The authors of this article argue that random testing (i.e., polling the fraction of infected people in the population) is central to managing the COVID-19 pandemic.

They believe that random testing, as opposed to testing confined to particular sub-populations (e.g., those displaying symptoms), would allow close to real-time assessment of the quantitative effect of restrictive measures. Random testing therefore has to potential to (i) significantly improve the predictability of the course of the pandemic, (ii) allow informed and optimized decisions on how to modify restrictive measures, with much shorter delay times than the present ones, and (iii) enable the real-time assessment of the efficiency of new means to reduce transmission rates (such as new tracing strategies based on the mobile telephone network, wearing face masks, etc.).

Using a feedback and control model, the authors predict that about 15000 tests with randomly selected people per day would be sufficient to obtain valuable data about the current number of infections and their evolution in time. With yet higher testing capacity, random testing further allows detection of geographical differences in spreading rates and thus the formulation of optimal strategies for a safe reboot of the economy. Most importantly, with daily random testing in place, a reboot could be attempted while the fraction of infected people is still an order of magnitude higher than the level required for a reboot without such polling.