A climate-driven mechanistic model of dengue transmission: Linking entomological processes to epidemiological dynamics

Dengue transmission is strongly influenced by climatic variability, and understanding how weather affects mosquito ecology is essential for both interpreting outbreaks and improving surveillance. In this study, we analyze weekly dengue incidence together with temperature, rainfall, and humidity data from Brasília, which serve as the basis for deriving empirical, climate sensitive entomological parameters describing mosquito development, mortality, and aquatic stage dynamics. Using these weather informed parameters, we formulate and calibrate a six-compartment dengue transmission model that incorporates the mosquito aquatic stage. The model is employed to investigate the underlying transmission mechanisms, including parameter sensitivity, the temporal evolution of the basic reproduction number R0(t) and the influence of climatic drivers on system behavior. The epidemiological relevance of R0(t) is examined through comparison with weekly reported dengue incidence. Additional analyses such as bifurcation assessment and evaluation of vector-control and treatment-related parameters provide further insight into how environmental conditions and interventions shape transmission potential. Complementing the mechanistic analysis, we conduct a data-driven forecasting study using several statistical and machine learning approaches (Neural Networks, Support Vector Regression, Extreme Gradient Boosting, Seasonal ARIMA, and Long Short Term Memory networks) applied to the epidemiological and climate datasets. This component examines the predictive performance of diverse methods across linear and nonlinear temporal patterns, highlighting the role of climate information in improving incidence forecasts. By integrating climate derived entomological characterization, mechanistic modeling, and data driven forecasting within a unified framework, this study enhances our understanding of climate–dengue interactions while also evaluating practical approaches for short term outbreak prediction.