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

We extracted the climatic covariates such as monthly maximum and minimum temperatures and precipitation from 'Terra Climate' high-resolution global data from 2000-2017 (14) . Land cover data used was a composite of the 2017 MODIS satellite imagery and the deforestations mdata was derived from the Hansan's deforestation tree loss-year maps from 2000-2018. The topological data including altitude and hydrological models was extracted from one arc-second digital elevation model (DEM) of 30m resolution, derived from NASA Version 3.0 Shuttle Radar Topography Mission (SRTM) imagery available in the United States Geological Survey (USGS). We obtained the geographical distribution of the primary hosts and reservoir mammals from the IUCN red list (15) . The shapefiles were rasterized and resampled into rasters of the 5km resolution raster model. We used livestock distribution rasters (2010) from the Food and Agriculture Organisation (FAO) for diseases such as RVF and Nipah including cattle, goats and . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.