Impact of public health and social measures on contact dynamics during a SARS-CoV-2 Omicron variant outbreak in Quanzhou, China, March to April 2022

Abstract

Objectives: To evaluate the impact of early implementation of public health and social measures (PHSMs) on contact rates over time and explore contact behavior of asymptomatic versus symptomatic cases.

Methods: We used the largest contact tracing data in China thus far to estimate the mean contacts over time by age groups and contact settings. We used bootstrap with replacement to quantify the uncertainty of contact matrixes. The Pearson correlation was performed to demonstrate the number of contacts over time in relation to the evolution of restrictions. In addition, we analyzed the index cases with a high number of contacts and index cases that produced a high number of secondary cases.

Results: Rapidly adapted PHSMs can reduce the mean contact rates in public places while increasing the mean contact rates within households. The mean contact rates were 11.81 (95% confidence interval, 11.61-12.01) for asymptomatic (at the time of investigation) cases and 6.70 (95% confidence interval, 6.54-6.87) for symptomatic cases. The percentage of asymptomatic cases (at the time of investigation) meeting >50 close contacts make up more than 65% of the overall cases. The percentage of asymptomatic cases producing >10 secondary cases account for more than 80% of the overall cases.

Conclusion: PHSMs may increase the contacts within the household, necessitating the need for pertinent prevention strategies at home. Asymptomatic cases can contribute significantly to Omicron transmission. By making asymptomatic people aware that they are already contagious, hence limiting their social contacts, it is possible to lower the transmission risk.

Keywords: COVID-19; Contact tracing; Public health intervention.

Figure 1

Mean contacts by settings and by age group over time in Quanzhou City, China during an Omicron outbreak. The uncertainty of mean contacts was calculated using bootstrap with replacement on the original contact tracing dataset. We assigned weights based on the Quanzhou population stratified by age. Pearson correlation was employed to examine the correlation between the stringent index and mean contacts over time. The red colors signify positive correlation, the blue pieces signify negative correlation, the coefficients and P-values of each subgroup were annotated in gray on the upper right of each cell. The time series of the stringency index is represented by the brown lines on the left, which map to the second y-axis of the settings for all age groups combined.

Figure 2

Analyses of index cases by severity. (a) The estimated number of contacts made by asymptomatic and symptomatic infections with 95% confidence intervals. (b,c) Age-mixing patterns of index cases and their close contacts. The age was categorized into 16 age groups. Each cell of the matrix represents the mean number of daily contacts produced by a member in each age group with other individuals. The color intensity represents the number of daily contacts. (d) Proportions by severity for cases with high numbers of contacts. The number of contacts attributed to the two days of contact tracing. (e) Proportions by severity for cases caused high numbers of secondary cases. The number of secondary cases is calculated by counting the infected individuals that occur among close contacts after exposure to an index case.