Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Lancet Public Health. 2020 May;5(5):e261-e270. doi: 10.1016/S2468-2667(20)30073-6. Epub 2020 Mar 25.
In December, 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus, emerged in Wuhan, China. Since then, the city of Wuhan has taken unprecedented measures in response to the outbreak, including extended school and workplace closures. We aimed to estimate the effects of physical distancing measures on the progression of the COVID-19 epidemic, hoping to provide some insights for the rest of the world.
To examine how changes in population mixing have affected outbreak progression in Wuhan, we used synthetic location-specific contact patterns in Wuhan and adapted these in the presence of school closures, extended workplace closures, and a reduction in mixing in the general community. Using these matrices and the latest estimates of the epidemiological parameters of the Wuhan outbreak, we simulated the ongoing trajectory of an outbreak in Wuhan using an age-structured susceptible-exposed-infected-removed (SEIR) model for several physical distancing measures. We fitted the latest estimates of epidemic parameters from a transmission model to data on local and internationally exported cases from Wuhan in an age-structured epidemic framework and investigated the age distribution of cases. We also simulated lifting of the control measures by allowing people to return to work in a phased-in way and looked at the effects of returning to work at different stages of the underlying outbreak (at the beginning of March or April).
Our projections show that physical distancing measures were most effective if the staggered return to work was at the beginning of April; this reduced the median number of infections by more than 92% (IQR 66-97) and 24% (13-90) in mid-2020 and end-2020, respectively. There are benefits to sustaining these measures until April in terms of delaying and reducing the height of the peak, median epidemic size at end-2020, and affording health-care systems more time to expand and respond. However, the modelled effects of physical distancing measures vary by the duration of infectiousness and the role school children have in the epidemic.
Restrictions on activities in Wuhan, if maintained until April, would probably help to delay the epidemic peak. Our projections suggest that premature and sudden lifting of interventions could lead to an earlier secondary peak, which could be flattened by relaxing the interventions gradually. However, there are limitations to our analysis, including large uncertainties around estimates of R and the duration of infectiousness.
Bill & Melinda Gates Foundation, National Institute for Health Research, Wellcome Trust, and Health Data Research UK.
2019 年 12 月，新型冠状病毒（SARS-CoV-2）在中国武汉出现。此后，武汉市政府采取了前所未有的措施应对疫情，包括延长学校和工作场所的关闭时间。我们旨在评估身体距离措施对 COVID-19 疫情发展的影响，希望为世界其他地区提供一些见解。
为了研究人口混合变化如何影响武汉的疫情发展，我们使用了武汉特定位置的综合接触模式，并在学校关闭、延长工作场所关闭以及减少社区混合的情况下对这些模式进行了调整。使用这些矩阵和武汉疫情最新的流行病学参数估计，我们使用年龄结构易感-暴露-感染-清除（SEIR）模型模拟了几种身体距离措施下武汉疫情的持续轨迹。我们将从传播模型中得出的最新疫情参数估计拟合到武汉当地和国际输出病例的数据中，在年龄结构的疫情框架中研究病例的年龄分布。我们还模拟了通过分阶段让人们返回工作岗位来解除控制措施的情况，并研究了在疫情发展的不同阶段（3 月初或 4 月初）返回工作的效果。
我们的预测表明，如果分阶段返回工作时间在 4 月初，身体距离措施将最为有效；这将使 2020 年中期和年末的感染中位数分别减少 92%（66-97）和 24%（13-90）。在 4 月之前继续实施这些措施可以延迟和减少高峰期的峰值、2020 年末的疫情规模中位数，并为医疗保健系统提供更多的时间来扩大和应对。然而，身体距离措施的模拟效果因传染性的持续时间和学龄儿童在疫情中的作用而有所不同。
如果武汉的活动限制持续到 4 月，可能有助于延迟疫情高峰。我们的预测表明，过早和突然取消干预措施可能导致更早的二次高峰，而逐渐放松干预措施可以使二次高峰变平。然而，我们的分析存在局限性，包括对 R 和传染性持续时间的估计存在较大不确定性。
比尔及梅琳达·盖茨基金会、英国国家卫生研究院、惠康信托基金会和英国健康数据研究中心。
