Evidence map›Paper›PMID 36068625›Full record

ArticleInfectious diseases of poverty2022

Optimization of COVID-19 prevention and control measures during the Beijing 2022 Winter Olympics: a model-based study.

Lingcai Kong, Mengwei Duan, Jin Shi, Jie Hong, Xuan Zhou, Xinyi Yang, Zheng Zhao, Jiaqi Huang, Xi Chen, Yun Yin and 9 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Infectious diseases of poverty, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.6field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 12 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors at 3 institutions in 1 country.

Lingcai KongDepartment of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.
Mengwei DuanDepartment of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.
Jin ShiDepartment of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China.
Jie HongDepartment of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China.
Xuan ZhouDepartment of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.
Xinyi YangDepartment of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.
Zheng ZhaoDepartment of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China.
Jiaqi HuangDepartment of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China.
Xi ChenDepartment of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China.
Yun YinDepartment of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China.
Ke LiDepartment of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China.
Yuanhua LiuDepartment of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China.
Jinggang LiuDepartment of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.
Xiaozhe WangDepartment of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.
Po ZhangDepartment of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.
Xiyang XieDepartment of Mathematics and Physics, North China Electric Power University, Baoding, 071003, China.
Fei LiDepartment of Power Engineering, North China Electric Power University, Baoding, 071003, China.
Zhaorui ChangDivision of Infectious Disease, Key Laboratory of Surveillance and Early-Warning On Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing, China. changzr@chinacdc.cn.
Zhijie ZhangDepartment of Epidemiology and Health Statistics, Fudan University, Shanghai, 200032, China. epistat@gmail.com.
Fudan University · CNNorth China Electric Power University · CNChinese Center For Disease Control and Prevention · CN

Funding

13th Five-Year National Science and Technology Major Project for Infectious Diseases 2018ZX10725-509Fundamental Research Funds for the Central Universities No. 2021MS117Key projects of the PLA logistics Scientific research Program BHJ17J013Major Project of Scientific and Technical Winter Olympics from National Key Research and Development Program of China 2021YFF0306000National Natural Science Foundation of China 81773487National Natural Science Foundation of China 81973102Public Health Talents Training Program of Shanghai Municipality GWV-10.2-XD21Shanghai New Three-year Action Plan for Public Health GWV-10.1-XK16the Natural Science Funds of Hebei Grant No. D2022502001
6 · The paper itself

Abstract

backgroundThe continuous mutation of severe acute respiratory syndrome coronavirus 2 has made the coronavirus disease 2019 (COVID-19) pandemic complicated to predict and posed a severe challenge to the Beijing 2022 Winter Olympics and Winter Paralympics held in February and March 2022.

methodsDuring the preparations for the Beijing 2022 Winter Olympics, we established a dynamic model with pulse detection and isolation effect to evaluate the effect of epidemic prevention and control measures such as entry policies, contact reduction, nucleic acid testing, tracking, isolation, and health monitoring in a closed-loop management environment, by simulating the transmission dynamics in assumed scenarios. We also compared the importance of each parameter in the combination of intervention measures through sensitivity analysis.

resultsAt the assumed baseline levels, the peak of the epidemic reached on the 57th day. During the simulation period (100 days), 13,382 people infected COVID-19. The mean and peak values of hospitalized cases were 2650 and 6746, respectively. The simulation and sensitivity analysis showed that: (1) the most important measures to stop COVID-19 transmission during the event were daily nucleic acid testing, reducing contact among people, and daily health monitoring, with cumulative infections at 0.04%, 0.14%, and 14.92% of baseline levels, respectively (2) strictly implementing the entry policy and reducing the number of cases entering the closed-loop system could delay the peak of the epidemic by 9 days and provide time for medical resources to be mobilized; (3) the risk of environmental transmission was low.

conclusionsComprehensive measures under certain scenarios such as reducing contact, nucleic acid testing, health monitoring, and timely tracking and isolation could effectively prevent virus transmission. Our research results provided an important reference for formulating prevention and control measures during the Winter Olympics, and no epidemic spread in the closed-loop during the games indirectly proved the rationality of our research results.

Indexed as

COVID-19Nucleic AcidsBeijingHumansPandemicsSARS-CoV-2Nucleic AcidsCOVID-19Dynamic modelPrevention and control measureThe Beijing 2022 Winter Olympics

Identifiers

PMID36068625
PMCPMC9447360
OpenAlexW4294770906

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.