Data Balancing Process to Strengthen a Malaria Control Prediction System in Senegal

Malaria is a public health problem in Senegal. Despite the implementation of prevention and treatment programs, the prevalence rate remains high, although there has been a noticeable decrease over the years. To further strengthen our efforts in the fight against malaria, we have previously developed a prediction system aimed at assessing the presence or absence of Anopheles larvae in specific sites. This system, is a crucial component of our anti-larval control (ALC) strategy, which involves gathering physico-chemical parameters from the site and using them to predict the likelihood of larvae presence. Given that, our prediction system relies on these physico-chemical parameters, ensuring the reliability and quality of the data is paramount. In our previous study, although we had access to reliable and high-quality data, we encountered an issue with data imbalance. To validate the accuracy of our prediction system, it is essential to address this data imbalance.