Widespread, repeated testing using rapid antigen tests to proactively detect asymptomatic SARS-CoV-2 infections has been a promising yet controversial topic during the COVID-19 pandemic. Concerns have been raised over whether currently authorized lateral flow tests are sufficiently sensitive and specific to detect enough infections to impact transmission whilst minimizing unnecessary isolation of false positives. These concerns have often been illustrated using simple, textbook calculations of positivity rates and positive predictive value assuming fixed values for sensitivity, specificity and prevalence. However, we argue that evaluating repeated testing strategies requires the consideration of three additional factors: new infections continue to arise depending on the incidence rate, isolating positive individuals reduces prevalence in the tested population, and each infected individual is tested multiple times during their infection course. We provide a simple mathematical model with an online interface to illustrate how these three factors impact test positivity rates and the number of isolating individuals over time. These results highlight the potential pitfalls of using inappropriate textbook-style calculations to evaluate statistics arising from repeated testing strategies during an epidemic.

在 COVID-19 大流行期间，使用快速抗原检测来主动检测无症状 SARS-CoV-2 感染进行广泛、重复的检测一直是一个有前途但有争议的话题。人们担心目前授权的侧向层析检测是否足够敏感和具体，能够检测出足够多的感染以影响传播，同时最大限度地减少不必要的误报隔离。这些担忧通常通过简单的教科书计算的阳性率和阳性预测值来说明，假设敏感性、特异性和患病率是固定值。然而，我们认为评估重复检测策略需要考虑三个额外因素：根据发病率不断出现新的感染，隔离阳性个体会降低检测人群中的患病率，以及每个感染者在感染过程中接受多次检测。我们提供了一个带有在线界面的简单数学模型，以说明这三个因素如何影响测试阳性率和随着时间的推移隔离个体的数量。这些结果凸显了使用不适当的教科书式计算来评估流行病期间重复测试策略所产生的统计数据的潜在陷阱。