School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada;
School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada.
Proc Natl Acad Sci U S A. 2020 Sep 29;117(39):24575-24580. doi: 10.1073/pnas.2014385117. Epub 2020 Sep 4.
In the late stages of an epidemic, infections are often sporadic and geographically distributed. Spatially structured stochastic models can capture these important features of disease dynamics, thereby allowing a broader exploration of interventions. Here we develop a stochastic model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission among an interconnected group of population centers representing counties, municipalities, and districts (collectively, "counties"). The model is parameterized with demographic, epidemiological, testing, and travel data from Ontario, Canada. We explore the effects of different control strategies after the epidemic curve has been flattened. We compare a local strategy of reopening (and reclosing, as needed) schools and workplaces county by county, according to triggers for county-specific infection prevalence, to a global strategy of province-wide reopening and reclosing, according to triggers for province-wide infection prevalence. For trigger levels that result in the same number of COVID-19 cases between the two strategies, the local strategy causes significantly fewer person-days of closure, even under high intercounty travel scenarios. However, both cases and person-days lost to closure rise when county triggers are not coordinated and when testing rates vary among counties. Finally, we show that local strategies can also do better in the early epidemic stage, but only if testing rates are high and the trigger prevalence is low. Our results suggest that pandemic planning for the far side of the COVID-19 epidemic curve should consider local strategies for reopening and reclosing.
在疫情后期，感染通常是零星发生且分布在地理上的。空间结构随机模型可以捕捉到疾病动态的这些重要特征，从而可以更广泛地探索干预措施。在这里，我们开发了一种严重急性呼吸系统综合症冠状病毒 2（SARS-CoV-2）在代表县、市和区（统称“县”）的相互关联的人群中心之间传播的随机模型。该模型使用来自加拿大安大略省的人口统计、流行病学、检测和旅行数据进行参数化。我们探讨了在疫情曲线变平时采用不同控制策略的效果。我们根据县内感染流行率的触发因素，对每个县进行重新开放（并根据需要重新关闭）学校和工作场所的局部策略，与根据全省感染流行率的触发因素进行全省重新开放和重新关闭的全局策略进行比较。对于导致两种策略下 COVID-19 病例数量相同的触发水平，局部策略会导致关闭的人数天数明显减少，即使在县际旅行场景较高的情况下也是如此。但是，如果县际触发因素不协调且各县的检测率存在差异，则病例数和因关闭而损失的人数都会增加。最后，我们表明，即使检测率较高且触发流行率较低，局部策略也可以在疫情早期阶段表现更好。我们的结果表明，COVID-19 疫情曲线远侧的大流行规划应考虑重新开放和重新关闭的局部策略。
