Dengue, a disease with multifactorial determinants, is linked to population susceptibility to circulating viruses and the extent of vector infestation. This study aimed to analyze the temporal trends of dengue cases and deaths in Belo Horizonte, Minas Gerais, Brazil, from 2007 to 2020. Data from the Notifiable Diseases Information System (Sinan) were utilized for the investigation. To assess the disease's progression over the study period and predict its future incidence, time series analyses were conducted using a generalized additive model (GAM) and a seasonal autoregressive integrated moving average (SARIMA) model. Over the study period, a total of 463,566 dengue cases and 125 deaths were reported. Notably, there was an increase in severe cases and deaths, marking hyperendemics characterized by simultaneous virus circulation (79.17% in 2016-50% in 2019). The generalized additive model revealed a non-linear pattern with epidemic peaks in 2010, 2013, 2016, and 2019, indicating an explosive pattern of dengue incidence. The SARIMA (3,1,1) (0,0,0)12 model was validated for each year (2015 to 2019). Comparing the actual and predicted numbers of dengue cases, the model demonstrated its effectiveness for predicting cases in the municipality. The rising number of dengue cases emphasizes the importance of vector surveillance and control. Enhanced models and predictions by local health services will aid in anticipating necessary control measures to combat future epidemics.
Keywords: arbovirus; epidemic; prediction; time-series; vulnerability.