COVID-19 pandemics has challenged emergency response
systems worldwide, with widespread reports of
essential services breakdown and collapse of health
care structure. Testing capacity is also problematic
in several countries, where diagnosis demand
outnumbers available local testing capacity.
A Naïve-Bayes model for machine
learning is proposed for handling different scarcity
scenarios. Hemogram result data was used to predict
qRT-PCR results in situations where the latter was not
performed, or results are not yet available. Adjusts
in assumed prior probabilities allow fine-tuning of the
model, according to actual prediction context. The results
can be used for screening of patients to be tested in a
scenario where few qRT-PCR or IgM/IgG tests are available.