Quantifying human mixing patterns in Chinese provinces outside Hubei after the 2020 lockdown was lifted
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...
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| Format: | Article |
| Language: | English |
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BMC
2022-05-01
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| Series: | BMC Infectious Diseases |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12879-022-07455-7 |
| _version_ | 1811340169470541824 |
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| author | Yining Zhao Samantha O’Dell Xiaohan Yang Jingyi Liao Kexin Yang Laura Fumanelli Tao Zhou Jiancheng Lv Marco Ajelli Quan-Hui Liu |
| author_facet | Yining Zhao Samantha O’Dell Xiaohan Yang Jingyi Liao Kexin Yang Laura Fumanelli Tao Zhou Jiancheng Lv Marco Ajelli Quan-Hui Liu |
| author_sort | Yining Zhao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T18:38:02Z |
| format | Article |
| id | doaj.art-7d2aeb8921654d7abd94e7e6062fbf80 |
| institution | Directory Open Access Journal |
| issn | 1471-2334 |
| language | English |
| last_indexed | 2024-04-13T18:38:02Z |
| publishDate | 2022-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Infectious Diseases |
| spelling | doaj.art-7d2aeb8921654d7abd94e7e6062fbf802022-12-22T02:34:49ZengBMCBMC Infectious Diseases1471-23342022-05-0122111010.1186/s12879-022-07455-7Quantifying human mixing patterns in Chinese provinces outside Hubei after the 2020 lockdown was liftedYining Zhao0Samantha O’Dell1Xiaohan Yang2Jingyi Liao3Kexin Yang4Laura Fumanelli5Tao Zhou6Jiancheng Lv7Marco Ajelli8Quan-Hui Liu9College of Computer Science, Sichuan UniversityLaboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public HealthInstitute for Applied Computational Science, Harvard UniversityShenzhen International Graduate School, Tsinghua UniversityCollege of Computer Science, Sichuan UniversityLaboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public HealthBig Data Research Center, University of Electronic Science and Technology of ChinaCollege of Computer Science, Sichuan UniversityLaboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public HealthCollege of Computer Science, Sichuan UniversityAbstract 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.https://doi.org/10.1186/s12879-022-07455-7Contact patternsHuman behaviorCOVID-19AgeDisease burdenMathematical modeling |
| spellingShingle | Yining Zhao Samantha O’Dell Xiaohan Yang Jingyi Liao Kexin Yang Laura Fumanelli Tao Zhou Jiancheng Lv Marco Ajelli Quan-Hui Liu Quantifying human mixing patterns in Chinese provinces outside Hubei after the 2020 lockdown was lifted BMC Infectious Diseases Contact patterns Human behavior COVID-19 Age Disease burden Mathematical modeling |
| title | Quantifying human mixing patterns in Chinese provinces outside Hubei after the 2020 lockdown was lifted |
| title_full | Quantifying human mixing patterns in Chinese provinces outside Hubei after the 2020 lockdown was lifted |
| title_fullStr | Quantifying human mixing patterns in Chinese provinces outside Hubei after the 2020 lockdown was lifted |
| title_full_unstemmed | Quantifying human mixing patterns in Chinese provinces outside Hubei after the 2020 lockdown was lifted |
| title_short | Quantifying human mixing patterns in Chinese provinces outside Hubei after the 2020 lockdown was lifted |
| title_sort | quantifying human mixing patterns in chinese provinces outside hubei after the 2020 lockdown was lifted |
| topic | Contact patterns Human behavior COVID-19 Age Disease burden Mathematical modeling |
| url | https://doi.org/10.1186/s12879-022-07455-7 |
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