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.

Introduction

Studies have suggested that early implementation of interventions was associated with higher effectiveness in reducing COVID-19 cases or deaths [1,2]; however, its impact on contacts over time has not been fully discussed [3,4]. Here, we used the largest contact tracing data in China thus far to present the daily mean contacts by various age groups under diverse contact settings. A total of 66,583 close contacts linked to 2036 unique index cases were included. We estimated the mean number of contacts and their evolution in the first week before the epidemic curve reached its peak, during which, the government's rapid response issued containment orders. In addition, we analyzed all the confirmed cases connected to close contacts and compared the asymptomatic and symptomatic cases from varied perspectives. This study aimed to provide differentiated parameters for infectious disease modeling and add information about public health and social measures.

Methods

The contact tracing database was compiled during an epidemiology field investigation that responded to an Omicron outbreak in Quanzhou City, China between March 13, 2022 and April 14, 2022. All the cases were traced in this outbreak and confirmed by reverse transcriptase-polymerase chain reaction positive results. Close contacts include people who have contacted within proximity in the 2 days before the identification of index cases but have not taken effective protection. The detailed identification criteria of close contacts are provided in the Appendix. A total of 66,583 close contacts linked to 2036 unique index cases were included. The demographic information and contact settings were obtained for both. In addition, the date of case identification, date of isolation, the severest clinical outcomes, and occupations were obtained for the infected individuals.

We analyzed the index case and close contact pairs. We calculated the mean number of contacts in school, workplace, household, and overall settings. The uncertainty of contact matrixes was quantified using a 100-times bootstrap, with replacement on the original contact dataset. We calculated the mean, smoothed over the 1-week intervals to give a larger number of index cases per estimate. The index cases identified on each day and 3 days before and after and their close contacts were included per estimate. The Pearson correlation coefficient was used to demonstrate the correlation between the mean contact and the stringency index collected by the Oxford COVID-19 Government Response Tracker [5]. The analyses and generated figures were conducted using R version 4.2.1.

Results and discussion

At the very beginning of the outbreak in Quanzhou City, countermeasures, such as increasing lockdown areas, suspension of public events, strict control of gatherings in schools, and traffic restrictions were promptly taken by the government (Figure 1 ). The number of daily new confirmed cases peaked within the first week, and the most stringent control was achieved on the third day after the outbreak. As shown in Figure 1, the mean contacts within households tended to increase over this period, but those in school, at the workplace, and in overall tended to decline. Strong negative correlations can be seen across most age groups in school, at the workplace, and in overall settings. However, positive correlations were found within household circumstances. The 15-20 years age group population in school settings likewise showed a positive correlation; this might relate to a sense of urgency heightened by the fact that the gaokao (one of the most influential examinations in China) will be completed in less than 100 days. Thus, senior students and high school teachers were reluctant to switch to online teaching. In addition, because of the residential system, high school students stayed in school 5-6 days a week despite social constraints and maintained regular contact in the school setting. In the overall setting, the elderly aged >60 years also showed a propensity for mean contacts to rise as the stringency index increased. The elderly might be able to meet more family members than usual when the city advocates or requires people not to leave their homes. Studies have shown that households remain important venues for transmission [3,6,7], and it is essential to implement multicomponent prevention strategies, including up-to-date vaccination, isolation of infected persons, and mask use at home [8].

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.

The mean contacts were 11.81 (95% confidence interval, 11.61-12.01) for asymptomatic cases and 6.70 (95% confidence interval, 6.54-6.87) for symptomatic cases (Figure 2 a). Then, we further explored the index cases with high contacts and cases that produced a high number of secondary infections. More than 65% of cases who had >50 close contacts were asymptomatic (at the time of investigation) (Figure 2d), and among cases with a high transmissibility that caused secondary infections in >10 people, given that the average basic reproduction number of Omicron is around 9.5 [9], the asymptomatic cases (at the time of investigation) accounted for greater than 80% (Figure 2e). Symptomatic patients typically alter their behavioral patterns when they develop symptoms, such as reducing the frequency of having close contacts or paying greater attention to self-protection. In contrast, asymptomatic cases lack the self-awareness to restrict social events before testing positive. Notably, of all confirmed cases linked to close contacts in this outbreak, the proportion of asymptomatic cases made up 64.86%. In this case, emphasis should be placed on broad screenings with high efficacy. Rapid antigen testing has a relatively low detection sensitivity [10,11], but if it is widely used, it may help a significant number of asymptomatic people become aware of their infection, hence limiting their social interactions with others. Consequently, the likelihood of ongoing transmission through crowds can be decreased.

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.

Conclusion

By summarizing and comparing the contact patterns, this study draws the conclusion that rapidly adapted public health and social measures can reduce the mean contacts in public places in a timely manner before the epidemic curve peaks. However, these measures also increase contacts within households, necessitating the need for pertinent prevention strategies. We also saw that asymptomatic cases contribute significantly to Omicron transmission. By making asymptomatic people aware that they are already contagious and thus modifying their behavioral patterns, lowering the risk of spreading the disease is possible.

Funding

This work was supported by the National Key Research and Development Program of China (2021YFC2301604), the Fundamental Research Funds for the Central Universities (No. 20720230001), Self-supporting Program of Guangzhou Laboratory (No. SRPG22-007), and the Natural Science Foundation of Fujian Province (No. 2021J01353 and No. 2020J01094).

Ethical approval

No personally identifiable information on the index cases or close contacts were included in any of the data we used, which were all deidentified; thus, our research does not require ethics committee approval.

Data and code availability

The source code and differentiated daily mean contact and contact matrixes are accessible at GitHub repository (https://github.com/rorschachkwok/contact_patterns_during_outbreak).

Declaration of competing interest

The authors have no competing interests to declare.

Author contributions

YCG, ZYZ, and TMC conceived and designed the study. SGW, WJY, JMO, WC, SJC, MRZ, and ZQH conducted the investigation and compiled the data. YCG, KGL, and XHG standardized the data and performed the statistical analysis. SGW, WJY, JMO, and TMC guided the research. YCG, SGW, WJY, and TMC interpreted the results and drafted the manuscript. YCG, ZYZ, WC, SJC, MRZ, and ZQH contributed to revising subsequent versions of the manuscript. All authors read and approved the final manuscript.