Which Machine Learning method for outbreaks predictions?

Bokonda, P. L.; Ouazzani-Touhami, K.; Souissi, N.

African swine fever (ASF), dengue fever, influenza, norovirus disease are names of outbreaks that have taken some parts of the world by surprise and caused several deaths. The advent of coronavirus disease at the end of 2019 is clear proof that the issue of outbreaks is more topical than ever. The Machine Learning (ML) comes to the rescue of medicine by offering predictive analysis methods to predict the reappearance of outbreaks and thus help the healthcare system and decision-makers to take the necessary measures in advance. But which ML method should be used to predict an outbreak? Given the large number of ML methods, it is sometimes difficult to know which method to use. This is why we conducted this study. We looked at the above-mentioned outbreaks and identified the following ML methods as the most commonly used: LR, C4.5, NB, SVM, ANN and RF. Of these six methods, the random forest method (RF) stands out from the others because it was used in all four outbreaks considered, has the best accuracy for two outbreaks (ASF and dengue fever) and has been designated by several independent researchers as the most suitable ML method for outbreak prediction.