  I propose novel partial identification bounds on infection prevalence from
information on test rate and test yield. The approach utilizes user-specified
bounds on (i) test accuracy and (ii) the extent to which tests are targeted,
formalized as restriction on the effect of true infection status on the odds
ratio of getting tested and thereby embeddable in logit specifications. The
motivating application is to the COVID-19 pandemic but the strategy may also be
useful elsewhere.
  Evaluated on data from the pandemic's early stage, even the weakest of the
novel bounds are reasonably informative. Notably, and in contrast to
speculations that were widely reported at the time, they place the infection
fatality rate for Italy well above the one of influenza by mid-April.
