COVID-19 prediction with doubly multi-task Gaussian Process

This paper addresses a real-world multi-task prediction problem with time-series characteristics by proposing a novel Doubly Multi-Task Gaussian Process (DMTGP) model. Motivated by strong correlations between the number of confirmed cases and deaths, as well as between cases across the different countries, the model incorporates task-wise correlations to predict the number of COVID-19 patients, considering both task-specific (individual) and cross-task (shared) information to enhance overall performance. We constructed a database for three East Asian countries — Japan, South Korea, and Taiwan — and aim to simultaneously predict the number of confirmed cases and deaths in each country. To model the interactions among these countries, we employed a Transformer encoder layer to calculate cross-attention scores. Qualitative analysis of the attention score map demonstrates that our framework effectively captures the dynamic relationships between multiple nations over time. Our experimental results show that the DMTGP model outperforms other baseline models in handling doubly multiple tasks.