Evidence map›Paper›PMID 35597895›Full record

ArticleBMC infectious diseases2022

Quantifying human mixing patterns in Chinese provinces outside Hubei after the 2020 lockdown was lifted.

Yining Zhao, Samantha O'Dell, Xiaohan Yang, Jingyi Liao, Kexin Yang, Laura Fumanelli, Tao Zhou, Jiancheng Lv, Marco Ajelli, Quan-Hui Liu

Open access · goldAbstract read
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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 38% 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

3 citing papers in PubMed, 5 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

10 authors at 6 institutions in 2 countries.

Yining ZhaoCollege of Computer Science, Sichuan University, Chengdu, China.
Samantha O'DellLaboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
Xiaohan YangInstitute for Applied Computational Science, Harvard University, Cambridge, MA, USA.
Jingyi LiaoShenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Kexin YangCollege of Computer Science, Sichuan University, Chengdu, China.
Laura FumanelliLaboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
Tao ZhouBig Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.
Jiancheng LvCollege of Computer Science, Sichuan University, Chengdu, China.
Marco Ajelli *Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
Quan-Hui Liu *College of Computer Science, Sichuan University, Chengdu, China. quanhuiliu@scu.edu.cn.
Indiana University Bloomington · USChengdu University · CNSichuan University · CNHarvard University · USUniversity of Electronic Science and Technology of China · CNUniversity Town of Shenzhen · CN

Funding

Fundamental Research Funds for the Central Universities 1082204112289National Natural Science Foundation of China 62003230Sichuan University 0082604151026
6 · The paper itself

Abstract

backgroundContact patterns play a key role in the spread of respiratory infectious diseases in human populations. During the COVID-19 pandemic, the regular contact patterns of the population have been disrupted due to social distancing both imposed by the authorities and individual choices. Many studies have focused on age-mixing patterns before the COVID-19 pandemic, but they provide very little information about the mixing patterns in the COVID-19 era. In this study, we aim at quantifying human heterogeneous mixing patterns immediately after lockdowns implemented to contain COVID-19 spread in China were lifted. We also provide an illustrative example of how the collected mixing patterns can be used in a simulation study of SARS-CoV-2 transmission. METHODS AND

resultsIn this work, a contact survey was conducted in Chinese provinces outside Hubei in March 2020, right after lockdowns were lifted. We then leveraged the estimated mixing patterns to calibrate a mathematical model of SARS-CoV-2 transmission. Study participants reported 2.3 contacts per day (IQR: 1.0-3.0) and the mean per-contact duration was 7.0 h (IQR: 1.0-10.0). No significant differences in average contact number and contact duration were observed between provinces, the number of recorded contacts did not show a clear trend by age, and most of the recorded contacts occurred with family members (about 78%). The simulation study highlights the importance of considering age-specific contact patterns to estimate the COVID-19 burden.

conclusionsOur findings suggest that, despite lockdowns were no longer in place at the time of the survey, people were still heavily limiting their contacts as compared to the pre-pandemic situation.

Indexed as

COVID-19SARS-CoV-2Communicable Disease ControlHumansPandemicsPhysical DistancingAgeContact patternsCOVID-19Disease burdenHuman behaviorMathematical modeling

Identifiers

PMID35597895
PMCPMC9123295
OpenAlexW3216766167

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.