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. 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.

Keywords: Age; COVID-19; Contact patterns; Disease burden; Human behavior; Mathematical modeling.

Fig. 1

Daily number of contacts and contacts by age. A Mean number of contacts by age group of study participants. The standard error in each age group is obtained from bootstrap sampling according to the age distribution for the 12 provinces included in our analysis as reported in the 2010 Chinese census. The two bars on the right show the mean number of contacts irrespective of the age of study participant in the sample and by adjusting for the age structure of the 12 provinces included in our analysis. B Fraction of contacts with same age class as participants. C Overall contact matrix by age group

Fig. 2

Relationship between contacts and location of contacts. A Probability distribution of the relationship between the study participant of a given age group and the contacted individual. The bars on the right show the probability distribution of the relation between study participant irrespective of age and the contacted individual in the sample and by adjusting for the age structure of the 12 provinces included in our analysis as reported in the Chinese 2010 census. B As A, but showing the location where contacts took place

Fig. 3

Daily average duration of contacts by age. A Daily average duration of contacts by age group of study participants. The standard error in each age group is obtained from bootstrap sampling according to the age distribution for the 12 provinces included in our analysis as reported in the Chinese 2010 census. The bars on the right show the daily average duration per contact between study participant irrespective of age and the contacted individual in the sample and by adjusting for the age structure of the 12 provinces included in our analysis. B Fraction of contact duration with same age class individuals. C Overall contact duration matrix by age groups. D Probability distribution of the relation between study participant of a given age group and contacted individuals

Fig. 4

Application to COVID-19. A Number of infections by age group for the three models. The number of initial seeds is set to 1, ${\mathrm{R}}_0$ is fixed to 1.3, and the simulation is interrupted when cumulative 1,000 infections are reached. B As A, but for symptomatic individuals. C As A, but for hospital admissions. D As A, but for deaths