模拟 COVID-19 流动中断对华盛顿州西雅图 RSV 传播的影响

Atchuta Srinivas Duddu, Islam Elgamal, José Camacho-Mateu, Olena Holubowska, Simon A. Rella, Samantha J. Bents, Cécile Viboud, Chelsea L. Hansen, Giulia Pullano, Amanda C. Perofsky
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摘要

导言:呼吸道合胞病毒(RSV)感染是幼儿和老年人急性呼吸道住院治疗的主要原因。2020 年初,大多数国家实施了非药物干预措施 (NPI),以减缓 SARS-CoV-2 的传播。COVID-19 非药物干预措施在全球范围内阻断了 RSV 的传播,许多地方直到 2020 年底或 2021 年才再次出现广泛传播。在此,我们利用手机移动数据建立了一个机理传播模型,以确定人口行为的哪些方面对华盛顿州西雅图大流行后 RSV 的反弹影响最大:我们使用移动设备定位汇总数据来描述西雅图的市内混合、游客流入和景点人流量。我们将年龄结构流行病学模型与每周 RSV 住院治疗数据进行了拟合,考虑到了大流行期间流动性下降导致的传播减少。我们将模型拟合结果与观察到的数据进行了比较,以评估哪些流动行为最能反映西雅图大流行后头两次大流行期间 RSV 的动态:在西雅图,COVID-19 NPIs扰乱了2020年至2022年的RSV季节性。西雅图在 2021 年夏季爆发了一次小规模的非季节性疫情,在 2022 年秋季爆发了一次非典型的大规模早期疫情。RSV 传播模型包含了流动网络的连通性(以西雅图居民区之间的平均最短路径长度衡量)或西雅图以外的游客流入量,这些模型最好地捕捉到了疫情爆发后前两次疫潮的时间和规模。包括通往学校或儿童日托所的人流或街区内流动在内的模型与观测数据的拟合度较差:我们的研究结果表明,来自其他地区的病例输入和邻里间的本地传播对西雅图 RSV 复发的时间影响最大。这些发现有助于人们了解 RSV 流行传播的行为因素,并为采取免疫预防等预防措施的时机提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling the effects of COVID-19 mobility disruptions on RSV transmission in Seattle, Washington
Introduction: Respiratory Syncytial Virus (RSV) infection is a major cause of acute respiratory hospitalizations in young children and older adults. In early 2020 most countries implemented non-pharmaceutical interventions (NPIs) to slow the spread of SARS-CoV-2. COVID-19 NPIs disrupted the transmission of RSV on a global scale, and many locations did not experience widespread re-circulation until late 2020 or 2021. Here, we use a mechanistic transmission model informed by cellphone mobility data to determine which aspects of population behavior had the greatest influence on post-pandemic RSV rebound in Seattle, Washington. Methods: We used aggregated mobile device location data to characterize within-city mixing, visitor in-flows, and foot traffic to points of interest in Seattle. We fit an age-structured epidemiological model to data on weekly RSV hospitalizations, allowing for reductions in transmission due to declines in mobility during the pandemic. We compared model fits to observed data to assess which mobility behaviors best capture RSV dynamics during the first two post-pandemic waves in Seattle. Results: In Seattle, COVID-19 NPIs perturbed RSV seasonality from 2020 to 2022. Seattle experienced a small out-of-season outbreak in Summer 2021 and an atypically large and early wave in Fall 2022. RSV transmission models incorporating mobility network connectivity (measured as the average shortest path length between Seattle neighborhoods) or the inflow of visitors from outside of Seattle best captured the timing and magnitude of the first two post-pandemic waves. Models including foot traffic to schools or child daycares or within-neighborhood movement produced poor fits to observed data. Conclusions: Our results suggest that case importations from other regions and local spread between neighborhoods had the greatest influence on the timing of RSV reemergence in Seattle. These findings contribute to the understanding of behavioral factors underlying RSV epidemic spread and can inform the timing of preventative measures, such as the administration of immunoprophylaxis.
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