We know that the statistical analyses and predictive techniques we have used successfully to map livestock are effective for some vector borne diseases (like the tsetse transmitted trypanosome and the screwworm). Some of our most recent efforts have been to see whether they work for some of the major communicable diseases that affect livestock. We have initially looked at Rinderpest, Contagious Bovine Pleuro-Pneumonia (CBPP), Anthrax, Bovine Tuberculosis, Bovine Brucellosis, and Foot and Mouth Disease in cattle and buffaloes; Sheep and/or Goat Pox in small ruminants; Hog Cholera or Classical Swine Fever in pigs; and finally Fowl Plague and Newcastle disease in poultry.
The training data we have used for these studies is extracted from the OIE yearbooks, which synthesise official disease occurrence reports and provide details of the severity and extent of each disease in each contributing country. These measures are qualitative rather than quantitative and so we first had to define a ranking system which described the levels of disease present. This ranged from -4 for 'never found' through 0 for 'not present' to +4 for 'High Occurrence', and allowed us to perform numerical analyses. We can also combine disease 'scores' to give a map of the observed occurrence of all diseases, though we stress that this map is designed to provide an impression of changes in overall disease levels as it makes little epidemiological sense to lump diseases of different animals together.
At a more realistic level, we have used eco-climatic indicators from satellite imagery, human demographic variables, and livestock density and production parameters as inputs to produce a predicted map of bovine diseases. As you can see from the comments in the Special Information Button on the map, we have made significant progress and can hazard a guess at the factors that are most closely associated with disease levels - in this case primarily the densities of agriculturally active people and of the cattle themselves. There is, however, a lot of room for improvement!