Evidence map›Paper›PMID 32503192›Full record

ArticleInternational journal of environmental research and public health2020

A Comparison of Infection Venues of COVID-19 Case Clusters in Northeast China.

Pengcheng Zhao, Nan Zhang, Yuguo Li

Abstract readComparative Study
In one paragraph

Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Factors Associated with Mental Health Results among Workers with Income Losses Exposed to COVID-19 in China.International journal of environmental research and public health · 2020
    Article
  5. Article
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

3 authors.

Pengcheng ZhaoDepartment of Mechanical Engineering, The University of Hong Kong, Hong Kong 999077, China.
Nan ZhangDepartment of Mechanical Engineering, The University of Hong Kong, Hong Kong 999077, China.
Yuguo LiDepartment of Mechanical Engineering, The University of Hong Kong, Hong Kong 999077, China.

Funding

Research Grants Council, University Grants Committee 17249616Research Grants Council, University Grants Committee C7025-16G
6 · The paper itself

Abstract

The world has been suffering from the COVID-19 pandemic since late 2019. In this study, we compared various types of infection locations in which COVID-19 cases clustered, based on the data from three adjacent provinces in Northeast China. The collected data include all officially reported cases in this area until 8 March 2020. We explored the associations between the cases and the frequency of infection locations. The COVID-19 epidemic situation was worse in Heilongjiang Province than in Liaoning and Jilin Provinces. Most clustered cases occurred in individual families and/or between relatives. The transmission in public venues served as a hub for transmitting the disease to other families and results in new clusters. The public transport spread the infection over long distances by transporting infected individuals, and most infections did not seem to occur within vehicles. This field study shows the effect of indoor environments on SARS-CoV-2 transmission and our data may be useful in developing guidance for future disease prevention and control.

Indexed as

Public FacilitiesWorkplaceBetacoronavirusChinaCluster AnalysisCoronavirus InfectionsCOVID-19HumansPandemicsPneumonia, ViralSARS-CoV-2case reportclusterCOVID-19infection locationNortheast China

Identifiers

PMID32503192
PMCPMC7312430

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.