Evidence map›Paper›PMID 36642738›Full record

ArticleInfectious diseases of poverty2023

Optimizing the nucleic acid screening strategy to mitigate regional outbreaks of SARS-CoV-2 Omicron variant in China: a modeling study.

Yun Yin, Yuanhua Liu, Mengwei Duan, Xiyang Xie, Jie Hong, Jiaqi Huang, Ke Li, Jin Shi, Xi Chen, Hongyan Guo and 6 more

Open access · goldAbstract read
In one paragraph

Article in Infectious diseases of poverty, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.8field-weighted citation impact, top 32% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Review
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

16 authors at 3 institutions in 2 countries.

Yun Yin *Department of Epidemiology and Health Statistics, School of Public Health, Fudan University, No.130, Dong'An Road, Xuhui District, Shanghai, 200032, China.
Yuanhua Liu *Department of Epidemiology and Health Statistics, School of Public Health, Fudan University, No.130, Dong'An Road, Xuhui District, Shanghai, 200032, China.
Mengwei Duan *Department of Mathematics and Physics, North China Electric Power University, Baoding, China.
Xiyang XieDepartment of Mathematics and Physics, North China Electric Power University, Baoding, China.
Jie HongDepartment of Epidemiology and Health Statistics, School of Public Health, Fudan University, No.130, Dong'An Road, Xuhui District, Shanghai, 200032, China.
Jiaqi HuangDepartment of Epidemiology and Health Statistics, School of Public Health, Fudan University, No.130, Dong'An Road, Xuhui District, Shanghai, 200032, China.
Ke LiDepartment of Epidemiology and Health Statistics, School of Public Health, Fudan University, No.130, Dong'An Road, Xuhui District, Shanghai, 200032, China.
Jin ShiDepartment of Epidemiology and Health Statistics, School of Public Health, Fudan University, No.130, Dong'An Road, Xuhui District, Shanghai, 200032, China.
Xi ChenDepartment of Epidemiology and Health Statistics, School of Public Health, Fudan University, No.130, Dong'An Road, Xuhui District, Shanghai, 200032, China.
Hongyan GuoDepartment of Blood Transfusion, Changchun People's Hospital, Changchun, China.
Xuan ZhouDepartment of Mathematics and Physics, North China Electric Power University, Baoding, China.
Rui LiuDepartment of Geomatics and Spatial Information, Shandong University of Science and Technology, Qingdao, China.
Caifeng ZhouDepartment of Mathematics and Physics, North China Electric Power University, Baoding, China.
Xiaozhe WangDepartment of Mathematics and Physics, North China Electric Power University, Baoding, China.
Lingcai KongDepartment of Mathematics and Physics, North China Electric Power University, Baoding, China. konglc@ncepu.edu.cn.
Zhijie ZhangDepartment of Epidemiology and Health Statistics, School of Public Health, Fudan University, No.130, Dong'An Road, Xuhui District, Shanghai, 200032, China. epistat@gmail.com.ORCID http://orcid.org/0000-0002-1276-787X
Ministry of Education of the People's Republic of China · CNNorth China Electric Power University · CNShandong University of Science and Technology · CN

Funding

National Key Research and Development Program of China 2022YFC2602900National Natural Science Foundation of China 81973102Public Health Talents Training Program of Shanghai Municipality GWV-10.2-XD21the Fundamental Research Funds for the Central Universities 2021MS117the Fundamental Research Funds for the Central Universities nceputhe Major Project of Scientific and Technical Winter Olympics from National Key Research and Development Program of China 2021YFF0306000the Shanghai New Three-year Action Plan for Public Health GWV-10.1-XK16
6 · The paper itself

Abstract

backgroundThe Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spreads rapidly and insidiously. Coronavirus disease 2019 (COVID-19) screening is an important means of blocking community transmission in China, but the costs associated with testing are high. Quarantine capacity and medical resources are also threatened. Therefore, we aimed to evaluate different screening strategies to balance outbreak control and consumption of resources.

methodsA community network of 2000 people, considering the heterogeneities of household size and age structure, was generated to reflect real contact networks, and a stochastic individual-based dynamic model was used to simulate SARS-CoV-2 transmission and assess different whole-area nucleic acid screening strategies. We designed a total of 87 screening strategies with different sampling methods, frequencies of screening, and timings of screening. The performance of these strategies was comprehensively evaluated by comparing the cumulative infection rates, the number of tests, and the quarantine capacity and consumption of medical resource, which were expressed as medians (95% uncertainty intervals, 95% UIs).

resultsTo implement COVID-19 nucleic acid testing for all people (Full Screening), if the screening frequency was four times/week, the cumulative infection rate could be reduced to 13% (95% UI: 1%, 51%), the miss rate decreased to 2% (95% UI: 0%, 22%), and the quarantine and medical resource consumption was lower than higher-frequency Full Screening or sampling screening. When the frequency of Full Screening increased from five to seven times/week (which resulted in a 2581 increase in the number of tests per positive case), the cumulative infection rate was only reduced by 2%. Screening all people weekly by splitting them equally into seven batches could reduce infection rates by 73% compared to once per week, which was similar to Full Screening four times/week. Full Screening had the highest number of tests per positive case, while the miss rate, number of tests per positive case, and hotel quarantine resource consumption in Household-based Sampling Screening scenarios were lower than Random Sampling Screening. The cumulative infection rate of Household-based Sampling Screening or Random Sampling Screening seven times/week was similar to that of Full Screening four times/week.

conclusionsIf hotel quarantine, hospital and shelter hospital capacity are seriously insufficient, to stop the spread of the virus as early as possible, high-frequency Full Screening would be necessary, but intermediate testing frequency may be more cost-effective in non-extreme situations. Screening in batches is recommended if the testing capacity is low. Household-based Sampling Screening is potentially a promising strategy to implement.

Indexed as

COVID-19SARS-CoV-2COVID-19 TestingDisease OutbreaksHumansCOVID-19ModelingOmicron variantSARS-CoV-2Whole-area screening strategy

Identifiers

PMID36642738
PMCPMC9841147
OpenAlexW4316362273

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.