Pacific Biological Station, Fisheries and Oceans Canada, Nanaimo, BC, Canada; Department of Mathematics, Simon Fraser University, Burnaby, BC, Canada.
Department of Mathematics, Simon Fraser University, Burnaby, BC, Canada.
Epidemics. 2021 Jun;35:100453. doi: 10.1016/j.epidem.2021.100453. Epub 2021 Mar 18.
Following successful non-pharmaceutical interventions (NPI) aiming to control COVID-19, many jurisdictions reopened their economies and borders. As little immunity had developed in most populations, re-establishing higher contact carried substantial risks, and therefore many locations began to see resurgence in COVID-19 cases. We present a Bayesian method to estimate the leeway to reopen, or alternatively the strength of change required to re-establish COVID-19 control, in a range of jurisdictions experiencing different COVID-19 epidemics. We estimated the timing and strength of initial control measures such as widespread distancing and compared the leeway jurisdictions had to reopen immediately after NPI measures to later estimates of leeway. Finally, we quantified risks associated with reopening and the likely burden of new cases due to introductions from other jurisdictions. We found widely varying leeway to reopen. After initial NPI measures took effect, some jurisdictions had substantial leeway (e.g., Japan, New Zealand, Germany) with > 0.99 probability that contact rates were below 80% of the threshold for epidemic growth. Others had little leeway (e.g., the United Kingdom, Washington State) and some had none (e.g., Sweden, California). For most such regions, increases in contact rate of 1.5-2 fold would have had high (> 0.7) probability of exceeding past peak sizes. Most jurisdictions experienced June-August trajectories consistent with our projections of contact rate increases of 1-2-fold. Under such relaxation scenarios for some regions, we projected up to ∼100 additional cases if just one case were imported per week over six weeks, even between jurisdictions with comparable COVID-19 risk. We provide an R package covidseir to enable jurisdictions to estimate leeway and forecast cases under different future contact patterns. Estimates of leeway can establish a quantitative basis for decisions about reopening. We recommend a cautious approach to reopening economies and borders, coupled with strong monitoring for changes in transmission.
在成功实施旨在控制 COVID-19 的非药物干预(NPI)措施之后,许多司法管辖区重新开放了其经济和边界。由于大多数人群中几乎没有产生免疫力,重新建立更高的接触水平带来了巨大的风险,因此许多地方的 COVID-19 病例再次出现。我们提出了一种贝叶斯方法来估计重新开放的余地,或者更确切地说,在经历不同 COVID-19 疫情的各种司法管辖区中,重新建立 COVID-19 控制所需的变化力度。我们估计了广泛隔离等初始控制措施的实施时间和力度,并将这些措施对 COVID-19 控制的影响与之后对余地的估计进行了比较。最后,我们量化了重新开放相关的风险以及由于其他司法管辖区的引入而导致新病例的可能负担。我们发现重新开放的余地差异很大。在最初的 NPI 措施生效后,一些司法管辖区(例如日本、新西兰、德国)有很大的余地,即接触率低于引发疫情增长的阈值的 80%的概率超过 0.99。其他地区余地很小(例如英国、华盛顿州),有些地区则没有余地(例如瑞典、加利福尼亚州)。对于大多数这样的地区,接触率增加 1.5-2 倍将极有可能超过过去的高峰值。大多数司法管辖区在 6 月至 8 月期间的轨迹与我们对接触率增加 1-2 倍的预测一致。在某些地区放松这些限制的情况下,即使在具有相似 COVID-19 风险的司法管辖区之间,每周输入一例病例,也可能会导致多达 100 例的新增病例。我们提供了一个名为 covidseir 的 R 包,以便司法管辖区能够根据不同的未来接触模式估计余地并预测病例。余地的估计可以为重新开放经济和边界的决策提供定量依据。我们建议谨慎行事,重新开放经济和边界,并加强对传播变化的监测。