Statistical analysis

Two methods were compared to determine the most reliable clinical predictors of influenza virus activity in Cameroon: binomial logistic predictive model and random forest model. For each of the methods we divided the samples into two: a training data set (used to build the models) and a test data set (used to assess the prediction performance of the models). The samples were selected randomly in order to obtain approximately 80% for the training data set and 20% for the test data set. Performance of both models was assessed. The choice of the best binomial logistic predictive model was based on the Akaike Information Criteria (AIC). The area under the curve (AUC) was used to assess the performance of the binomial logistic predictive model while the out-of-bag (OOB) error rate was used to measure the performance of the random forest model. All statistical analyses were performed in R version 3.5.2.