Retrospective estimation of the time-varying effective reproduction number for a COVID-19 outbreak in Shenyang, China: An observational study

The time-varying effective reproduction number Re(t) is essential for designing and adjusting public health responses. Retrospective analysis of Re(t) helps to evaluate health emergency capabilities. We conducted this study to estimate the Re(t) of the Corona Virus Disease 2019 (COVID-19) outbreak caused by SARS-CoV-2 Omicron in Shenyang, China. Data on the daily incidence of this Corona Virus Disease 2019 outbreak between March 5, 2022, and April 25, 2022, in Shenyang, China, were downloaded from the Nationwide Notifiable Infectious Diseases Reporting Information System. Infector-infectee pairs were identified through epidemiological investigation. Re(t) was estimated by R-studio Package "EpiEstim" based on Bayesian framework through parameter and nonparametric method, respectively. About 1134 infections were found in this outbreak, with 20 confirmed cases and 1124 asymptomatic infections. Fifty-four infector-infectee pairs were identified and formed a serial interval list, and 15 infector-infectee pairs were included in the generation time table. Re(t) calculated by parameter and nonparametric method all peaked on March 17, 2022, with a value of 2.58 and 2.54 and decreased to <1 after March 28, 2022. There was no statistical difference in the Re(t) distribution calculated using the 2 methods (t = 0.001, P > .05). The present study indicated that the decisive response of Shenyang, China, played a significant role in preventing the spread of the epidemic, and the retrospective analysis provided novel insights into the outbreak response to future public health emergencies.