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COVID-19 pandemics has challenged emergency response systems worldwide, withwidespread reports of essential services breakdown and collapse of health carestructure. A critical element involves essential workforce management sincecurrent protocols recommend release from duty for symptomatic individuals,including essential personnel. Testing capacity is also problematic in severalcountries, where diagnosis demand outnumbers available local testing capacity.This work describes a machine learning model derived from hemogram exam dataperformed in symptomatic patients and how they can be used to predict qRT-PCRtest results. Methods: A Naive-Bayes model for machine learning is proposed forhandling different scarcity scenarios, including managing symptomatic essentialworkforce and absence of diagnostic tests. Hemogram result data was used topredict qRT-PCR results in situations where the latter was not performed, orresults are not yet available. Adjusts in assumed prior probabilities allowfine-tuning of the model, according to actual prediction context. Proposedmodels can predict COVID-19 qRT-PCR results in symptomatic individuals withhigh accuracy, sensitivity and specificity. Data assessment can be performed inan individual or simultaneous basis, according to desired outcome. Based onhemogram data and background scarcity context, resource distribution issignificantly optimized when model-based patient selection is observed,compared to random choice. The model can help manage testing deficiency andother critical circumstances. Machine learning models can be derived fromwidely available, quick, and inexpensive exam data in order to predict qRT-PCRresults used in COVID-19 diagnosis. These models can be used to assiststrategic decision-making in resource scarcity scenarios, including personnelshortage, lack of medical resources, and testing insufficiency.

Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios