I propose novel partial identification bounds on disease prevalence from information on test rate and test yield. The approach broadly follows recent work by \mbox{%DIFAUXCMD MM20 user-specified bounds on (i) test accuracy , in particular sensitivity, (ii) the extent to which tests are targeted, formalized as restriction on the effect of true  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. For example , they place the infection fatality rate for Italy well above the one of influenza by mid-April.
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.
R
disease
infection
R
broadly follows recent work by \mbox{%DIFAUXCMD MM20
utilizes
R
, in particular sensitivity,
and
A
infection
R
For example
Notably, and in contrast to speculations that were widely reported at the time