We argue that frequent sampling of the fraction of a priori non-symptomatic but infectious humans (either by random or cohort testing) significantly improves the management of the COVID-19 pandemic, when compared to intervention strategies relying on data from symptomatic cases only. This is because such sampling measures the incidence of the disease, the key variable controlled by restrictive measures, and thus anticipates the load on the healthcare system due to progression of the disease. The frequent testing of non-symptomatic infectiousness will (i) significantly improve the predictability of the pandemic, (ii) allow informed and optimized decisions on how to modify restrictive measures, with shorter delay times than the present ones, and (iii) enable the real-time assessment of the efficiency of new means to reduce transmission rates. These advantages are quantified by considering a feedback and control model of mitigation where the feedback is derived from the evol...

中文翻译：

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测试无症状个体以快速反馈控制 COVID-19 流行病。

我们认为，与仅依赖有症状病例数据的干预策略相比，频繁抽样先验无症状但具有传染性的人群（通过随机或队列测试）可以显着改善 COVID-19 大流行的管理。这是因为这种抽样衡量的是疾病的发病率，这是受限制性措施控制的关键变量，因此可以预测由于疾病的进展而给医疗保健系统带来的负担。频繁检测无症状传染性将 (i) 显着提高大流行的可预测性，(ii) 允许就如何修改限制措施做出明智和优化的决定，延迟时间比目前的更短，以及 (iii) 使实时评估降低传输速率的新方法的效率。