Author: Fredi A Diaz-Quijano; Jose Mario Nunes da Silva; Fabiana Ganem; Silvano Oliveira; Andrea Liliana Vesga-Varela; Julio Croda
Title: A model to predict SARS-CoV-2 infection based on the first three-month surveillance data in Brazil. Document date: 2020_4_8
ID: 3dzv20b7_11
Snippet: The values predicted by the multiple model obtained were used to estimate the area under the ROC curve (AUC). We interpreted the AUC as an indicator of goodness of fit such that values between 0.9 and 0.99 are excellent, 0.8 -0.89 good, 0.7 -0.79 acceptable, and 0.51 -0.69 are poor. 13 Next, the model was applied to the 20% of patients from SP and RJ who were not included in the modeling dataset (validation dataset). Moreover, we applied it to th.....
Document: The values predicted by the multiple model obtained were used to estimate the area under the ROC curve (AUC). We interpreted the AUC as an indicator of goodness of fit such that values between 0.9 and 0.99 are excellent, 0.8 -0.89 good, 0.7 -0.79 acceptable, and 0.51 -0.69 are poor. 13 Next, the model was applied to the 20% of patients from SP and RJ who were not included in the modeling dataset (validation dataset). Moreover, we applied it to those from FUs other than SP/RJ to evaluate the applicability in a very different scenario. We also calculated the accuracy to classify events of a predicted probability of ≥ 0.5. 14 We presented some cut-off points of the predicted value based on optimized accuracy indicators (in SP/RJ patients). These cut-offs included: a preset predicted value of 0.5; the highest value with a sensitivity >95%; the lowest with specificity >95%; the value with the highest overall accuracy; and the value with the best balance between sensitivity and specificity (based on the product thereof). Accuracy indicators of these selected cutoffs were described for both the SP/RJ patients (modeling + validation dataset) and those from the other FUs.
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