Evidence map›Paper›PMID 35910302›Full record

ArticleOne health (Amsterdam, Netherlands)2022

Analysis of the close contact management mode and epidemiological characteristics of COVID-19 in Chengdu, China.

Kai Yang, Jiali Deng, Xiaoli Tuo, Shuangfeng Fan, Yong Yue, Hui Liu, Zhijian Liu, Shuang Zhang, Lingyi Wang, Rong Dai and 1 more

Abstract read
In one paragraph

Article in One health (Amsterdam, Netherlands), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

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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

11 authors.

Kai YangChengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu, Sichuan, People's Republic of China.
Jiali DengDepartment of Orthopaedics, Chengdu Medical College, Sichuan, People's Republic of China.
Xiaoli TuoChengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu, Sichuan, People's Republic of China.
Shuangfeng FanChengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu, Sichuan, People's Republic of China.
Yong YueChengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu, Sichuan, People's Republic of China.
Hui LiuChengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu, Sichuan, People's Republic of China.
Zhijian LiuDepartment of Infectious Disease Control, Center for Disease Control and Prevention of Chenghua District, Chengdu, Sichuan, People's Republic of China.
Shuang ZhangChengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu, Sichuan, People's Republic of China.
Lingyi WangChengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu, Sichuan, People's Republic of China.
Rong DaiChengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu, Sichuan, People's Republic of China.
Yao ZhaoChengdu Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Chengdu, Sichuan, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the development of the novel coronavirus disease 2019 (COVID-19) epidemic and the increase in cases, as a potential source of infection, the risk of close contact has gradually increased. However, few studies have analyzed the tracking and management of cross-regional personnel. In this study, we hope to understand the effectiveness and feasibility of existing close contact management measures in Chengdu, so as to provide a reference for further prevention and control of the epidemic. The close contact management mode and epidemiological characteristics of 40,425 close contacts from January 22, 2020, to March 1, 2022, in Chengdu, China, were analyzed. The relationship with index cases was mainly co-passengers (57.58%) and relatives (7.20%), and the frequency of contact was mainly occasional contact (70.39%). A total of 400 (0.99%) close contacts were converted into cases, which were mainly found in the first and second nucleic acid tests (53.69%), and the contact mode was mainly by sharing transportation (63.82%). In terms of close contact management time, both the supposed ((11.93 ± 3.00) days

Indexed as

Close contactDisease conversionEpidemiological characteristicsSpatial distributionTracing and management

Identifiers

PMID35910302
PMCPMC9323207

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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.