  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.
