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Open Access 01-12-2022 | SARS-CoV-2 | Research

Quantifying human mixing patterns in Chinese provinces outside Hubei after the 2020 lockdown was lifted

Authors: Yining Zhao, Samantha O’Dell, Xiaohan Yang, Jingyi Liao, Kexin Yang, Laura Fumanelli, Tao Zhou, Jiancheng Lv, Marco Ajelli, Quan-Hui Liu

Published in: BMC Infectious Diseases | Issue 1/2022

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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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Literature
2.
go back to reference Zhang J, Litvinova M, Wang W, et al. Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study. Lancet Infect Dis. 2020;20(7):793–802.CrossRef Zhang J, Litvinova M, Wang W, et al. Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study. Lancet Infect Dis. 2020;20(7):793–802.CrossRef
4.
go back to reference Mossong J, Hens N, Jit M, et al. Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Med. 2008;5(3): e74.CrossRef Mossong J, Hens N, Jit M, et al. Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Med. 2008;5(3): e74.CrossRef
5.
go back to reference Kretzschmar M, Mikolajczyk RT. Contact profiles in eight European countries and implications for modelling the spread of airborne infectious diseases. PLoS ONE. 2009;4(6): e5931.CrossRef Kretzschmar M, Mikolajczyk RT. Contact profiles in eight European countries and implications for modelling the spread of airborne infectious diseases. PLoS ONE. 2009;4(6): e5931.CrossRef
6.
go back to reference Leung K, Jit M, Lau EHY, et al. Social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong. Sci Rep. 2017;7(1):1–12.CrossRef Leung K, Jit M, Lau EHY, et al. Social contact patterns relevant to the spread of respiratory infectious diseases in Hong Kong. Sci Rep. 2017;7(1):1–12.CrossRef
7.
go back to reference Ajelli M, Litvinova M. Estimating contact patterns relevant to the spread of infectious diseases in Russia. J Theor Biol. 2017;419:1–7.CrossRef Ajelli M, Litvinova M. Estimating contact patterns relevant to the spread of infectious diseases in Russia. J Theor Biol. 2017;419:1–7.CrossRef
8.
go back to reference Zhang J, Klepac P, Read JM, et al. Patterns of human social contact and contact with animals in Shanghai, China. Sci Rep. 2019;9(1):1–11. Zhang J, Klepac P, Read JM, et al. Patterns of human social contact and contact with animals in Shanghai, China. Sci Rep. 2019;9(1):1–11.
9.
go back to reference Jia J, Lu X, Yuan Y, et al. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature. 2020;582(7812):389–94.CrossRef Jia J, Lu X, Yuan Y, et al. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature. 2020;582(7812):389–94.CrossRef
10.
go back to reference Kraemer MUG, Yang CH, Gutierrez B, et al. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science. 2020;368(6490):493–7.CrossRef Kraemer MUG, Yang CH, Gutierrez B, et al. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science. 2020;368(6490):493–7.CrossRef
11.
go back to reference Schlosser F, Maier BF, Jack O, et al. COVID-19 lockdown induces disease-mitigating structural changes in mobility networks. Proc Natl Acad Sci. 2020;117(52):32883–90.CrossRef Schlosser F, Maier BF, Jack O, et al. COVID-19 lockdown induces disease-mitigating structural changes in mobility networks. Proc Natl Acad Sci. 2020;117(52):32883–90.CrossRef
12.
go back to reference Kogan NE, Clemente L, Liautaud P, et al. An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time. Sci Adv. 2021;7(10):eabd6989.CrossRef Kogan NE, Clemente L, Liautaud P, et al. An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time. Sci Adv. 2021;7(10):eabd6989.CrossRef
13.
go back to reference Machens A, Gesualdo F, Rizzo C, et al. An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices. BMC Infect Dis. 2013;13(1):1–15.CrossRef Machens A, Gesualdo F, Rizzo C, et al. An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices. BMC Infect Dis. 2013;13(1):1–15.CrossRef
14.
go back to reference Aleta A, Ferraz de Arruda G, Moreno Y. Data-driven contact structures: from homogeneous mixing to multilayer networks. PLoS Comput Biol. 2020;16(7):e1008035.CrossRef Aleta A, Ferraz de Arruda G, Moreno Y. Data-driven contact structures: from homogeneous mixing to multilayer networks. PLoS Comput Biol. 2020;16(7):e1008035.CrossRef
15.
go back to reference Backer JA, Mollema L, Vos ERA, et al. Impact of physical distancing measures against COVID-19 on contacts and mixing patterns: repeated cross-sectional surveys, the Netherlands, 2016–17, April 2020 and June 2020. Eurosurveillance. 2021;26(8):2000994.CrossRef Backer JA, Mollema L, Vos ERA, et al. Impact of physical distancing measures against COVID-19 on contacts and mixing patterns: repeated cross-sectional surveys, the Netherlands, 2016–17, April 2020 and June 2020. Eurosurveillance. 2021;26(8):2000994.CrossRef
16.
