Assessment of the impacts of public health and social measures on influenza activity during the COVID-19 pandemic from 2020 to 2022 in Beijing, China: a modelling study.

INTRODUCTION

Understanding the impact of public health and social measures (PHSMs) on influenza transmission is crucial for developing effective influenza prevention and control strategies.

METHODS

This modeling study analyzed data from 2017 to 2022, in Beijing, China. Weekly influenza positive rate and influenza-like rate were incorporated to quantify the community-level influenza activities. The effective reproduction number and influenza attack rate were estimated using a branching process model and a transmission dynamics model, respectively. The impact of PHSMs was quantified through log-linear regression and counterfactual simulations under varying PHSM scenarios.

RESULTS

The transmissibility of influenza decreased by 68.41% (95%CI: 52.43, 78.80) in 2020, 67.07% (95%CI: 50.80, 77.89) in 2021 and 79.08% (95%CI: 63.18, 88.06) in 2022, and the attack rate dropped by 93.47% (95%CI: 85.86, 95.78), 95.37% (95%CI: 94.30, 96.89) and 71.61% (95%CI: 42.96, 81.24) over the same period, primarily due to the PHSMs. The simulation shows that strict PHSMs effectively suppressed the current flu epidemic effectively. When susceptible individuals drop to 50%, a relaxed strategy results in a smaller rebound in the next flu season, with epidemic sizes increasing to 1.18 (1.10, 1.30), 1.41 (1.20, 1.54), and 1.54 (1.35, 1.55) for relaxed, moderate, and strict measures, respectively.

CONCLUSIONS

Our study confirms the suppressive effect of coronavirus disease 2019 PHSMs on influenza transmission in Beijing. However, the relaxation of these measures' triggers resurgence, emphasizing the need for adaptive control strategies tailored to the population susceptibility and epidemic dynamics.