Evidence map›Paper›PMID 35287110›Full record

ArticleEpidemics2022

Variability in transmission risk of SARS-CoV-2 in close contact settings: A contact tracing study in Shandong Province, China.

Tim K Tsang, Li-Qun Fang, Anran Zhang, Fa-Chun Jiang, Shi-Man Ruan, Lan-Zheng Liu, Benjamin J Cowling, Wei Liu, Yang Yang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Epidemics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 2 pooled it
–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

9 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Observational
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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

9 authors.

Tim K TsangWHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.
Li-Qun FangState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China.
Anran ZhangState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China; Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
Fa-Chun JiangQingdao Municipal Centre for Disease Control and Prevention & Qingdao Institute of Preventive Medicine, Shandong, China.
Shi-Man RuanJinan Municipal Centre for Disease Control and Prevention, Shandong, China.
Lan-Zheng LiuJinan Municipal Centre for Disease Control and Prevention, Shandong, China.
Benjamin J CowlingWHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China. Electronic address: bcowling@hku.hk.
Wei LiuState Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China. Electronic address: liuwei@bmi.ac.cn.
Yang YangDepartment of Biostatistics, School of Public Health and Health Professions & Emerging Pathogens Institute, University of Florida, Gainesville, FL 32610, USA. Electronic address: yangyang@ufl.edu.

Funding

Mathematical and Statistical Methods for the Control of Global Infectious Disease ThreatsR56AI148284 · NIAID · UNIVERSITY OF FLORIDA · PI HALLORAN, M ELIZABETH, LONGINI, IRA M · 2020 to 2020
$802k
NIAID NIH HHS R56 AI148284
6 · The paper itself

Abstract

backgroundUnderstanding the relative transmissibility of SARS-CoV-2 virus across different contact settings and the possibility of superspreading events is important for prioritizing disease control. Such assessment requires proper consideration of individual level exposure history, which is made possible by contact tracing.

methodsThe case-ascertained study in Shandong, China including 97 laboratory-confirmed index cases and 3158 close contacts. All close contacts were quarantined after their last exposure of index cases. Contacts were tested for COVID-19 regularly by PCR to identify both symptomatic and asymptomatic infections. We developed a Bayesian transmission model to the contact tracing data to account for different duration of exposure among individuals to transmission risk in different settings, and the heterogeneity of infectivity of cases.

resultsWe estimate secondary attack rates (SAR) to be 39% (95% credible interval (CrI): 20-64%) in households, 30% (95% CrI: 11-67%) in healthcare facilities, 23% (95% CrI: 7-51%) at workplaces, and 4% (95% CrI: 1-17%) during air travel. Models allowing heterogeneity of infectivity of cases provided a better goodness-of-fit. We estimated that 64% (95% CrI: 55-72%) of cases did not generate secondary transmissions, and 20% (95% CrI: 15-26%) cases explained 80% of secondary transmissions.

conclusionsHousehold, healthcare facilities and workplaces are efficient setting for transmission. Timely identification of potential superspreaders in most transmissible settings remains crucial for containing the pandemic.

Indexed as

COVID-19SARS-CoV-2Bayes TheoremChinaContact TracingHumansClose contactCOVID-19HouseholdsSuperspreadingTransmission risk

Identifiers

PMID35287110
PMCPMC8906027

What OpenQuestion holds

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Registered trials

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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.