‘Dynamic zero-COVID’ policy and viral clearance during an omicron wave in Tianjin, China: a city-wide retrospective observational study

Author:

Feng Yahui,Shao Hongxia,Gong Xiaowen,Song Zhen,Xie Yan,Qi Saibing,Shi Lixia,Hu Yu,Liu Xueou,Liu Xiaolong,Shen Qiujin,Wu Qi,Cheng Tao,Jiang Wentao,Wu Junping,Chen JunrenORCID

Abstract

ObjectiveTo report how the Chinese mainland battled its first omicron wave, which happened in Tianjin, a metropolis with 14 million residents. We also sought to better understand how clinical features affected the timing of viral clearance.DesignA retrospective study of the omicron wave in Tianjin between 8 January 2022 and 3 March 2022.SettingExcept for the first cases on 8 January, all the omicron cases were identified through PCR mass testing in the residential communities. Residential quarantine and serial PCR mass testing were dynamically adjusted according to the trends of new cases.ParticipantsAll the 417 consecutive PCR-positive cases identified through mass screening of the entire city’s 14 million residents. 45.3% of the cases were male, and the median age was 37 (range 0.3–90). 389 (93%) cases had complete data for analysing the correlation between clinical features and the timing of viral clearance.Main outcome and measureTime to viral clearance.ResultsTianjin initiated the ‘dynamic zero-COVID’ policy very early, that is, when daily new case number was ≈0.4 cases per 1 000 000 residents. Daily new cases dropped to <5 after 3 February, and the number of affected residential subdivisions dropped to ≤2 after 13 February. 64% (267/417) of the cases had no or mild symptoms. The median interval from hospital admission to viral clearance was 10 days (range 3–28). An exploratory analysis identified a feature cluster associated with earlier viral clearance, with HRs of 3.56 (95% CI 1.66 to 7.63) and 3.15 (95% CI 1.68 to 5.91) in the training and validation sets, respectively.ConclusionsThe ‘dynamic zero-COVID’ policy can suppress an omicron wave within a month. It might be possible to predict in advance which cases will require shorter periods of isolation based on their clinical features.

Funder

Haihe Laboratory of Cell Ecosystem Innovation Fund

Publisher

BMJ

Subject

General Medicine

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