Hybrid Epidemiological–Neural Modelling of Dengue Under Climate-Socioeconomic Influence

Dengue transmission in climate-sensitive and socioeconomically heterogeneous settings arises from nonlinear interactions between environmental suitability, mosquito ecology, and human behaviour. Classical SEIR-type models offer mechanistic interpretability but are often constrained by observational noise and incomplete data, while purely data-driven approaches typically fail to respect underlying transmission dynamics. In this study, we introduce a hybrid epidemiological framework that embeds a temperature–unemployment driven SEIR–SEI model within a Physics-Informed Neural Network (SEIR–PINN). Using weekly dengue incidence data from Medellín, Colombia, we incorporate unemployment as a socioeconomic proxy that modulates transmission alongside climatic suitability. The Model performance was evaluated in two ways; the prediction of observed incidence, the SEIR–PINN achieved a mean absolute error (MAE) of 15.05 and R2 of 0.975, compared with MAE of 43.57 and R2 of 0.870 for a mechanistic SEIR–SEI baseline fitted under identical climatic and socioeconomic assumptions. Shuffled five-fold cross-validation provided held-out prediction of observed incidence within the 2009–2011 period, with mean MAE of 14.87 and mean R2 of 0.977 (out-of-fold MAE 14.89, R2 0.978). These held-out results interpolate within the observed epidemic and are not an assessment of prospective forecasting performance Importantly, the SEIR–PINN reconstructs latent human and mosquito compartments that remain biologically consistent across epidemic phases. By fusing mechanistic structure with data-adaptive flexibility, the proposed SEIR–PINN provides an interpretable framework for analysing the joint climatic and socioeconomic modulation of dengue transmission in an urban endemic setting. Although using Medellín as a single-city methodological case study, application of this framework to other dengue endemic settings requires external validation using additional locations, longer time periods, and independent datasets. The results therefore support the potential use of physics-informed hybrid models as interpretable tools for dengue surveillance and transmission-dynamics research, while recognising the need for further external validation across longer time periods and additional geographical regions.