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Quantifying human mixing patterns in Chinese provinces outside Hubei after the 2020 lockdown was lifted.
Zhao, Yining; O'Dell, Samantha; Yang, Xiaohan; Liao, Jingyi; Yang, Kexin; Fumanelli, Laura; Zhou, Tao; Lv, Jiancheng; Ajelli, Marco; Liu, Quan-Hui.
Affiliation
  • Zhao Y; College of Computer Science, Sichuan University, Chengdu, China.
  • O'Dell S; Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
  • Yang X; Institute for Applied Computational Science, Harvard University, Cambridge, MA, USA.
  • Liao J; Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
  • Yang K; College of Computer Science, Sichuan University, Chengdu, China.
  • Fumanelli L; Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
  • Zhou T; Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.
  • Lv J; College of Computer Science, Sichuan University, Chengdu, China.
  • Ajelli M; Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
  • Liu QH; College of Computer Science, Sichuan University, Chengdu, China. quanhuiliu@scu.edu.cn.
BMC Infect Dis ; 22(1): 483, 2022 May 21.
Article in En | MEDLINE | ID: mdl-35597895
ABSTRACT

BACKGROUND:

Contact 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

RESULTS:

In 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.

CONCLUSIONS:

Our 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.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Prognostic_studies Limits: Humans Language: En Journal: BMC Infect Dis Journal subject: DOENCAS TRANSMISSIVEIS Year: 2022 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Prognostic_studies Limits: Humans Language: En Journal: BMC Infect Dis Journal subject: DOENCAS TRANSMISSIVEIS Year: 2022 Document type: Article Affiliation country:
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