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_10
Snippet: The information from São Paulo (SP) and Rio de Janeiro (RJ) was used to obtain and validate the predictive model. This choice was because these are the FUs with the largest number of confirmed cases and the earliest establishment of the surveillance system. Thus, we used a subset of 80% of randomly selected patients from SP and RJ (modeling dataset) to specify the multiple model. We selected the covariates by a non-automatic stepwise procedure u.....
Document: The information from São Paulo (SP) and Rio de Janeiro (RJ) was used to obtain and validate the predictive model. This choice was because these are the FUs with the largest number of confirmed cases and the earliest establishment of the surveillance system. Thus, we used a subset of 80% of randomly selected patients from SP and RJ (modeling dataset) to specify the multiple model. We selected the covariates by a non-automatic stepwise procedure using logistic regression. Age and days after notification of the first confirmed case (DNFCC) were used to create interaction terms with each other as an independent predictor. During modeling, a p-value of 0.15 was considered as a criterium to enter the variable and 0.20 to exclude it. After evaluating all the variables, exclusions were made until obtaining a model including only covariates with p <0. 10 .
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