Lemaitre Joseph C, Perez-Saez Javier, Azman Andrew S, Rinaldo Andrea, Fellay Jacques
Laboratory of Ecohydrology, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Switzerland.
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Swiss Med Wkly. 2020 May 30;150:w20295. doi: 10.4414/smw.2020.20295. eCollection 2020 May 18.
Following the rapid dissemination of COVID-19 cases in Switzerland, large-scale non-pharmaceutical interventions (NPIs) were implemented by the cantons and the federal government between 28 February and 20 March 2020. Estimates of the impact of these interventions on SARS-CoV-2 transmission are critical for decision making in this and future outbreaks. We here aim to assess the impact of these NPIs on disease transmission by estimating changes in the basic reproduction number (R0) at national and cantonal levels in relation to the timing of these NPIs. We estimated the time-varying R0 nationally and in eleven cantons by fitting a stochastic transmission model explicitly simulating within-hospital dynamics. We used individual-level data from more than 1000 hospitalised patients in Switzerland and public daily reports of hospitalisations and deaths. We estimated the national R0 to be 2.8 (95% confidence interval 2.1–3.8) at the beginning of the epidemic. Starting from around 7 March, we found a strong reduction in time-varying R0 with a 86% median decrease (95% quantile range [QR] 79–90%) to a value of 0.40 (95% QR 0.3–0.58) in the period of 29 March to 5 April. At the cantonal level, R0 decreased over the course of the epidemic between 53% and 92%. Reductions in time-varying R0 were synchronous with changes in mobility patterns as estimated through smartphone activity, which started before the official implementation of NPIs. We inferred that most of the reduction of transmission is attributable to behavioural changes as opposed to natural immunity, the latter accounting for only about 4% of the total reduction in effective transmission. As Switzerland considers relaxing some of the restrictions of social mixing, current estimates of time-varying R0 well below one are promising. However, as of 24 April 2020, at least 96% (95% QR 95.7–96.4%) of the Swiss population remains susceptible to SARS-CoV-2. These results warrant a cautious relaxation of social distance practices and close monitoring of changes in both the basic and effective reproduction numbers.
在瑞士新冠病毒病（COVID-19）病例迅速传播之后，各州和联邦政府于2020年2月28日至3月20日实施了大规模非药物干预措施（NPIs）。评估这些干预措施对严重急性呼吸综合征冠状病毒2（SARS-CoV-2）传播的影响对于此次及未来疫情的决策至关重要。我们在此旨在通过估计全国和各州层面基本再生数（R0）相对于这些非药物干预措施实施时间的变化，来评估这些非药物干预措施对疾病传播的影响。我们通过拟合一个明确模拟医院内动态的随机传播模型，估计了全国和11个州随时间变化的R0。我们使用了来自瑞士1000多名住院患者的个体层面数据以及住院和死亡情况的每日公开报告。我们估计疫情开始时全国的R0为2.8（95%置信区间2.1 - 3.8）。从3月7日左右开始，我们发现随时间变化的R0大幅下降，中位数下降86%（95%分位数范围[QR]79 - 90%），在3月29日至4月5日期间降至0.40（95% QR 0.3 - 0.58）。在州层面，R0在疫情过程中下降了53%至92%。随时间变化的R0下降与通过智能手机活动估计的出行模式变化同步，出行模式变化在非药物干预措施正式实施之前就已开始。我们推断传播的大部分减少归因于行为变化，而非自然免疫，后者仅占有效传播总减少量的约4%。由于瑞士考虑放宽一些社交接触限制，当前随时间变化的R0远低于1的估计结果令人鼓舞。然而，截至2020年4月24日，至少96%（95% QR 95.7 - 96.4%）的瑞士人口仍易感染SARS-CoV-2。这些结果表明应谨慎放宽社交距离措施，并密切监测基本再生数和有效再生数的变化。
