Resurgence Prediction of Ten Infectious Diseases under Surveillance in Senegal
In this paper, there are proposed two multi-class predictive models for estimating the resurgence probability of ten infectious diseases under epidemic surveillance in Senegal. The first model is a Multiple Binary Random Forest (MBRF), which utilizes the ranger function with Gini criterion and allows to separately predict each of the ten diseases while taking account of their interdependencies. The second model is a Multi-Output Decision Tree (MODT), which introduces an inertia criterion (calculated with Chi-square distance) as the node impurity measure and allows to