  Accurate forecasts for COVID-19 are necessary for better preparedness and
resource management. Specifically, deciding the response over months or several
months requires accurate long-term forecasts which is particularly challenging
as the model errors accumulate with time. A critical factor that can hinder
accurate long-term forecasts, is the number of unreported/asymptomatic cases.
While there have been early serology tests to estimate this number, more tests
need to be conducted for more reliable results. To identify the number of
unreported/asymptomatic cases, we take an epidemiology data-driven approach. We
show that we can identify lower bounds on this ratio or upper bound on actual
cases as a factor of reported cases. To do so, we propose an extension of our
prior heterogeneous infection rate model, incorporating unreported/asymptomatic
cases. We prove that the number of unreported cases can be reliably estimated
only from a certain time period of the epidemic data. In doing so, we construct
an algorithm called Fixed Infection Rate method, which identifies a reliable
bound on the learned ratio. We also propose two heuristics to learn this ratio
and show their effectiveness on simulated data. We use our approaches to
identify the upper bounds on the ratio of actual to reported cases for New York
City and several US states. Our results demonstrate with high confidence that
the actual number of cases cannot be more than 35 times in New York, 40 times
in Illinois, 38 times in Massachusetts and 29 times in New Jersey, than the
reported cases.
