respond to emerging threats. To address this gap, we propose the Inner-Inter City Learner (IIL) for short- and mid-term COVID-19 case predictions at the city level, based on the correlation between human interaction and new cases. IIL consists of two key components: an inter-city transmission learner and an inner-city propagation learner. The first uses city mobility graphs and graph neural networks to learn how transmission in one city is influenced by others. The second, leveraging the highly contagious nature of the virus, captures key features of COVID-19 spread within cities. To overcome limited inner-city mobility data, we apply model-agnostic meta-learning to transfer common features across cities. We conduct various experiments and compare our methods with the state-of-art baselines. The results show the superiority of our method across various forecast horizons.