  This paper provides a mathematical model to show that the incorrect
estimation of r, the fraction of asymptomatic COVID-19 carriers in the general
population, can account for much of the world's failure to contain the pandemic
in its early phases. The SE(A+O)R model with infectives separated into
asymptomatic and ordinary carriers, supplemented by a model of the data
generation process, is calibrated to standard datasets for several countries.
It is shown that certain fundamental parameters, notably r, are unidentifiable
with this data. A number of potential types of policy intervention are
analyzed. It is found that the lack of parameter identifiability implies that
only some, but not all, potential policy interventions can be correctly
predicted. In an example representing Italy in March 2020, a hypothetical
optimal policy of isolating confirmed cases that aims to reduce the basic
reproduction number of the outbreak to R0 = 0.8 assuming r = 10%, only achieves
R0 = 1.4 if it turns out that r = 40%.
