墨西哥 COVID-19 传播的空间尺度

Brennan Klein, Harrison Hartle, Munik Shrestha, Ana Cecilia Zenteno, David Barros Sierra Cordera, José R Nicolás-Carlock, Ana I Bento, Benjamin M Althouse, Bernardo Gutierrez, Marina Escalera-Zamudio, Arturo Reyes-Sandoval, Oliver G Pybus, Alessandro Vespignani, José Alberto Díaz Quiñonez, Samuel V Scarpino, Moritz U G Kraemer
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引用次数: 0

摘要

在新发传染病爆发期间,国际相连的城市往往会较早爆发大规模传染病,而农村地区则会稍晚爆发。疾病传播的这种分层结构主要受到人类流动的多尺度结构的影响。然而,在 COVID-19 流行期间,公共卫生应对措施在设计非药物干预措施(NPIs)时通常没有考虑到人类流动的明确空间结构。非药物干预措施主要应用于国家或地区范围。在此,我们利用每周匿名汇总的人员流动数据和墨西哥市级 COVID-19 病例的空间高分辨率数据,研究在第一波疫情(2020 年 3 月至 6 月)期间,应对疫情的行为变化如何改变了传播和干预的空间尺度。我们发现,墨西哥的疫情动态最初是由墨西哥州和墨西哥城的 SARS-CoV-2 出口驱动的,而墨西哥州和墨西哥城正是早期疫情爆发地。2020 年 3 月下旬实施干预措施后,流动网络发生了变化,流动网络社区变得更加分散,而这些社区的疫情变得越来越同步。我们的研究结果为如何利用网络科学和流行病学建模提供了动态见解,有助于确定在何种空间范围内采取干预措施对减缓 COVID-19 和一般传染病的传播影响最大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatial scales of COVID-19 transmission in Mexico
During outbreaks of emerging infectious diseases, internationally connected cities often experience large and early outbreaks, while rural regions follow after some delay. This hierarchical structure of disease spread is influenced primarily by the multiscale structure of human mobility. However, during the COVID-19 epidemic, public health responses typically did not take into consideration the explicit spatial structure of human mobility when designing non-pharmaceutical interventions (NPIs). NPIs were applied primarily at national or regional scales. Here we use weekly anonymized and aggregated human mobility data and spatially highly resolved data on COVID-19 cases at the municipality level in Mexico to investigate how behavioural changes in response to the pandemic have altered the spatial scales of transmission and interventions during its first wave (March - June 2020). We find that the epidemic dynamics in Mexico were initially driven by SARS-CoV-2 exports from Mexico State and Mexico City, where early outbreaks occurred. The mobility network shifted after the implementation of interventions in late March 2020, and the mobility network communities became more disjointed while epidemics in these communities became increasingly synchronised. Our results provide dynamic insights into how to use network science and epidemiological modelling to inform the spatial scale at which interventions are most impactful in mitigating the spread of COVID-19 and infectious diseases in general.
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