Temporal Modelling of Dengue Fever in Côte d’Ivoire: Performance of the GAM and ARMAX Models with and without Climate Lag

Introduction: Predictive modelling of vector-borne diseases such as dengue is essential for anticipating epidemic outbreaks and guiding response strategies. In Côte d’Ivoire, few studies have focused on the multivariate prediction of dengue using climatic data. The aim of this study is to assess the performance of various statistical models, in particular generalised additive models (GAMs) and ARMAX models, with and without the integration of time lags on climatic variables. Methods: Four models were compared: GAM with and without lag, and ARMAX with and without lag. Predictive performance was assessed using standard criteria such as root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), Akaike information criterion (AIC) and Bayesian information criterion (BIC). Weekly data on confirmed dengue cases and meteorological variables (temperature, humidity) from 2017 to 2023 were used. Results: The lagged GAM model performed best, with an RMSE of 0.92, a MAE of 0.26, and an R2 of 0.837. It also had the best AIC (243.58) and BIC (318.61), underlining its parsimony. The ARMAX model with lag performed intermediately, but performed less well than the GAM with lag. The ARMAX model without lag, with a lower R2 (0.53) and a MAE of 0.52, shows a reduced explanatory capacity, although it has a slightly better AIC than the version with lag. Finally, the GAM without lag showed the weakest results (R²: 0.101, RMSE:2.21, MAE: 0.82), suggesting that the model is inadequate without taking into account the temporal dynamics of climatic factors. Conclusion: Incorporating the time lag of climatic variables significantly improves the quality of forecasts. The GAM with time lag stands out as the best performing model for anticipating dengue cases in Côte d’Ivoire. These results highlight the importance of taking into account lagged environmental dynamics in epidemiological modelling and open up prospects for an effective early warning system.