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严重急性呼吸综合征冠状病毒2(SARS-CoV-2)在不同暴露环境下人群传播模式的变异性及其在推动疫情动态中的作用。

Variability in the Population Diffusion Patterns of SARS-CoV-2 by Exposure Setting and Its Roles in Driving Epidemic Dynamics.

作者信息

Chan Chin Pok, Wong Ngai Sze, Kwan Tsz Ho, Yeoh Eng Kiong, Lee Shui Shan

机构信息

S.H. Ho Research Centre for Infectious Diseases, The Chinese University of Hong Kong, Hong Kong, China.

Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Hong Kong, China.

出版信息

Influenza Other Respir Viruses. 2025 Jun;19(6):e70125. doi: 10.1111/irv.70125.

Abstract

BACKGROUND

Identifying transmission events that trigger epidemic spread is paramount for informing outbreak control. This study characterised the population diffusion patterns of SARS-CoV-2 across exposure settings and evaluate their ramifications in epidemic growth.

METHODS

In Hong Kong, COVID-19 clusters delineated through case-based surveillance during the pandemic period were classified into eight exposure settings: residence, home gathering, neighbourhood, workplace (office)/school, workplace (non-office), daily activity, social activity and healthcare. Diffusion patterns characterised by outbreak size, speed and likelihood of spillover (cases seeding a new cluster) were compared among settings. With different clusters emerging, the lagged effect on effective reproduction number (R) was evaluated.

RESULTS

Between January 2020 and January 2022, some 2800 clusters involving 14,202 cases were identified over five epidemic waves precipitated by outbreaks occurring in daily activity (wave I/III), social activity (wave II/IV) and neighbourhood (wave V-Omicron). Adjusted for variations by epidemic wave, the largest and fastest spread was observed in neighbourhood, averaging a size of 11.9 and daily generation of 1.18 cases per cluster. Spillover was the most common for social activity clusters with each of which normally breeding 3.73 onward clusters, compared to 0.18 for residential clusters. A cluster emerging in neighbourhood, social activity and daily activity was estimated to raise the R by 0.021-0.025, 0.013-0.024 and 0.008-0.015, respectively, on the ensuing 7 days.

CONCLUSIONS

Neighbourhood and social activity outbreaks were inclined to induce epidemic spread, warranting the need for prioritised mitigation and targeted implementation of precautionary measures during both epidemics and peak season of respiratory infection.

摘要

背景

识别引发疫情传播的传播事件对于指导疫情防控至关重要。本研究描述了严重急性呼吸综合征冠状病毒2(SARS-CoV-2)在不同暴露场景下的人群传播模式,并评估其对疫情增长的影响。

方法

在香港,通过疫情期间基于病例的监测确定的2019冠状病毒病(COVID-19)聚集性疫情被分为八个暴露场景:居住场所、家庭聚会、邻里、工作场所(办公室)/学校、工作场所(非办公室)、日常活动、社交活动和医疗保健。比较了不同场景下以疫情规模、传播速度和溢出可能性(引发新聚集性疫情的病例)为特征的传播模式。随着不同聚集性疫情的出现,评估了其对有效繁殖数(R)的滞后影响。

结果

在2020年1月至2022年1月期间,在由日常活动(第一波/第三波)、社交活动(第二波/第四波)和邻里(第五波-奥密克戎)爆发引发的五波疫情中,共识别出约2800个聚集性疫情,涉及14202例病例。经疫情波次差异调整后,邻里场景中观察到的传播规模最大、速度最快,平均每个聚集性疫情规模为11.9例,每天新增1.18例。社交活动聚集性疫情的溢出最为常见,每个社交活动聚集性疫情通常引发3.73个后续聚集性疫情,而居住场所聚集性疫情为0.18个。估计在邻里、社交活动和日常活动中出现的一个聚集性疫情在随后7天内分别使R值提高0.021-0.025、0.013-0.024和0.008-0.015。

结论

邻里和社交活动引发的疫情倾向于导致疫情传播,因此在疫情期间和呼吸道感染高发季节都需要优先采取缓解措施并针对性地实施预防措施。

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