论文标题
时空旅行距离使用可解释的基于注意的序列模型的影响对流行病的影响
The impact of spatio-temporal travel distance on epidemics using an interpretable attention-based sequence-to-sequence model
论文作者
论文摘要
在共同199大流行中，出现了旅行限制，这是减轻病毒扩散的关键干预措施。在这项研究中，我们通过合并注意力模块，增强了模型的预测能力，序列到序列流行病注意力网络（S2SEA-NET），从而使我们能够评估不同类别的旅行距离对流行动力学的影响。此外，我们的模型为新的已确认案件和死亡提供了预测。为了实现这一目标，我们利用各种旅行距离类别的人口运动的每日数据，再加上美国县级流行数据。我们的发现阐明了不同距离范围的旅行者的体积与Covid-19的轨迹之间的引人注目的关系。值得注意的是，针对这些旅行距离类别在全国范围内出现了可辨别的空间模式。我们揭示了不同行进距离在流行病动力学动力学方面的人口运动影响的地理变化。这将有助于制定未来流行病和公共卫生政策的战略。
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.
