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China's post-zero-COVID Omicron wave: A Bayesian analysis.

Jun Cai1, Yanpeng Wu2, Hengcong Liu1,3

  • 1Department of Epidemiology, School of Public Health, Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai 200032, China.

Proceedings of the National Academy of Sciences of the United States of America
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China

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • China shifted from its "dynamic zero-COVID" policy in late 2022, leading to a widespread Omicron wave of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
  • Understanding the transmission dynamics of this wave is crucial for informing future public health strategies.

Purpose of the Study:

  • To characterize the transmission dynamics of the SARS-CoV-2 Omicron wave in China following the policy shift.
  • To estimate the cumulative infection attack rate and effective reproduction number during the wave.
  • To investigate the influence of behavioral factors and mobility on transmission.

Main Methods:

  • A Bayesian framework was employed to fit a deterministic transmission model.
  • The model was calibrated using reported COVID-19 daily case counts and weekly virological/influenza-like illness (ILI) surveillance data.
  • Time-varying behavioral response coefficients were incorporated, reflecting cumulative infections and current prevalence.

Main Results:

  • An estimated cumulative infection attack rate of 87.8% was reached by mid-February 2023, with 84.1% of the population infected within one month of policy relaxation.
  • The effective reproduction number peaked at 5.69 on December 8, 2022.
  • Transmission rates correlated significantly with long-term behavioral responses (ρ = 0.92) and mobility patterns with short-term responses (ρ = 0.87) prior to the Spring Festival travel rush (Chunyun).

Conclusions:

  • The SARS-CoV-2 Omicron wave in China was extensive, with a high cumulative infection attack rate.
  • Population immunity largely prevented a subsequent wave despite increased transmission during Chunyun.
  • Incorporating time-varying behavioral factors into epidemic models is essential for accurately describing transmission dynamics, particularly after abrupt policy changes.