Klein Brennan, Hartle Harrison, Shrestha Munik, Zenteno Ana Cecilia, Barros Sierra Cordera David, Nicolás-Carlock José R, Bento Ana I, Althouse Benjamin M, Gutierrez Bernardo, Escalera-Zamudio Marina, Reyes-Sandoval Arturo, Pybus Oliver G, Vespignani Alessandro, Díaz-Quiñonez José Alberto, Scarpino Samuel V, Kraemer Moritz U G
Network Science Institute, Northeastern University, Boston, MA 02115, USA.
Laboratory for the Modeling of Biological & Socio-technical Systems, Northeastern University, Boston, MA 02115, USA.
PNAS Nexus. 2024 Jul 31;3(9):pgae306. doi: 10.1093/pnasnexus/pgae306. eCollection 2024 Sep.
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 nonpharmaceutical 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 behavioral 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 exports of COVID-19 cases 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 synchronized. Our results provide dynamic insights into how to use network science and epidemiological modeling to inform the spatial scale at which interventions are most impactful in mitigating the spread of COVID-19 and infectious diseases in general.
在新发传染病暴发期间，与国际相连的城市往往会较早出现大规模疫情，而农村地区则会在一段时间后才出现。疾病传播的这种层级结构主要受人类流动的多尺度结构影响。然而，在新冠疫情期间，公共卫生应对措施在设计非药物干预措施（NPIs）时通常没有考虑人类流动的明确空间结构。NPIs主要在国家或地区尺度上实施。在此，我们使用墨西哥市级层面每周匿名汇总的人类流动数据以及关于新冠病例的空间分辨率高的数据，来研究在疫情第一波（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由墨西哥州和墨西哥城的新冠病例输出驱动，早期疫情就发生在这些地方。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，而这些社区的疫情却日益同步。我们的研究结果为如何利用网络科学和流行病学模型来确定干预措施在减轻新冠疫情及一般传染病传播方面最具影响力的空间尺度提供了动态见解。
