We argue that random testing (polling the fraction of infected people in the population) is central to managing the COVID-19 pandemic because it both measures  the key variable controlled by restrictive measures, and anticipates the load on the healthcare system via the progression of the disease. Knowledge of random testing outcomes will therefore (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. ). Frequent random testing for COVID-19 infections has the essential benefit of providing more reliable and refined data than currently available, in both time and space. This is crucial to accompany and monitor the safe release of restrictive measures. Here we show that independent of the total size of population with frequent interactions among its members, about 15000 tests with randomly selected people per day  suffice to obtain valuable data about the current number of infections and their evolution in time . In contrast to testing confined to particular subpopulations such as those displaying symptoms, this will allow close to real-time assessment of the quantitative effect of restrictive measures. 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 .
We argue that frequent sampling of the fraction of infected people (either by random testing or by analysis of sewage water), is central to managing the COVID-19 pandemic because it both measures in real time the key variable controlled by restrictive measures, and anticipates the load on the healthcare system due to progression of the disease. Knowledge of random testing outcomes will (i) significantly improve the predictability 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 . Here we suggest, irrespective of the size of a suitably homogeneous population, a conservative estimate of 15000 for the number of randomly tested people per day which will suffice to obtain reliable data about the current fraction of infections and its evolution in time , thus enabling close to real-time assessment of the quantitative effect of restrictive measures. Still higher testing capacity permits detection of geographical differences in spreading rates . Furthermore and most importantly, with daily sampling 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 relaxation of restrictions with testing focused on symptomatic individuals. This is demonstrated by considering a feedback and control model of mitigation where the feed-back is derived from noisy sampling data .
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