In search for the hotspots of Disease X: A biogeographic approach to mapping the predictive risk of WHO s Blueprint Priority Diseases

Soushieta Jagadesh; Marine Combe; Mathieu Nacher; Rodolphe Ellie Gozlan

The disease distribution models were fitted using the r package "discmo". In our study, we used the main two methods for species distribution modelling (SDM) i) the classical generalized linear models (glm) using gaussian regression methods and ii) the machine learning method, support vector machine (SVM). We choose the glm models to analyze the influence of the environmental factors on the emergence of BPDs. SVM are popular in SDM using presence and pseudo-absence data. The models were evaluated using ROC curves and the area under curves (AUC) for the produced thresholds were calculated. Studies have critized the use of AUCs in the evaluation of SDMs, especially when the study involves large extent. We removed the "spatial sorting bias" through "point-wise distance sampling" as explained by Hijmanns, 2012. Model prediction was made using the "predict" function to map the predictive risk of the diseases based on the values of the independent variables extracted from the environmental predictor rasters. A weighed average of the glm and SVM models were calculated using the AUCs for each BPD model and a final composite prediction was made.