Mathematical modeling of the 2023 dengue outbreak in the Centre Region of Burkina Faso: Parameter estimation and assessment of control strategies

Abstract

Mathematical models are valuable tools in the fight against infectious diseases such as dengue. However, their use to guide public health strategies in sub-Saharan Africa, particularly in Burkina Faso, remains limited due to the scarcity of locally calibrated models. Moreover, no study has yet applied the African vulture optimization algorithm (AVOA) for dengue parameters in this context. In this study, we develop a compartmental model to evaluate the impact of control strategies on the 2023 dengue epidemic in the Centre Region of Burkina Faso. The model combines a susceptible–infected (SI) structure for the mosquitoes aquatic phase, a susceptible–exposed–infected (SEI) structure for adult mosquitoes, and a susceptible–exposed–infected–recovered (SEIR) framework for the human population. It incorporates key features, including vertical transmission in mosquitoes and a distinction between clinically detected and undetected human cases. After mathematical analysis, key epidemiological parameters were estimated by calibrating the model against weekly reported case data from June to December 2023 using AVOA. The basic reproduction number (R0) was estimated at 2.30, confirming the potential for sustained transmission. Sensitivity analysis identified the mosquito biting rate (b), larval carrying capacity (kA), mosquito mortality (µV), and the recovery rate of undetected cases as the most influential parameters. Finally, numerical simulations assessed the impact of control measures recommended by the Ministry of Health of Burkina Faso. The results show that the effectiveness of dengue control strategies depends critically on their intensity and, most importantly, their duration, highlighting the need for integrated, intensive, and sustained vector control measures combined with individual protective actions for effective and long-term management of dengue transmission.