Pacific Biological Station, Fisheries and Oceans Canada, Nanaimo, Canada.
Department of Biology, University of Victoria, Victoria, Canada.
PLoS Comput Biol. 2020 Dec 3;16(12):e1008274. doi: 10.1371/journal.pcbi.1008274. eCollection 2020 Dec.
Extensive non-pharmaceutical and physical distancing measures are currently the primary interventions against coronavirus disease 2019 (COVID-19) worldwide. It is therefore urgent to estimate the impact such measures are having. We introduce a Bayesian epidemiological model in which a proportion of individuals are willing and able to participate in distancing, with the timing of distancing measures informed by survey data on attitudes to distancing and COVID-19. We fit our model to reported COVID-19 cases in British Columbia (BC), Canada, and five other jurisdictions, using an observation model that accounts for both underestimation and the delay between symptom onset and reporting. We estimated the impact that physical distancing (social distancing) has had on the contact rate and examined the projected impact of relaxing distancing measures. We found that, as of April 11 2020, distancing had a strong impact in BC, consistent with declines in reported cases and in hospitalization and intensive care unit numbers; individuals practising physical distancing experienced approximately 0.22 (0.11-0.34 90% CI [credible interval]) of their normal contact rate. The threshold above which prevalence was expected to grow was 0.55. We define the "contact ratio" to be the ratio of the estimated contact rate to the threshold rate at which cases are expected to grow; we estimated this contact ratio to be 0.40 (0.19-0.60) in BC. We developed an R package 'covidseir' to make our model available, and used it to quantify the impact of distancing in five additional jurisdictions. As of May 7, 2020, we estimated that New Zealand was well below its threshold value (contact ratio of 0.22 [0.11-0.34]), New York (0.60 [0.43-0.74]), Washington (0.84 [0.79-0.90]) and Florida (0.86 [0.76-0.96]) were progressively closer to theirs yet still below, but California (1.15 [1.07-1.23]) was above its threshold overall, with cases still rising. Accordingly, we found that BC, New Zealand, and New York may have had more room to relax distancing measures than the other jurisdictions, though this would need to be done cautiously and with total case volumes in mind. Our projections indicate that intermittent distancing measures-if sufficiently strong and robustly followed-could control COVID-19 transmission. This approach provides a useful tool for jurisdictions to monitor and assess current levels of distancing relative to their threshold, which will continue to be essential through subsequent waves of this pandemic.
目前，广泛的非药物和物理隔离措施是全球对抗 2019 年冠状病毒病（COVID-19）的主要干预措施。因此，迫切需要估计这些措施的影响。我们引入了一种贝叶斯流行病学模型，其中一部分人愿意并能够参与隔离，隔离措施的时间由对隔离和 COVID-19 的态度的调查数据告知。我们使用一种观察模型将我们的模型拟合到不列颠哥伦比亚省（BC）和其他五个司法管辖区的报告 COVID-19 病例，该模型考虑了症状出现和报告之间的低估和延迟。我们估计了物理隔离（社交隔离）对接触率的影响，并研究了放松隔离措施的预计影响。我们发现，截至 2020 年 4 月 11 日，在不列颠哥伦比亚省，隔离措施的影响非常大，这与报告病例以及住院和重症监护病房数量的下降相一致；实施身体隔离的个人经历了大约 0.22（0.11-0.34，90%可信区间[CI]）的正常接触率。预期流行率增加的阈值为 0.55。我们将“接触比”定义为估计的接触率与预期病例增长的阈值率之比；我们在不列颠哥伦比亚省估计该接触比为 0.40（0.19-0.60）。我们开发了一个名为“covidseir”的 R 包来提供我们的模型，并使用它来量化五个额外司法管辖区的隔离措施的影响。截至 2020 年 5 月 7 日，我们估计新西兰远低于其阈值（接触比为 0.22[0.11-0.34]），纽约（0.60[0.43-0.74]）、华盛顿（0.84[0.79-0.90]）和佛罗里达州（0.86[0.76-0.96]）接近但仍低于其阈值，但加利福尼亚州（1.15[1.07-1.23]）总体上超过了其阈值，病例仍在上升。因此，我们发现不列颠哥伦比亚省、新西兰和纽约可能比其他司法管辖区有更多的空间放松隔离措施，但这需要谨慎行事，并考虑到总病例数量。我们的预测表明，如果间歇性隔离措施足够强大并且得到严格遵守，可能会控制 COVID-19 的传播。这种方法为司法管辖区提供了一个有用的工具，用于监测和评估相对于其阈值的当前隔离水平，这在随后的疫情浪潮中仍然至关重要。
