Modeling malaria in Tori-Bossito: a significant advance for public health in Benin

Bienvenue Kouwaye, of the University of Abomey-Calavi, has developed a predictive model of malaria in Tori-Bossito, identifying key interactions between season, rainfall, population, and vegetation. This research, supported by a Franco-African co-supervision, aims to improve public health interventions against this endemic disease in West Africa.

Bienvenue Kouwaye's thesis, carried out under a co-supervision agreement between the University of Abomey-Calavi and Paris 1 University, focuses on modeling malaria morbidity in Tori-Bossito. Addressing a major public health issue, this research aims to understand the environmental factors influencing the density of anopheles mosquitoes, vectors of the disease, in a context where malaria remains a significant cause of morbidity.

Using generalized linear mixed models (GLMM) and machine learning techniques, the research made it possible to select determining variables for predicting malaria risk. The results show that factors such as season, rainfall amount, and population density interact significantly, thereby providing a solid framework for more accurate forecasts.

The implications of this research are considerable for decision-makers and public health actors. By integrating these predictive models, it is possible to improve the effectiveness of interventions against malaria. It is recommended to establish partnerships with local actors to promote the adoption of these tools and maximize their impact in the field.

« "By identifying interactions between variables such as season and population density, we were able to improve the accuracy of malaria risk predictions in Tori-Bossito," says Bienvenue Kouwaye. »

« "This research paves the way for innovative, evidence-based solutions, essential for public health decision-makers in West Africa," notes the Lektaris team. »