Exploring the nexus of climate-socioeconomic drivers of dengue transmission: a covariate-augmented SEIR-SEI mechanistic model

A deterministic covariate-augmented SEIR-SEI model is developed and used to gain insight into the nexus between climate and socioeconomic parameters and the transmission dynamics of dengue. The model developed a framework that incorporates two external influences: climate and socioeconomic factors. The model is rigorously analyzed, with disease-free and endemic equilibria investigated, and is simulated using data relevant to Medellín, Colombia. We then employ machine learning algorithms as an empirical feature-selection step to identify and rank socioeconomic predictors of dengue incidence, revealing that the unemployed population is the dominant socioeconomic driver. This finding motivates the selection of unemployment as the socioeconomic covariate in the mechanistic model. From our numerical simulation, we computed using temperature only and combined [temperature + socioeconomic (unemployment)] parameters to determine how their influences drive the epidemic spread. Our model suggests that socioeconomic influence can act as a driver alongside temperature, increasing the transmission rate and leading to an earlier peak. The framework is appropriate for endemic settings where socioeconomic and climatic factors are the primary drivers of transmission dynamics. The approach demonstrates how empirical machine-learning insights can inform covariate selection in mechanistic epidemiological models, thereby strengthening interpretation of how socioeconomic and climatic conditions interact to shape epidemic potential.