Linka Kevin, Rahman Proton, Goriely Alain, Kuhl Ellen
Department of Mechanical Engineering, Stanford University, Stanford, CA USA.
Department of Medicine, Memorial University of Newfoundland, St. John's, Canada.
Comput Mech. 2020;66(5):1081-1092. doi: 10.1007/s00466-020-01899-x. Epub 2020 Aug 29.
A key strategy to prevent a local outbreak during the COVID-19 pandemic is to restrict incoming travel. Once a region has successfully contained the disease, it becomes critical to decide when and how to reopen the borders. Here we explore the impact of border reopening for the example of Newfoundland and Labrador, a Canadian province that has enjoyed no new cases since late April, 2020. We combine a network epidemiology model with machine learning to infer parameters and predict the COVID-19 dynamics upon partial and total airport reopening, with perfect and imperfect quarantine conditions. Our study suggests that upon full reopening, every other day, a new COVID-19 case would enter the province. Under the current conditions, banning air travel from outside Canada is more efficient in managing the pandemic than fully reopening and quarantining 95% of the incoming population. Our study provides quantitative insights of the efficacy of travel restrictions and can inform political decision making in the controversy of reopening.
在新冠疫情期间,防止局部疫情爆发的一项关键策略是限制入境旅行。一旦某个地区成功控制住疫情,决定何时以及如何重新开放边境就变得至关重要。在此,我们以加拿大纽芬兰与拉布拉多省为例,探讨边境重新开放的影响。自2020年4月下旬以来,该省未出现新增病例。我们将网络流行病学模型与机器学习相结合,以推断参数,并预测在机场部分和完全重新开放且检疫条件完美和不完美的情况下新冠疫情的动态。我们的研究表明,全面重新开放后,每隔一天就会有一例新冠病例进入该省。在当前情况下,禁止来自加拿大境外的航空旅行在管理疫情方面比完全重新开放并对95%的入境人员进行检疫更为有效。我们的研究提供了关于旅行限制有效性的定量见解,并可为重新开放争议中的政治决策提供参考。