Evidence map›Paper›PMID 39030231›Full record

ArticleNature communications2024

Estimating the contribution of setting-specific contacts to SARS-CoV-2 transmission using digital contact tracing data.

Zengmiao Wang, Peng Yang, Ruixue Wang, Luca Ferretti, Lele Zhao, Shan Pei, Xiaoli Wang, Lei Jia, Daitao Zhang, Yonghong Liu and 4 more

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

14 authors.

Zengmiao Wang *State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China.
Peng Yang *Beijing Center for Disease Prevention and Control, Beijing, China.
Ruixue Wang *State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China.
Luca FerrettiPandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.ORCID 0000-0001-7578-7301
Lele ZhaoPandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.ORCID 0000-0002-2807-1914
Shan PeiState Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China.
Xiaoli WangBeijing Center for Disease Prevention and Control, Beijing, China.
Lei JiaBeijing Center for Disease Prevention and Control, Beijing, China.
Daitao ZhangBeijing Center for Disease Prevention and Control, Beijing, China.
Yonghong LiuBeijing Center for Disease Prevention and Control, Beijing, China.
Ziyan LiuState Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China.
Quanyi WangBeijing Center for Disease Prevention and Control, Beijing, China. wangqy@bjcdc.org.ORCID 0000-0001-9552-2503
Christophe FraserPandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.ORCID 0000-0003-2399-9657
Huaiyu TianState Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China. tianhuaiyu@gmail.com.ORCID 0000-0002-4466-0858

Funding

National Natural Science Foundation of China (National Science Foundation of China) 82073616
6 · The paper itself

Abstract

While many countries employed digital contact tracing to contain the spread of SARS-CoV-2, the contribution of cospace-time interaction (i.e., individuals who shared the same space and time) to transmission and to super-spreading in the real world has seldom been systematically studied due to the lack of systematic sampling and testing of contacts. To address this issue, we utilized data from 2230 cases and 220,878 contacts with detailed epidemiological information during the Omicron outbreak in Beijing in 2022. We observed that contact number per day of tracing for individuals in dwelling, workplace, cospace-time interactions, and community settings could be described by gamma distribution with distinct parameters. Our findings revealed that 38% of traced transmissions occurred through cospace-time interactions whilst control measures were in place. However, using a mathematical model to incorporate contacts in different locations, we found that without control measures, cospace-time interactions contributed to only 11% (95%CI: 10%-12%) of transmissions and the super-spreading risk for this setting was 4% (95%CI: 3%-5%), both the lowest among all settings studied. These results suggest that public health measures should be optimized to achieve a balance between the benefits of digital contact tracing for cospace-time interactions and the challenges posed by contact tracing within the same setting.

Indexed as

Contact TracingCOVID-19SARS-CoV-2ChinaDisease OutbreaksHumansModels, Theoretical

Identifiers

PMID39030231
PMCPMC11271501

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.