Background: Travel restrictions as a means of intervention in the COVID-19 epidemic have reduced the spread of outbreaks using epidemiological models. We introduce the attention module in the sequencing model to assess the effects of the different classes of travel distances. Objective: To establish a direct relationship between the number of travelers for various travel distances and the COVID-19 trajectories. To improve the prediction performance of sequencing model. Setting: Counties from all over the United States. Participants: New confirmed cases and deaths have been reported in 3158 counties across the United States. Measurements: Outcomes included new confirmed cases and deaths in the 30 days preceding November 13, 2021. The daily number of trips taken by the population for various classes of travel distances and the geographical information of infected counties are assessed. Results: There is a spatial pattern of various classes of travel distances across the country. The varying geographical effects of the number of people travelling for different distances on the epidemic spread are demonstrated. Limitation: We examined data up to November 13, 2021, and the weights of each class of travel distances may change accordingly as the data evolves. Conclusion: Given the weights of people taking trips for various classes of travel distances, the epidemics could be mitigated by reducing the corresponding class of travellers.
翻译:利用流行病学模型,旅行限制作为干预COVID-19流行病的一种手段,减少了疾病爆发的蔓延。我们在测序模型中引入了关注模块,以评估不同类别旅行距离的影响。目标:建立不同旅行距离旅行者人数与COVID-19轨迹之间的直接关系,改进测序模型的预测性能。设置:全美国各地的县。参与者:全美国3158个县报告了新的经证实的病例和死亡。衡量:结果包括2021年11月13日前30天以前新确认的病例和死亡。对人口为不同类别旅行每天旅行的次数和受感染县的地理信息进行了评估。结果:全国各地不同旅行距离的空间分布模式不同。不同距离旅行人数的地理影响不同。限制:我们审查了截至2021年11月13日的数据,每类旅行距离的重量可能随着数据的变化而相应变化。结论:根据不同类别旅行旅行的人相应减少旅行的距离。