go back to reference Zhang J, Litvinova M, Liang Y, et al. Changes in contact patterns shape the dynamics of the COVID-19 outbreak in china. Science. 2020;368(6498):1481–6.CrossRef Zhang J, Litvinova M, Liang Y, et al. Changes in contact patterns shape the dynamics of the COVID-19 outbreak in china. Science. 2020;368(6498):1481–6.CrossRef
17.
go back to reference Jarvis CI, Van Zandvoort K, Gimma A, et al. Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK. BMC Med. 2020;18(1):1–10.CrossRef Jarvis CI, Van Zandvoort K, Gimma A, et al. Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK. BMC Med. 2020;18(1):1–10.CrossRef
19.
go back to reference Efron B. Bootstrap methods: another look at the jackknife. In: Breakthroughs in statistics. New York: Springer; 1992. p. 569–93.CrossRef Efron B. Bootstrap methods: another look at the jackknife. In: Breakthroughs in statistics. New York: Springer; 1992. p. 569–93.CrossRef
21.
go back to reference Diekmann O, Heesterbeek JAP, Metz JAJ. On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations. J Math Biol. 1990;28:365–82.CrossRef Diekmann O, Heesterbeek JAP, Metz JAJ. On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations. J Math Biol. 1990;28:365–82.CrossRef
22.
go back to reference Fumanelli L, Ajelli M, Manfredi P, Vespignani A, Merler S. Inferring the structure of social contacts from demographic data in the analysis of infectious diseases spread. PLoS Comput Biol. 2012;8(9): e1002673.CrossRef Fumanelli L, Ajelli M, Manfredi P, Vespignani A, Merler S. Inferring the structure of social contacts from demographic data in the analysis of infectious diseases spread. PLoS Comput Biol. 2012;8(9): e1002673.CrossRef
23.
go back to reference Wallinga J, Lipsitch M. How generation intervals shape the relationship between growth rates and reproductive numbers. Proc R Soc B Biol Sci. 2007;274(1609):599–604.CrossRef Wallinga J, Lipsitch M. How generation intervals shape the relationship between growth rates and reproductive numbers. Proc R Soc B Biol Sci. 2007;274(1609):599–604.CrossRef
24.
go back to reference Liu QH, Ajelli M, Aleta A, et al. Measurability of the epidemic reproduction number in data-driven contact networks. Proc Natl Acad Sci. 2018;115(50):12680–5.CrossRef Liu QH, Ajelli M, Aleta A, et al. Measurability of the epidemic reproduction number in data-driven contact networks. Proc Natl Acad Sci. 2018;115(50):12680–5.CrossRef
25.
go back to reference Poletti P, Tirani M, Cereda D, et al. Association of age with likelihood of developing symptoms and critical disease among close contacts exposed to patients with confirmed sars-cov-2 infection in Italy. JAMA Netw Open. 2021;4(3):e211085–e211085.CrossRef Poletti P, Tirani M, Cereda D, et al. Association of age with likelihood of developing symptoms and critical disease among close contacts exposed to patients with confirmed sars-cov-2 infection in Italy. JAMA Netw Open. 2021;4(3):e211085–e211085.CrossRef
26.
go back to reference Yang J, Chen X, Deng X, et al. Disease burden and clinical severity of the first pandemic wave of COVID-19 in Wuhan, China. Nat Commun. 2020;11(1):1–10.CrossRef Yang J, Chen X, Deng X, et al. Disease burden and clinical severity of the first pandemic wave of COVID-19 in Wuhan, China. Nat Commun. 2020;11(1):1–10.CrossRef
27.
go back to reference Fu YC, Wang DW, Chuang JH. Representative contact diaries for modeling the spread of infectious diseases in Taiwan. PLoS ONE. 2012;7(10): e45113.CrossRef Fu YC, Wang DW, Chuang JH. Representative contact diaries for modeling the spread of infectious diseases in Taiwan. PLoS ONE. 2012;7(10): e45113.CrossRef
28.
go back to reference Zhang J, Litvinova M, Liang Y, et al. The impact of relaxing interventions on human contact patterns and SARS-CoV-2 transmission in China. Sci Adv. 2021;7(19):eabe2584.CrossRef Zhang J, Litvinova M, Liang Y, et al. The impact of relaxing interventions on human contact patterns and SARS-CoV-2 transmission in China. Sci Adv. 2021;7(19):eabe2584.CrossRef
29.
go back to reference Smieszek T, Burri EU, Scherzinger R, et al. Collecting close-contact social mixing data with contact diaries: reporting errors and biases. Epidemiol Infect. 2012;140(4):744–52.CrossRef Smieszek T, Burri EU, Scherzinger R, et al. Collecting close-contact social mixing data with contact diaries: reporting errors and biases. Epidemiol Infect. 2012;140(4):744–52.CrossRef
Metadata
Title
Quantifying human mixing patterns in Chinese provinces outside Hubei after the 2020 lockdown was lifted
Authors
Yining Zhao
Samantha O’Dell
Xiaohan Yang
Jingyi Liao
Kexin Yang
Laura Fumanelli
Tao Zhou
Jiancheng Lv
Marco Ajelli
Quan-Hui Liu
Publication date
01-12-2022
Publisher
BioMed Central
Published in
BMC Infectious Diseases / Issue 1/2022
Electronic ISSN: 1471-2334
DOI
https://doi.org/10.1186/s12879-022-07455-7

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