2022年北京冬奥会和冬残奥会闭环管理新型冠状病毒肺炎疫情防控效果评价

王雪纯; 闫翔宇; 裴少君; 贾忠伟

doi:10.3784/jbjc.202208060347

2022年北京冬奥会和冬残奥会闭环管理新型冠状病毒肺炎疫情防控效果评价

doi: 10.3784/jbjc.202208060347

王雪纯^1,,
闫翔宇¹,
裴少君²,
贾忠伟^{2, 3, 4, ,}

1.
北京大学公共卫生学院流行病与卫生统计学系, 北京 100191
2.
北京大学公共卫生学院全球卫生学系, 北京 100191
3.
北京大学人工智能研究院智慧健康研究中心, 北京 100191
4.
北京大学健康医疗大数据国家研究院药物滥用智能防控中心, 北京 100191

基金项目: 国家自然科学基金（No. 72174004）

详细信息

作者简介:
王雪纯，女，山东省青岛市人，硕士，主要从事传染病预防与控制研究，Email：2011210137@stu.pku.edu.cn

通讯作者:
贾忠伟，Tel：010−82802457，Email：urchinjj@163.com

中图分类号: R211; R184
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出版历程
- 收稿日期: 2022-08-06
- 网络出版日期: 2022-12-16
- 刊出日期: 2022-12-01

Evaluation of effectiveness of COVID-19 prevention and control in a closed-loop management manner during Beijing 2022 Winter Olympics and Paralympics Games

Wang Xuechun^1
,,
Yan Xiangyu¹,
Pei Shaojun²,
Jia Zhongwei^{2, 3, 4
, ,}

1.
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
2.
Department of Global Health, School of Public Health, Peking University, Beijing 100191, China
3.
Center for Intelligent Public Health, Institute for Artificial Intelligence, Peking University, Beijing 100191, China
4.
Center for Drug Abuse Control and Prevention, National Institute of Health Data Science, Peking University, Beijing 100191, China

Funds: This study was supported by the National Natural Science Foundation of China (No. 72174004)

More Information

Corresponding author: Jia Zhongwei, Email: urchinjj@163.com

摘要

摘要: 目的定量评估北京冬奥会和冬残奥会（北京冬残奥会）闭环管理对新冠疫情控制的效果。方法收集2022年1月4日至3月13日的北京冬残奥会新型冠状病毒肺炎防疫报告数据（机场入境人数、核酸检测等信息），利用群体传播动力学模型模拟非闭环（常态化）管理每日新增感染人数、每日检出率、累计检出率，与实际数据比较分析，评估闭环管理防控效果。结果模型结果提示，如果采取常态化管理，北京冬残奥会期间每日新增感染人数于2月12日达到峰值（120人），较实际峰值推迟了10 d且增加了94人；相比于常态化管理，闭环管理可避免95.24%的感染者（3 763 比 179）；常态化管理累计检出率为闭环管理21.02倍（0.15% 比 0.07‰）。此期间，实际机场入境运动员和随队官员与其他利益相关方累计检出率相近（1.68% 比 1.76%）；进入闭环内，两类人群的累计检出率均下降（0.49‰ 比 0.05‰），其中运动员和随队官员的每日检出率远高于其他利益相关方（最高每日检出率：3.15‰ 比0.22‰）。结论北京冬残奥会闭环管理能早期发现感染者，有效降低感染人数，为未来大型体育赛事疫情防控策略制定提供借鉴。
- 北京冬残奥会 /
- 闭环管理 /
- 传播动力学模型 /
- 效果评价
Abstract: Objective To quantitatively evaluate the effectiveness of closed-loop management in the control of COVID-19 during Beijing Winter Olympics and Paralympics Games in 2022. Methods The information about COVID-19 prevention and control during Beijing Winter Olympics and Paralympics Games from January 4 to March 13, 2022, such as the airport arrivals and confirmed positive cases, were collected. According to the non-closed-loop management (regular epidemic prevention and control measure), a population transmission dynamic model was built to simulate daily new infections, daily detection rate and cumulative detection rate, and the results were compared with the actual data to evaluate the effectiveness of the closed-loop management. Results The results of the model suggested that if regular management were adopted, the daily number of new infections during Beijing Winter Olympics and Paralympic would reach the peak on February 12 (120), 10 days later than the actual peak and an increase of 94 cases. Compared with regular management, closed-loop management might prevent 95.24% of infections (3,763 vs. 179); The cumulative detection rate of regular management was 21.02 times higher than that of closed-loop management (0.15% vs. 0.07‰). During this period, the cumulative detection rates of actual airport inbound athletes, team officials and other stakeholders were similar (1.68% vs. 1.76%). In the closed loop, the cumulative detection rate of both groups decreased (0.49‰ vs. 0.05‰), and the daily detection rate of athletes and team officials was much higher than that of other stakeholders (the highest daily detection rate: 3.15‰ vs. 0.22‰). Conclusion The closed-loop management during Beijing Winter Olympics and Paralympics Games could detect the infections early and effectively reduce the number of infections, providing a reference for developing epidemic prevention and control strategies for future large-scale sports events.
- Beijing Winter Olympics and Paralympics Games /
- Closed-loop management /
- Transmission dynamic model /
- Effect evaluation

