Modeling the impact of public opinion and preventive practices on malaria transmission in central and northern Benin

Abstract

Malaria remains a major public health challenge in Sub-Saharan Africa, especially in Northern Benin, where seasonal variations drive transmission. The success of prevention and treatment depends on community compliance, which is influenced by public risk perception and social attitudes. Despite the widespread implementation of preventive interventions, malaria transmission persists at alarming levels in many regions. Existing mathematical models have often overlooked the role of population opinions and behavioral responses in shaping the effectiveness of these interventions. To address this gap, we extended a mathematical model integrating malaria transmission dynamics with an opinion dynamics framework. The study was conducted across four Benin districts, using five years (2019–2023) of real-world malaria surveillance data from the National Malaria Control Program. Behavioral data were derived from the 2022 Malaria Behavior Survey. The model was calibrated using nonlinear least squares estimation techniques. Analytical results confirm the positivity and boundedness of the model, and a disease-free periodic solution was established. The control reproduction number (R_c) was computed using the monodromy matrix method. The numerical analysis revealed that increased the percentage of favorable adherence to prophylactic measures results in a slight but consistent decrease in malaria incidence. Specifically, in Bante, when partial adherence rose from 52% to 100%, the effective reproduction number decreased by 82.75%. Furthermore, we also note that a higher baseline influence rate (Ω₀) contributed to a substantial reduction in effective reproduction number. In Sinende, increasing Ω₀ from 0.05 to 50 reduced malaria incidence by 17.65%. The findings highlight that incorporating population behaviors and opinions into disease modeling enhances the effectiveness of public health strategies for sustainable malaria control in endemic areas.