Arboviral diseases represent a recurrent public health challenge in Brazil. Factors such as population growth and climate change contribute to their spread. Recent outbreaks reinforce the importance of rapid and continuous monitoring. This study proposes a model to estimate population dengue cases based on positive laboratory tests, aiming to identify outbreaks early.

Data on notified dengue cases (Ministério da Saúde) and positive tests (private laboratory) were used, aggregated by epidemiological week (EW) between 2014 and 2023, except 2020 due to unavailability of notified cases by EW. Data refer to the state of Minas Gerais. For each EW, the number of notifications was subtracted from the number of positive tests. A weighted average by EW was calculated, with higher weights for more recent years (2023 = weight 10 to 2014 = weight 1). Only molecular tests and immunoassays (NS1 and IgM) were considered. The averages were used to fit a regression model, applied to 2024 data to predict official cases based on positive tests.

A total of 92,535 positive tests and 1,507,175 notified cases were analyzed. The model explained 95% of the variance in notified cases (R² = 0.9495; p<0.001; F = 939.9), with each positive test associated with 19.15 cases (p<0.001). In EWs with fewer than 2,500 notifications, there was less discrepancy between predicted and observed cases, possibly due to greater use of testing in suspected cases. In weeks with many cases, discrepancies increased, suggesting greater use of clinical diagnosis.

The model demonstrated potential to predict official dengue cases based on laboratory tests, enabling rapid and real-time monitoring of the epidemiological scenario. Integration of environmental (rainfall, temperature) and genetic data may further improve accuracy and outbreak control.