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图 1 常态化管理的新冠肺炎传播动力学模型示意图

注：N=S+S_c+S_q+E+E_c+E_q+I+I_c+I_q

Figure 1. COVID-19 transmission dynamic model for regular management

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图 2 北京冬残奥会期间机场入境人数及累计检出率

Figure 2. Arrivals and cumulative detection rate at airport during Beijing Winter Olympic and Paralympic Games

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图 3 北京冬残奥会闭环内核酸检测情况

Figure 3. Nucleic acid detection in closed-loop during Beijing Winter Olympic and Paralympic Games

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图 4 北京冬残奥会闭环内实际与模拟新冠肺炎传播情况

Figure 4. Actual and simulated transmission of COVID-19 in closed loop during Beijing Winter Olympics and Paralympic Games

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表 1 闭环管理与常态化管理政策与措施的比较

Table 1. Comparison of closed-loop management and regular management policies and measures

措施	闭环管理	常态化管理
入境政策	完成疫苗接种核酸阴性者直接进入闭环管理，未接种疫苗核酸阴性者完成21 d集中隔离后进入闭环	核酸阴性者进行7 d集中隔离＋ 3 d居家健康监测^［8］
症状监测	24 h症状监测，可及时发现进行管理	以个人主动就医或报告为主
核酸检测	每天1次	每3天1次^［8］，密切接触者隔离监测期间（“7＋3”）进行6次核酸检测^［9］
个人防护	严格执行佩戴口罩、手卫生、隔座就餐、分餐等要求	自觉遵守佩戴口罩、手卫生等，建议隔座就餐、分餐
活动范围	闭环内	北京市内

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表 2 传播动力学模型相关参数及取值

Table 2. Transmissive dynamic model related parameters and values

参数	定义	取值
$ \alpha $	密接管理程度：感染者的密切接触者中被发现者占所有密切接触者的比例。	0.90
q	密接发现速率：感染者的密切接触者被发现并隔离所需时间（以天为单位）的倒数。	1/1.50
$ {\beta }_{0} $	接触传播率：与感染者每次接触的传播概率	0.17^{［11–12］}
c	每日人均接触人数：感染者的密切接触者人数	14.50^［13］
$ \sigma $	未发现感染者到发现感染者的转移速率	1/2.90^［1］
$ \lambda $	隔离速率	1/10^［9］
$ {P}_{NAT} $	隔离期后依旧不能被发现的概率	0.13^［14］
$ {\lambda }_{1} $	核酸检测速率：两次核酸检测间隔时间（以天为单位）的倒数。	1/3
n	核酸检测准确度	0.92^［15］
$ \delta $	一般状态与密接状态中被发现的感染者住院隔离时间（以天为单位）的倒数。	1/2
$ {\delta }_{q} $	隔离状态中被发现的感染者住院隔离时间（以天为单位）的倒数。	1

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表 3 传播动力学模型的单因素敏感性分析结果

Table 3. Results of univariate sensitivity analysis of transmission dynamic model

参数	取值范围	最大每日新增感染人数（例）	到达峰值时间（d）	累计感染人数（例）	累计检出率（%）
$ {\beta }_{0} $	0.13～0.21^a	58～339	16～29	1 340～9 101	0.05～0.36
$ \sigma $	1/3.63～1/2.20^a	96～169	21～22	3 071～5 126	0.12～0.20
c	10.88～18.13^a	58～308	16～29	1 371～8 058	0.05～0.32
$ {\lambda }_{1} $	1/1～1/5	42～192	15～29	811～5 932	0.03～0.23
注：a. 保持其他影响因素不变，基于初始值上下变化25%作为取值范围并进行敏感性分析

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