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


Journal

Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555

Informations de publication

Date de publication:
19 Jul 2024
Historique:
received: 03 12 2023
accepted: 09 07 2024
medline: 20 7 2024
pubmed: 20 7 2024
entrez: 19 7 2024
Statut: epublish

Résumé

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.

Identifiants

pubmed: 39030231
doi: 10.1038/s41467-024-50487-7
pii: 10.1038/s41467-024-50487-7
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

6103

Subventions

Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : 82073616

Informations de copyright

© 2024. The Author(s).

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Auteurs

Zengmiao Wang (Z)

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 (P)

Beijing Center for Disease Prevention and Control, Beijing, China.
Beijing Research Center for Respiratory Infectious Diseases, Beijing, China.

Ruixue Wang (R)

State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China.

Luca Ferretti (L)

Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.

Lele Zhao (L)

Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.

Shan Pei (S)

State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China.

Xiaoli Wang (X)

Beijing Center for Disease Prevention and Control, Beijing, China.
Beijing Research Center for Respiratory Infectious Diseases, Beijing, China.

Lei Jia (L)

Beijing Center for Disease Prevention and Control, Beijing, China.
Beijing Research Center for Respiratory Infectious Diseases, Beijing, China.

Daitao Zhang (D)

Beijing Center for Disease Prevention and Control, Beijing, China.
Beijing Research Center for Respiratory Infectious Diseases, Beijing, China.

Yonghong Liu (Y)

Beijing Center for Disease Prevention and Control, Beijing, China.
Beijing Research Center for Respiratory Infectious Diseases, Beijing, China.

Ziyan Liu (Z)

State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China.

Quanyi Wang (Q)

Beijing Center for Disease Prevention and Control, Beijing, China. wangqy@bjcdc.org.
Beijing Research Center for Respiratory Infectious Diseases, Beijing, China. wangqy@bjcdc.org.

Christophe Fraser (C)

Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.

Huaiyu Tian (H)

State 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.

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