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比利时的 SARS-CoV-2 传播与流动性。

Mobility and the spatial spread of sars-cov-2 in Belgium.

机构信息

KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Ghent, Belgium.

KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Ghent, Belgium; Division of Computational Science and Technology, KTH Royal Institute of Technology, Tomtebodavägen 23A, Solna, 17165, Sweden.

出版信息

Math Biosci. 2023 Jun;360:108957. doi: 10.1016/j.mbs.2022.108957. Epub 2023 Feb 17.

DOI:10.1016/j.mbs.2022.108957
PMID:36804448
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9934928/
Abstract

We analyse and mutually compare time series of covid-19-related data and mobility data across Belgium's 43 arrondissements (NUTS 3). In this way, we reach three conclusions. First, we could detect a decrease in mobility during high-incidence stages of the pandemic. This is expressed as a sizeable change in the average amount of time spent outside one's home arrondissement, investigated over five distinct periods, and in more detail using an inter-arrondissement "connectivity index" (CI). Second, we analyse spatio-temporal covid-19-related hospitalisation time series, after smoothing them using a generalise additive mixed model (GAMM). We confirm that some arrondissements are ahead of others and morphologically dissimilar to others, in terms of epidemiological progression. The tools used to quantify this are time-lagged cross-correlation (TLCC) and dynamic time warping (DTW), respectively. Third, we demonstrate that an arrondissement's CI with one of the three identified first-outbreak arrondissements is correlated to a substantial local excess mortality some five to six weeks after the first outbreak. More generally, we couple results leading to the first and second conclusion, in order to demonstrate an overall correlation between CI values on the one hand, and TLCC and DTW values on the other. We conclude that there is a strong correlation between physical movement of people and viral spread in the early stage of the sars-cov-2 epidemic in Belgium, though its strength weakens as the virus spreads.

摘要

我们分析并相互比较了比利时 43 个区(NUTS3)的新冠相关数据和流动性数据的时间序列。通过这种方式,我们得出了三个结论。首先,我们可以检测到在大流行高发阶段流动性的下降。这表现为在五个不同时期内,一个人离开家所在区的时间平均数量发生了相当大的变化,并用跨区“连通性指数”(CI)更详细地进行了调查。其次,我们分析了经过广义加性混合模型(GAMM)平滑后的时空新冠相关住院时间序列。我们确认,在流行病学进展方面,一些区比其他区更早,而且形态上与其他区不同。分别使用时间滞后交叉相关(TLCC)和动态时间规整(DTW)来量化这些。第三,我们表明,一个区的 CI 与三个最早爆发的区之一的 CI 相关,在第一个爆发后大约五到六周,会出现大量当地超额死亡率。更一般地说,我们将导致第一和第二结论的结果结合起来,以证明 CI 值与 TLCC 和 DTW 值之间存在总体相关性。我们的结论是,在比利时的 SARS-CoV-2 疫情早期,人们的身体活动与病毒传播之间存在很强的相关性,尽管随着病毒的传播,这种相关性会减弱。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9684/9934928/d3aa9a525ee3/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9684/9934928/7f7e610c9289/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9684/9934928/b1ee7ace5a59/gr4_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9684/9934928/964b6d554e70/gr5_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9684/9934928/2c28fc3380d2/gr6_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9684/9934928/defff2c87ca2/gr7_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9684/9934928/dfd01d8f2a43/gr8_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9684/9934928/30248e989e62/gr9_lrg.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9684/9934928/51a516b55e4c/gr11_lrg.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9684/9934928/d90474758df3/gr15_lrg.jpg

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