  The topic of this paper is prevalence estimation from the perspective of
active information. Prevalence among tested individuals has an upward bias
under the assumption that individuals' willingness to be tested for the disease
increases with the strength of their symptoms. Active information due to
testing bias quantifies the degree at which the willingness to be tested
correlates with infection status. Interpreting incomplete testing as a missing
data problem, the missingness mechanism impacts the degree at which the bias of
the original prevalence estimate can be removed. The reduction in prevalence,
when testing bias is adjusted for, translates into an active information due to
bias correction, with opposite sign to active information due to testing bias.
Prevalence and active information estimates are asymptotically normal, a
behavior also illustrated through simulations.
