  Using recent data from voluntary mass testing, I provide credible bounds on
prevalence of SARS-CoV-2 for Austrian counties in early December 2020. When
estimating prevalence, a natural missing data problem arises: no test results
are generated for non-tested people. In addition, tests are not perfectly
predictive for the underlying infection. This is particularly relevant for mass
SARS-CoV-2 testing as these are conducted with rapid Antigen tests, which are
known to be somewhat imprecise. Using insights from the literature on partial
identification, I propose a framework addressing both issues at once. I use the
framework to study differing selection assumptions for the Austrian data.
Whereas weak monotone selection assumptions provide limited identification
power, reasonably stronger assumptions reduce the uncertainty on prevalence
significantly.
