A model to predict SARS-CoV-2 infection based on the first three-month surveillance data in Brazil

OBJECTIVE: COVID-19 diagnosis is a critical problem, mainly due to the lack or delay in the test results We aimed to obtain a model to predict SARS-CoV-2 infection in suspected patients reported to the Brazilian surveillance system METHODS: We analyzed suspected patients reported to the National Surveillance System that corresponded to the following case definition: patients with respiratory symptoms and fever, who traveled to regions with local or community transmission or who had close contact with a suspected or confirmed case Based on variables routinely collected, we obtained a multiple model using logistic regression The area under the receiver operating characteristic curve (AUC) and accuracy indicators were used for validation RESULTS: We described 1468 COVID-19 cases (confirmed by RT-PCR) and 4271 patients with other illnesses With a data subset including 80% of patients from Sao Paulo (SP) and Rio Janeiro (RJ), we obtained a function which reached an AUC of 95 54% (95% CI: 94 41% - 96 67%) for the diagnosis of COVID-19 and accuracy of 90 1% (sensitivity 87 62% and specificity 92 02%) In a validation dataset including the other 20% of patients from SP and RJ, this model exhibited an AUC of 95 01% (92 51% - 97 5%) and accuracy of 89 47% (sensitivity 87 32% and specificity 91 36%) CONCLUSION: We obtained a model suitable for the clinical diagnosis of COVID-19 based on routinely collected surveillance data Applications of this tool include early identification for specific treatment and isolation, rational use of laboratory tests, and input for modeling epidemiological trends