Prediction model to discriminate leptospirosis from hantavirus.

Objective: The aim of this study was to build a prediction model to discriminate precociously hantavirus infection from leptospirosis, identifying the conditions and risk factors associated with these diseases.
Methods: A logistic regression model in which the response variable was the presence of hantavirus or leptospirosis was adjusted.
Results: As a result, the method selected the following variables that influenced the prediction formula: sociodemographic variables, clinical manifestations, and exposure to environmental risks. All variables considered in the model presented statistical significance with a p<0.05 value. The accuracy of the model to differentiate hantavirus from leptospirosis was 88.7%.
Conclusions: Concluding that the development of statistical tools with high potential to predict the disease, and thus differentiate them precociously, can reduce hospital costs, speed up the patient's care, reduce morbidity and mortality, and assist health professionals and public managers in decision-making.