Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, CNRS UMR 2000, Paris, France.
Infectious Diseases Department, Santé Publique France, Saint Maurice, France.
BMC Infect Dis. 2023 Mar 30;23(1):190. doi: 10.1186/s12879-023-08106-1.
Multiple factors shape the temporal dynamics of the COVID-19 pandemic. Quantifying their relative contributions is key to guide future control strategies. Our objective was to disentangle the individual effects of non-pharmaceutical interventions (NPIs), weather, vaccination, and variants of concern (VOC) on local SARS-CoV-2 transmission.
We developed a log-linear model for the weekly reproduction number (R) of hospital admissions in 92 French metropolitan departments. We leveraged (i) the homogeneity in data collection and NPI definitions across departments, (ii) the spatial heterogeneity in the timing of NPIs, and (iii) an extensive observation period (14 months) covering different weather conditions, VOC proportions, and vaccine coverage levels.
Three lockdowns reduced R by 72.7% (95% CI 71.3-74.1), 70.4% (69.2-71.6) and 60.7% (56.4-64.5), respectively. Curfews implemented at 6/7 pm and 8/9 pm reduced R by 34.3% (27.9-40.2) and 18.9% (12.04-25.3), respectively. School closures reduced R by only 4.9% (2.0-7.8). We estimated that vaccination of the entire population would have reduced R by 71.7% (56.4-81.6), whereas the emergence of VOC (mainly Alpha during the study period) increased transmission by 44.6% (36.1-53.6) compared with the historical variant. Winter weather conditions (lower temperature and absolute humidity) increased R by 42.2% (37.3-47.3) compared to summer weather conditions. Additionally, we explored counterfactual scenarios (absence of VOC or vaccination) to assess their impact on hospital admissions.
Our study demonstrates the strong effectiveness of NPIs and vaccination and quantifies the role of weather while adjusting for other confounders. It highlights the importance of retrospective evaluation of interventions to inform future decision-making.
多种因素塑造了 COVID-19 大流行的时间动态。量化它们的相对贡献是指导未来控制策略的关键。我们的目标是厘清非药物干预(NPI)、天气、疫苗接种和关注变体(VOC)对当地 SARS-CoV-2 传播的个体影响。
我们为 92 个法国大都市地区的住院人数每周繁殖数(R)开发了一个对数线性模型。我们利用了(i)部门之间数据收集和 NPI 定义的同质性,(ii)NPI 实施时间的空间异质性,以及(iii)涵盖不同天气条件、VOC 比例和疫苗接种水平的 14 个月的广泛观察期。
三次封锁分别将 R 降低了 72.7%(95%置信区间 71.3-74.1)、70.4%(69.2-71.6)和 60.7%(56.4-64.5)。晚上 6/7 点和 8/9 点实施的宵禁分别将 R 降低了 34.3%(27.9-40.2)和 18.9%(12.04-25.3)。学校关闭仅将 R 降低了 4.9%(2.0-7.8)。我们估计,为整个人口接种疫苗将使 R 降低 71.7%(56.4-81.6),而 VOC 的出现(在研究期间主要是 Alpha)使传播增加了 44.6%(36.1-53.6)与历史变体相比。冬季天气条件(较低的温度和绝对湿度)使 R 增加了 42.2%(37.3-47.3),与夏季天气条件相比。此外,我们还探索了反事实情景(不存在 VOC 或疫苗接种),以评估它们对住院人数的影响。
我们的研究表明 NPI 和疫苗接种的强大效果,并量化了天气的作用,同时调整了其他混杂因素。它强调了回顾性评估干预措施以告知未来决策的重要性。