The impact of spatio-temporal travel distance on epidemics using an interpretable attention-based sequence-to-sequence model

TL;DR

This paper introduces an interpretable attention-based sequence-to-sequence model that predicts COVID-19 cases and deaths while analyzing how different travel distances influence epidemic spread across the US.

Contribution

The study develops S2SEA-Net, an innovative model integrating attention mechanisms to assess travel distance impacts on epidemics, providing both forecasts and interpretability.

Findings

Travel distance significantly affects COVID-19 spread patterns.

Distinct spatial patterns linked to different travel distance categories.

Geographical variations influence the impact of population movement on epidemics.

Abstract

Amidst the COVID-19 pandemic, travel restrictions have emerged as crucial interventions for mitigating the spread of the virus. In this study, we enhance the predictive capabilities of our model, Sequence-to-Sequence Epidemic Attention Network (S2SEA-Net), by incorporating an attention module, allowing us to assess the impact of distinct classes of travel distances on epidemic dynamics. Furthermore, our model provides forecasts for new confirmed cases and deaths. To achieve this, we leverage daily data on population movement across various travel distance categories, coupled with county-level epidemic data in the United States. Our findings illuminate a compelling relationship between the volume of travelers at different distance ranges and the trajectories of COVID-19. Notably, a discernible spatial pattern emerges with respect to these travel distance categories on a national scale. We unveil the geographical variations in the influence of population movement at different travel distances on the dynamics of epidemic spread. This will contribute to the formulation of strategies for future epidemic prevention and public health policies.