Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands.
Department of Population Health Sciences, Utrecht University, Utrecht, The Netherlands.
J R Soc Interface. 2022 Aug;19(193):20220486. doi: 10.1098/rsif.2022.0486. Epub 2022 Aug 31.
In this paper, we present a method to forecast the spread of SARS-CoV-2 across regions with a focus on the role of mobility. Mobility has previously been shown to play a significant role in the spread of the virus, particularly between regions. Here, we investigate under which epidemiological circumstances incorporating mobility into transmission models yields improvements in the accuracy of forecasting, where we take the situation in The Netherlands during and after the first wave of transmission in 2020 as a case study. We assess the quality of forecasting on the detailed level of municipalities, instead of on a nationwide level. To model transmissions, we use a simple mobility-enhanced SEIR compartmental model with subpopulations corresponding to the Dutch municipalities. We use commuter information to quantify mobility, and develop a method based on maximum likelihood estimation to determine the other relevant parameters. We show that taking inter-regional mobility into account generally leads to an improvement in forecast quality. However, at times when policies are in place that aim to reduce contacts or travel, this improvement is very small. By contrast, the improvement becomes larger when municipalities have a relatively large amount of incoming mobility compared with the number of inhabitants.
在本文中，我们提出了一种方法来预测 SARS-CoV-2 在各地区的传播情况，重点关注流动性的作用。流动性先前已被证明在病毒传播中起着重要作用，特别是在地区之间。在这里，我们研究了在何种流行病学情况下，将流动性纳入传播模型可以提高预测的准确性，我们以 2020 年第一波传播期间和之后荷兰的情况作为案例研究。我们在更详细的市县级层面上评估预测质量，而不是在全国范围内。为了对传播进行建模，我们使用了一个简单的带流动性增强的 SEIR 分区模型，其中包含与荷兰各城市对应的子群体。我们使用通勤信息来量化流动性，并开发了一种基于最大似然估计的方法来确定其他相关参数。我们表明，通常情况下，考虑到地区间的流动性会提高预测质量。然而，在采取旨在减少接触或旅行的政策时，这种改进非常小。相比之下，当与居民人数相比，流入的流动性较大时，改进就会变得更大。
