The rapid and efficient strategy for SARS-CoV-2 Omicron transmission control: analysis of outbreaks at the city level

Abstract

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron (B.1.1.529) variant is highly transmissible with potential immune escape. Hence, control measures are continuously being optimized to guard against large-scale coronavirus disease 2019 (COVID-19) outbreaks. This study aimed to explore the relationship between the intensity of control measures in response to different SARS-CoV-2 variants and the degree of outbreak control at city level.

Methods: A retrospective study was conducted in 49 cities with COVID-19 outbreaks between January 2020 and June 2022. Epidemiological data on COVID-19 were extracted from the National Health Commission, People's Republic of China, and the population flow data were sourced from the Baidu migration data provided by the Baidu platform. Outbreak control was quantified by calculating the degree of infection growth and the time-varying reproduction number ([Formula: see text]). The intensity of the outbreak response was quantified by calculating the reduction in population mobility during the outbreak period. Correlation and regression analyses of the intensity of the control measures and the degree of outbreak control for the Omicron variant and non-Omicron mutants were conducted, respectively.

Results: Overall, 65 outbreaks occurred in 49 cities in China from January 2020 to June 2022. Of them, 66.2% were Omicron outbreaks and 33.8% were non-Omicron outbreaks. The intensity of the control measures was positively correlated with the degree of outbreak control (r = 0.351, P = 0.03). The degree of reduction in population mobility was negatively correlated with the R_t value (r = - 0.612, P < 0.01). Therefore, under the same control measure intensity, the number of new daily Omicron infections was 6.04 times higher than those attributed to non-Omicron variants, and the R_t value of Omicron outbreaks was 2.6 times higher than that of non-Omicron variants. In addition, the duration of non-Omicron variant outbreaks was shorter than that of the outbreaks caused by the Omicron variant (23.0 ± 10.7, 32.9 ± 16.3, t = 2.243, P = 0.031).

Conclusions: Greater intensity of control measures was associated with more effective outbreak control. Thus, in response to the Omicron variant, the management to restrict population movement should be used to control its spread quickly, especially in the case of community transmission occurs widely. Faster than is needed for non-Omicron variants, and decisive control measures should be imposed and dynamically adjusted in accordance with the evolving epidemic situation.

Keywords: COVID-19; Outbreak; Population flow; SARS-CoV-2; Time-varying reproduction number.

Fig. 1

Flow chart demonstrating the quantification of the strength of outbreak control and intensity of control measures

Fig. 2

The relationship between intensity of outbreak with infection case control and the degree of population control in Omicron pandemic and non-Omicron pandemic. a The scatter plot of infection case control with log transform and population flow index control in non-Omicron pandemic. b The scatter plot of infection case control with log transform and population flow index control in Omicron pandemic

Fig. 3

The daily number of reported infection and the population flow index of the non-Omicron outbreak in Xi’an. a Simulations of daily cases in by time-varying reproduction number according to daily number of reported infection, the bar chart is the daily number of reported infected individuals. b Model estimating the effective reproductive numbers (R_t) in each day by EpiNow2 package. c The daily number of reported infections (bar plot) and population flow index (pink line)

Fig. 4

The direct relationship between intensity of outbreak with time-varying reproduction number (R_t) and the degree of population control in Omicron pandemic and non-Omicron pandemic. a The scatter plot of time-varying reproduction number with population control intensity in non-Omicron pandemic. b The scatter plot of time-varying reproduction number with population control intensity in Omicron pandemic

Fig. 5

The daily number of reported infections and population flow index of the Omicron outbreak in Shanghai. a Simulations of daily infections in by time-varying reproduction number according to daily number of reported infections, the bar chart is the daily number of reported infections. b Model estimating the effective reproductive numbers (R_t) in each day by EpiNow2 package. c The daily number of reported infections (bar plot) and population flow index (pink line)