  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.
