DEVELOPMENT AND VALIDATION OF A TUBERCULOSIS CASE PREDICTION MODEL IN THE STATE OF GOIÁS (2025-2026) WITH A FOCUS ON COMPUTATIONAL PATHOLOGY

Introduction: Globally, tuberculosis (TB) is a public health issue.In Gois, in 2023, there was a 9.42% increase in TB cases compared to 2022.The approach of computational pathology is an essential tool for analyzing data that can contribute to controlling the Tb epidemic.Objective: Develop and validate a model statistical capable of to predict the number of TB notifications for 2025 and 2026.Methods: A computational algorithm was developed to construct a time series model for predicting notifications.This model provided better estimates of AIC, BIC, MSE, and RMSE compared to an self-adjusting model from an R Studio software library.The model developed in this study was applied to TB notification data in Gois from 2001 to 2023.Results: A gradual increase in TB notifications in Gois was estimated for 2025 and 2026, with peaks observed in January, March, September, and October.Additionally, a decrease in TB notifications was noted in February, July, and December for 2025, and in February, June, and December for 2026.Conclusion: These findings can significantly contribute to public health planning and decision-making aimed at controlling TB in the region.