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

作者：Jun Cai, Yanpeng Wu, Hengcong Liu, Zhu Deng, Lan Yi, Lwc Lai, Anna Funk, Marco Ajelli, Hongjie Yu · 发表于：Proceedings of the National Academy of Sciences · 年份：2025 · DOI：10.1073/pnas.2514157122 · 被引用次数：3 · 研究领域：COVID-19 epidemiological studies、SARS-CoV-2 and COVID-19 Research、Respiratory viral infections research

Following the late 2022 transition from its “dynamic zero-COVID” policy, China experienced a major nationwide severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron wave. To characterize the wave’s transmission dynamics, we used a Bayesian framework to fit a deterministic transmission model to two key data streams: reported COVID-19 daily case counts preceding the policy shift (up to November 11, 2022) and weekly virological and influenza-like illness (ILI) surveillance data afterward (through February 12, 2023). We estimated a nationwide cumulative infection attack rate reaching 87.8% (95% CrI: 75.9 to 93.3%) by mid-February 2023. Notably, 84.1% of the population became infected within just 1 mo following the full policy relaxation on December 7. The estimated time-varying effective reproduction number peaked at 5.69 (95% CrI: 4.56 to 6.85) on December 8, 2022. Although transmission intensity increased during the Spring Festival travel rush (Chunyun), widespread population immunity prevented a subsequent wave. Prior to the Chunyun period, distinct relationships emerged: Estimated transmission rates showed a significant positive correlation with long-term population behavioral response coefficient (reflecting cumulative infections; Pearson correlation: ρ = 0.92, P < 0.001), while mobility patterns correlated positively with short-term behavioral response coefficient (reflecting current infection prevalence; Pearson correlation: ρ = 0.87, P < 0.001). These ...