Liu Hengcong, Cai Jun, Zhou Jiaxin, Xu Xiangyanyu, Ajelli Marco, Yu Hongjie
School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.
Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.
Infect Dis Model. 2024 Feb 28;9(2):519-526. doi: 10.1016/j.idm.2024.02.013. eCollection 2024 Jun.
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.
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.
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.
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.
2022年3月至6月,上海奥密克戎BA.2感染病例大幅激增。除了当时实施的标准干预措施外,还针对此次疫情实施了额外的干预措施。然而,这些干预措施对BA.2传播的影响仍不明确。
我们系统收集了这波疫情期间每日新增报告感染病例的数据,并采用贝叶斯方法估计每日有效繁殖数。从牛津大学新冠疫情政府应对追踪器中获取公共卫生应对措施的数据,并将其作为此次疫情期间实施干预措施的替代指标。我们使用对数线性回归模型评估这些干预措施对繁殖数的影响。此外,我们构建了一个BA.2传播的数学模型。通过结合回归模型和传播模型中干预措施的估计效果,我们估算了已实施的干预措施避免的感染和死亡人数。
我们发现干预措施的强度与感染病例数之间存在负相关(-0.0069,95%置信区间:0.0096至-0.0045)。如果在疫情期间没有加强干预措施,我们估计感染和死亡人数将增加22.6%(95%置信区间:22.4-22.8%),导致总共768,576例(95%置信区间:768,021-769,107)感染和722例(95%置信区间:722-723)死亡。如果在疫情期间未采取任何干预措施,我们估计感染和死亡人数将增加46.0%(95%置信区间:45.8-46.2%),导致总共915,099例(95%置信区间:914,639-915,518)感染和860例(95%置信区间:860-861)死亡。
我们的研究结果表明,2022年春季上海奥密克戎BA.2疫情期间采取的干预措施在减少新冠病毒传播和疾病负担方面是有效的。我们的研究结果强调了非药物干预措施在控制疫情暴发期间病例快速激增方面的重要性。