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Estimating the contribution of setting-specific contacts to SARS-CoV-2 transmission using digital contact tracing data.
Wang, Zengmiao; Yang, Peng; Wang, Ruixue; Ferretti, Luca; Zhao, Lele; Pei, Shan; Wang, Xiaoli; Jia, Lei; Zhang, Daitao; Liu, Yonghong; Liu, Ziyan; Wang, Quanyi; Fraser, Christophe; Tian, Huaiyu.
Afiliação
  • 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.
  • Yang P; Beijing Center for Disease Prevention and Control, Beijing, China.
  • Wang R; Beijing Research Center for Respiratory Infectious Diseases, Beijing, China.
  • Ferretti L; State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China.
  • Zhao L; Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
  • Pei S; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
  • Wang X; Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
  • Jia L; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
  • Zhang D; State Key Laboratory of Remote Sensing Science, Center for Global Change and Public Health, Faculty of Geographical Science, Beijing Normal University, Beijing, China.
  • Liu Y; Beijing Center for Disease Prevention and Control, Beijing, China.
  • Liu Z; Beijing Research Center for Respiratory Infectious Diseases, Beijing, China.
  • Wang Q; Beijing Center for Disease Prevention and Control, Beijing, China.
  • Fraser C; Beijing Research Center for Respiratory Infectious Diseases, Beijing, China.
  • Tian H; Beijing Center for Disease Prevention and Control, Beijing, China.
Nat Commun ; 15(1): 6103, 2024 Jul 19.
Article em En | MEDLINE | ID: mdl-39030231
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Busca de Comunicante / SARS-CoV-2 / COVID-19 Limite: Humans País/Região como assunto: Asia Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Busca de Comunicante / SARS-CoV-2 / COVID-19 Limite: Humans País/Região como assunto: Asia Idioma: En Ano de publicação: 2024 Tipo de documento: Article