Assessing the impact of interventions on the major Omicron BA.2 outbreak in spring 2022 in Shanghai

IF 8.8 3区 医学 Q1 Medicine Infectious Disease Modelling Pub Date : 2024-02-28 DOI:10.1016/j.idm.2024.02.013
Hengcong Liu , Jun Cai , Jiaxin Zhou , Xiangyanyu Xu , Marco Ajelli , Hongjie Yu
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Abstract

Background

Shanghai experienced a significant surge in Omicron BA.2 infections from March to June 2022. In addition to the standard interventions in place at that time, additional interventions were implemented in response to the outbreak. However, the impact of these interventions on BA.2 transmission remains unclear.

Methods

We systematically collected data on the daily number of newly reported infections during this wave and utilized a Bayesian approach to estimate the daily effective reproduction number. Data on public health responses were retrieved from the Oxford COVID-19 Government Response Tracker and served as a proxy for the interventions implemented during this outbreak. Using a log-linear regression model, we assessed the impact of these interventions on the reproduction number. Furthermore, we developed a mathematical model of BA.2 transmission. By combining the estimated effect of the interventions from the regression model and the transmission model, we estimated the number of infections and deaths averted by the implemented interventions.

Results

We found a negative association (−0.0069, 95% CI: 0.0096 to −0.0045) between the level of interventions and the number of infections. If interventions did not ramp up during the outbreak, we estimated that the number of infections and deaths would have increased by 22.6% (95% CI: 22.4–22.8%), leading to a total of 768,576 (95% CI: 768,021-769,107) infections and 722 (95% CI: 722–723) deaths. If no interventions were deployed during the outbreak, we estimated that the number of infections and deaths would have increased by 46.0% (95% CI: 45.8–46.2%), leading to a total of 915,099 (95% CI: 914,639-915,518) infections and 860 (95% CI: 860–861) deaths.

Conclusion

Our findings suggest that the interventions adopted during the Omicron BA.2 outbreak in spring 2022 in Shanghai were effective in reducing SARS-CoV-2 transmission and disease burden. Our findings emphasize the importance of non-pharmacological interventions in controlling quick surges of cases during epidemic outbreaks.

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评估干预措施对 2022 年春季上海大规模爆发的 Omicron BA.2 疫情的影响
背景2022年3月至6月期间,上海的Omicron BA.2感染率大幅上升。除了当时实施的标准干预措施外,还针对疫情实施了额外的干预措施。方法我们系统地收集了这一波疫情中每日新报告感染人数的数据,并利用贝叶斯方法估算了每日有效繁殖人数。公共卫生响应数据来自牛津 COVID-19 政府响应追踪系统,可作为此次疫情中实施的干预措施的替代数据。通过对数线性回归模型,我们评估了这些干预措施对繁殖数量的影响。此外,我们还建立了 BA.2 传播的数学模型。结果我们发现,干预水平与感染数量之间存在负相关(-0.0069,95% CI:0.0096 至 -0.0045)。如果在疫情爆发期间不加大干预力度,我们估计感染人数和死亡人数将增加 22.6% (95% CI: 22.4-22.8%),导致总计 768,576 (95% CI: 768,021-769,107) 例感染和 722 (95% CI: 722-723) 例死亡。结论我们的研究结果表明,在 2022 年春季上海爆发的 Omicron BA.2 疫情中采取的干预措施有效地减少了 SARS-CoV-2 的传播和疾病负担。我们的研究结果强调了非药物干预措施在控制疫情爆发期间病例快速激增方面的重要性。
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来源期刊
Infectious Disease Modelling
Infectious Disease Modelling Mathematics-Applied Mathematics
CiteScore
17.00
自引率
3.40%
发文量
73
审稿时长
17 weeks
期刊介绍: Infectious Disease Modelling is an open access journal that undergoes peer-review. Its main objective is to facilitate research that combines mathematical modelling, retrieval and analysis of infection disease data, and public health decision support. The journal actively encourages original research that improves this interface, as well as review articles that highlight innovative methodologies relevant to data collection, informatics, and policy making in the field of public health.
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