Evidence map›Paper›PMID 36371145›Full record

ArticleBMC infectious diseases2022

Quantitative assessment of the effects of massive nucleic acid testing in controlling a COVID-19 outbreak.

Wenlong Zhu, Yue Zhu, Zexuan Wen, Bo Zheng, Ao Xu, Ye Yao, Weibing Wang

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

Article in BMC infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.2field-weighted citation impact, top 20% 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

9 citing papers in PubMed, 14 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

7 authors at 1 institution in 1 country.

Wenlong Zhu *Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, 20032, China.
Yue Zhu *Department of Epidemiology, School of Public Health, Fudan University, 138 Yi Xue Yuan Road, Shanghai, 200032, China.
Zexuan WenDepartment of Epidemiology, School of Public Health, Fudan University, 138 Yi Xue Yuan Road, Shanghai, 200032, China.
Bo ZhengDepartment of Epidemiology, School of Public Health, Fudan University, 138 Yi Xue Yuan Road, Shanghai, 200032, China.
Ao XuDepartment of Epidemiology, School of Public Health, Fudan University, 138 Yi Xue Yuan Road, Shanghai, 200032, China.
Ye YaoDepartment of Epidemiology, School of Public Health, Fudan University, 138 Yi Xue Yuan Road, Shanghai, 200032, China. yyao@fudan.edu.cn.
Weibing WangShanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, 20032, China. wwb@fudan.edu.cn.
Fudan University · CN

Funding

Bill and Melinda Gates Foundation Grant No. INV-006277Bill & Melinda Gates Foundation INV-006277National Natural Science Foundation of China Grant No. 82073612Shanghai Municipal Health Commission Grant No. GWV-10.1-XK16Shanghai Municipal Science and Technology Major Project ZD2021CY001
6 · The paper itself

Abstract

backgroundFrom 20 July to 26 August 2021, local outbreaks of COVID-19 occurred in Nanjing City and Yangzhou City (Jiangsu Province, China). We analyzed the characteristics of these outbreaks in an effort to develop specific and effective intervention strategies.

methodsPublicly available data on the characteristics of the COVID-19 outbreaks in Jiangsu Province were collected. Logistic regression was used to assess the association of age and sex with clinical severity. Analysis of onset dates, generation time distributions, and locations were used to estimate the mean transmission distance. A branching process model was used to evaluate different management strategies.

resultsFrom 20 July to 26 August 2021, 820 patients were diagnosed with COVID-19 in Jiangsu Province, with 235 patients (28.7%) from Nanjing, 570 (69.5%) from Yangzhou, and 15 (1.8%) from other cities. Overall, 57.9% of the patients were female, 13.7% were under 20 years-old, and 58.3% had moderate disease status. The mean transmission distance was 4.12 km, and closed-loop management of the area within 2.23 km of cases seemed sufficient to control an outbreak. The model predicted that the cumulative cases in Yangzhou would increase from 311 to 642 if the interval between rounds of nucleic acid amplification testing (NAAT) increased from 1 to 6 days. It also predicted there would be 44.7% more patients if the NAAT started 10 days (rather than 0 days) after diagnosis of the first case. The proportion of cases detected by NAAT would increase from 11.16 to 44.12% when the rounds of NAAT increased from 1 to 7 within 17 days. When the effective vaccine coverage was 50%, the outbreak would be controlled even when using the most relaxed non-pharmaceutical interventions.

conclusionsThe model predicted that a timely closed-loop management of a 2.23 km area around positive COVID-19 cases was sufficient to control the outbreak. Prompt serial NAAT is likely to contain an outbreak quickly, and our model results indicated that three rounds of NAAT sufficiently controlled local transmission. Trial registration We did not involve clinical trial.

Indexed as

COVID-19Nucleic AcidsAdultChinaDisease OutbreaksFemaleHumansMaleSARS-CoV-2Young AdultNucleic AcidsBranching process modelClose contact tracingCOVID-19Non-pharmaceutical interventionNucleic acid amplification testingOutbreakTransmission distanceVaccine

Identifiers

PMID36371145
PMCPMC9652905
OpenAlexW4309072814

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.