A geospatial risk assessment model for leprosy in Ethiopia based on environmental thermal-hydrological regime analysis

Abstract

Geospatial methods were used to study the associations of the environmental thermal-hydrological regime with leprosy prevalence in the Oromia and Amhara regions of Ethiopia. Prediction models were developed that indicated leprosy prevalence was related to: (i) long-term normal climate grid data on temperature and moisture balance (rain/potential evapo-transpiration); (ii) satellite surveillance data on the Normalized Difference Vegetation Index (NDVI) and daytime earth surface temperature (Tmax) from the Advanced Very High Resolution Radiometer (AVHRR); and (iii) a Genetic Algorithm Rule-Set Prediction (GARP) model based on NDVI and Tmax data in relation to leprosy prevalence data. Our results suggest that vertical transmission is not the only means of acquiring leprosy and support earlier reports that a major factor that governs transmission of leprosy is the viability of Mycobacterium leprae outside the human body which is related to the thermal-hydrologic regime of the environment.

Keywords

• leprosy, epidemiology, Geographic Information Systems, remote sensing, climate, Genetic Algorithm Rule- Set Prediction (GARP).