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西班牙疫情传播网络:一种用于刻画疾病传播途径的移动性模型。

Epidemic Diffusion Network of Spain: A Mobility Model to Characterize the Transmission Routes of Disease.

机构信息

Departamento de Medicina Preventiva y Salud Pública y Microbiología, Facultad de Medicina, Universidad Autónoma de Madrid. C. Arzobispo Morcillo 4, 28029 Madrid, Spain.

Centro Nacional de Epidemiología, Instituto de Salud Carlos IIII, Calle de Melchor Fernández Almagro 5, 28029 Madrid, Spain.

出版信息

Int J Environ Res Public Health. 2023 Feb 28;20(5):4356. doi: 10.3390/ijerph20054356.

DOI:10.3390/ijerph20054356
PMID:36901366
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10001675/
Abstract

Human mobility drives the geographical diffusion of infectious diseases at different scales, but few studies focus on mobility itself. Using publicly available data from Spain, we define a Mobility Matrix that captures constant flows between provinces by using a distance-like measure of effective distance to build a network model with the 52 provinces and 135 relevant edges. Madrid, Valladolid and Araba/Álaba are the most relevant nodes in terms of degree and strength. The shortest routes (most likely path between two points) between all provinces are calculated. A total of 7 mobility communities were found with a modularity of 63%, and a relationship was established with a cumulative incidence of COVID-19 in 14 days (CI14) during the study period. In conclusion, mobility patterns in Spain are governed by a small number of high-flow connections that remain constant in time and seem unaffected by seasonality or restrictions. Most of the travels happen within communities that do not completely represent political borders, and a wave-like spreading pattern with occasional long-distance jumps (small-world properties) can be identified. This information can be incorporated into preparedness and response plans targeting locations that are at risk of contagion preventively, underscoring the importance of coordination between administrations when addressing health emergencies.

摘要

人类流动在不同尺度上驱动着传染病的地理传播,但很少有研究关注流动本身。我们使用西班牙公开提供的数据,定义了一个流动矩阵,该矩阵通过使用类似于距离的有效距离度量来捕获省份之间的恒定流动,从而构建了一个包含 52 个省份和 135 个相关边的网络模型。马德里、巴利亚多利德和阿拉瓦/阿利瓦是度数和强度方面最重要的节点。计算了所有省份之间最短的路线(两点之间最可能的路径)。总共发现了 7 个流动社区,模块化程度为 63%,并与研究期间 COVID-19 的 14 天累积发病率(CI14)建立了关系。总之,西班牙的流动模式受少数高流量连接的控制,这些连接在时间上保持不变,似乎不受季节性或限制的影响。大多数旅行发生在社区内,这些社区不完全代表政治边界,可以识别出具有偶发性远距离跳跃(小世界特性)的波浪状传播模式。这些信息可以纳入针对预防性传染病风险地点的准备和应对计划,强调在应对卫生紧急情况时,各行政部门之间协调的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/904e/10001675/de25bc5664c3/ijerph-20-04356-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/904e/10001675/6d6fb2e6eb4b/ijerph-20-04356-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/904e/10001675/e8e86442d7b1/ijerph-20-04356-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/904e/10001675/b7be670578ff/ijerph-20-04356-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/904e/10001675/b03d5b19f9b1/ijerph-20-04356-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/904e/10001675/248cdab446c2/ijerph-20-04356-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/904e/10001675/5a38d78b667b/ijerph-20-04356-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/904e/10001675/de25bc5664c3/ijerph-20-04356-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/904e/10001675/6d6fb2e6eb4b/ijerph-20-04356-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/904e/10001675/e8e86442d7b1/ijerph-20-04356-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/904e/10001675/b7be670578ff/ijerph-20-04356-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/904e/10001675/b03d5b19f9b1/ijerph-20-04356-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/904e/10001675/248cdab446c2/ijerph-20-04356-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/904e/10001675/5a38d78b667b/ijerph-20-04356-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/904e/10001675/de25bc5664c3/ijerph-20-04356-g007.jpg

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Interplay between mobility, multi-seeding and lockdowns shapes COVID-19 local impact.流动性、多点传播和封锁措施之间的相互作用塑造了 COVID-19 的局部影响。
PLoS Comput Biol. 2021 Oct 14;17(10):e1009326. doi: 10.1371/journal.pcbi.1009326. eCollection 2021 Oct.
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Estimation of Human Mobility Patterns for Forecasting the Early Spread of Disease.用于预测疾病早期传播的人类流动模式估计
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