Laboratory of Multiscale Studies in Building Physics, Empa, Dübendorf, Switzerland.
Department of Health Sciences and Technology, Swiss Federal Institute of Technology, Zürich, Switzerland.
PLoS One. 2021 Nov 30;16(11):e0259018. doi: 10.1371/journal.pone.0259018. eCollection 2021.
A variety of mitigation strategies have been employed against the Covid-19 pandemic. Social distancing is still one of the main methods to reduce spread, but it entails a high toll on personal freedom and economic life. Alternative mitigation strategies that do not come with the same problems but are effective at preventing disease spread are therefore needed. Repetitive mass-testing using PCR assays for viral RNA has been suggested, but as a stand-alone strategy this would be prohibitively resource intensive. Here, we suggest a strategy that aims at targeting the limited resources available for viral RNA testing to subgroups that are more likely than the average population to yield a positive test result. Importantly, these pre-selected subgroups include symptom-free people. By testing everyone in these subgroups, in addition to symptomatic cases, large fractions of pre- and asymptomatic people can be identified, which is only possible by testing-based mitigation. We call this strategy smart testing (ST). In principle, pre-selected subgroups can be found in different ways, but for the purpose of this study we analyze a pre-selection procedure based on cheap and fast virus antigen tests. We quantify the potential reduction of the epidemic reproduction number by such a two-stage ST strategy. In addition to a scenario where such a strategy is available to the whole population, we analyze local applications, e.g. in a country, company, or school, where the tested subgroups are also in exchange with the untested population. Our results suggest that a two-stage ST strategy can be effective to curb pandemic spread, at costs that are clearly outweighed by the economic benefit. It is technically and logistically feasible to employ such a strategy, and our model predicts that it is even effective when applied only within local groups. We therefore recommend adding two-stage ST to the portfolio of available mitigation strategies, which allow easing social distancing measures without compromising public health.
针对新冠疫情，人们已经采取了多种缓解策略。保持社交距离仍然是减少病毒传播的主要方法之一，但这会极大地影响个人自由和经济生活。因此，需要寻找其他非强制性的缓解策略，这些策略既要有效预防疾病传播，又不能带来同样的问题。有人建议重复使用聚合酶链反应（PCR）检测病毒 RNA 进行大规模检测，但作为一种独立的策略，这种方法的资源需求过高。在这里，我们提出了一种策略，旨在将有限的病毒 RNA 检测资源集中用于更有可能产生阳性检测结果的亚组，而不是平均人群。重要的是，这些预先选定的亚组包括无症状人群。通过对这些亚组中的所有人进行检测，包括有症状的病例，以及很大一部分未出现症状的人和无症状感染者都可以被识别，这只有通过基于检测的缓解策略才能实现。我们将这种策略称为智能检测（ST）。原则上，可以通过不同的方式找到预先选定的亚组，但为了进行这项研究，我们分析了一种基于廉价和快速病毒抗原检测的预筛选程序。我们量化了这种两阶段 ST 策略对降低传染病传播系数的潜在影响。除了在整个人群中实施这种策略的情况外，我们还分析了在局部环境中的应用，例如在一个国家、公司或学校中，对测试的亚组与未测试的人群进行交换的情况。研究结果表明，两阶段 ST 策略可以有效地遏制疫情传播，其成本明显低于经济效益。这种策略在技术和后勤上是可行的，我们的模型预测，即使仅在局部群体中实施，它也是有效的。因此，我们建议将两阶段 ST 策略纳入现有的缓解策略组合中，这可以在不损害公众健康的情况下放宽社交距离措施。
