Department of Integrative Biology, University of California, Berkeley, United States; Department of Ecology and Evolution, University of Chicago, United States.
Center for Computational Biology, College of Engineering, University of California, Berkeley, United States.
Epidemics. 2021 Dec;37:100527. doi: 10.1016/j.epidem.2021.100527. Epub 2021 Nov 15.
The high proportion of transmission events derived from asymptomatic or presymptomatic infections make SARS-CoV-2, the causative agent in COVID-19, difficult to control through the traditional non-pharmaceutical interventions (NPIs) of symptom-based isolation and contact tracing. As a consequence, many US universities developed asymptomatic surveillance testing labs, to augment NPIs and control outbreaks on campus throughout the 2020-2021 academic year (AY); several of those labs continue to support asymptomatic surveillance efforts on campus in AY2021-2022. At the height of the pandemic, we built a stochastic branching process model of COVID-19 dynamics at UC Berkeley to advise optimal control strategies in a university environment. Our model combines behavioral interventions in the form of group size limits to deter superspreading, symptom-based isolation, and contact tracing, with asymptomatic surveillance testing. We found that behavioral interventions offer a cost-effective means of epidemic control: group size limits of six or fewer greatly reduce superspreading, and rapid isolation of symptomatic infections can halt rising epidemics, depending on the frequency of asymptomatic transmission in the population. Surveillance testing can overcome uncertainty surrounding asymptomatic infections, with the most effective approaches prioritizing frequent testing with rapid turnaround time to isolation over test sensitivity. Importantly, contact tracing amplifies population-level impacts of all infection isolations, making even delayed interventions effective. Combination of behavior-based NPIs and asymptomatic surveillance also reduces variation in daily case counts to produce more predictable epidemics. Furthermore, targeted, intensive testing of a minority of high transmission risk individuals can effectively control the COVID-19 epidemic for the surrounding population. Even in some highly vaccinated university settings in AY2021-2022, asymptomatic surveillance testing offers an effective means of identifying breakthrough infections, halting onward transmission, and reducing total caseload. We offer this blueprint and easy-to-implement modeling tool to other academic or professional communities navigating optimal return-to-work strategies.
由于 SARS-CoV-2（导致 COVID-19 的病原体）引起的大量传播事件源自无症状或症状前感染，因此通过基于症状的隔离和接触者追踪等传统非药物干预措施（NPIs）难以控制该病毒。因此，许多美国大学在 2020-2021 学年（AY）期间开发了无症状监测检测实验室，以补充 NPIs 并控制校园内的疫情爆发；其中一些实验室在 2021-2022 学年 AY 期间继续支持校园内的无症状监测工作。在大流行高峰期，我们在加州大学伯克利分校建立了 COVID-19 动力学的随机分支过程模型，以提供大学环境下的最佳控制策略建议。我们的模型将群体规模限制等行为干预措施与基于症状的隔离和接触者追踪相结合，同时进行无症状监测检测。我们发现，行为干预措施是一种具有成本效益的控制疫情的方法：群体规模限制在 6 人或以下可大大减少超级传播，并且迅速隔离有症状的感染可以阻止疫情上升，具体取决于人群中无症状传播的频率。监测检测可以克服无症状感染的不确定性，最有效的方法是优先考虑频繁进行具有快速周转时间的检测，然后进行隔离，而不是提高检测灵敏度。重要的是，接触者追踪会放大所有感染隔离对人群的影响，即使是延迟的干预措施也能发挥作用。基于行为的 NPI 和无症状监测的组合还可以减少每日病例数的变化，从而产生更可预测的疫情。此外，对少数高传播风险个体进行有针对性的密集检测，可以有效地控制周围人群的 COVID-19 疫情。即使在 2021-2022 学年 AY 中一些接种率很高的大学环境中，无症状监测检测也是识别突破性感染、阻止传播以及减少总病例数的有效手段。我们为其他正在制定最佳复工策略的学术或专业社区提供了这一蓝图和易于实施的建模工具。
