Forecasting COVID-19 Dynamics: Clustering, Generalized Spatiotemporal Attention, and Impacts of Mobility and Geographic Proximity

Abstract

Forecasting the dynamics of COVID-19 enables government agencies and public health administrators to take proactive measures to combat the pandemic. This forecasting task faces several key challenges: First, the dynamics of COVID-19 exhibit complex spatial and temporal dependencies. The current growing trend at a location may be similar to that at another location in the past. Second, numerous factors, such as population mobility and geographic proximity between regions, mask usage, vaccine coverage, etc., significantly impact the dynamics. Third, we need to find the appropriate granularity for the forecasting task. The granularity should not be too coarse that we ignore the idiosyncrasies of individual regions. Still, the granularity should not be too fine that the prediction results are seriously vulnerable to noise.This paper addresses these challenges. We propose a simple but effective clustering algorithm that finds the appropriate granularity for the forecasting task. We invent generalized spatiotemporal attention, an attention mechanism that is generalized enough to capture the complex spatial and temporal dependencies and to flexibly account for intra- and inter-region characteristics such as geographic proximity and population mobility. Based on this generalized spatiotemporal attention, we designed COVID-Forecaster, a lightweight deep learning model for forecasting the dynamics of COVID-19. Experimental results demonstrate that COVID-Forecaster significantly outperforms state-of-the-art models. For example, COVID-Forecaster reduces the mean absolute percentage error (MAPE) by 6.8% and the weighted absolute percentage error (WAPE) by 13.5% in forecasting the COVID-19 dynamics at the 3141 counties of the United States.