In particular, the models were fitted on a random sample of 85% of the locations and used the remaining locations to compare model-based predictions with observed prevalence by calculating the Mean Absolute Error (MAE). Model validation was performed on the first two models with the highest probability of having generated the data among those considered. Prediction was carried out using Bayesian kriging based on the model with the best predictive ability.
