Using geostatistics to better understand the epidemiology of animal rabies in Morocco: what is the contribution of the predictive value?

This study aims to characterize the spatial distribution of animal rabies in Morocco in order to provide appropriate control approaches. Descriptive analyses of the epidemiological data show that the number of reported canine rabies cases greatly underestimates the true incidence of the disease. Underreporting subsequently affects the coherence of its spatial distribution. To perform accurate geographic distribution mapping of the disease based on interpolation methods, a data set was created using data between 2000 and 2018 to compare the derived disease cases with known true values in order to identify disease clusters. The subsequent interpolation was conducted using Ordinary Kriging regression methods and the semi variogram to focus on short distances and reduce uncertainty. The estimated clusters of rabies were evaluated using a cross validation step which revealed predicted cases close to the true values. To improve the precision of analysis, the authors displayed georeferenced dog and human rabies cases reported during the last three years, demonstrating reliable results that correspond to the estimated cluster areas similar to the true disease incidence on the field. This work highlights a strong correlation between infrastructure projects (i.e. railways, roads, facilities) and rabies epizootics for several specific locations. This study is the first attempt to use geostatistics to build upon the understanding of animal rabies in Morocco and shed light on the most appropriate strategies to sustainably reduce and mitigate the risk of rabies. There has been little literature on the use of kriging methods in animal health research. Thus, this study also aimed to explore a novel method in the veterinary sciences to establish kriging as a valid and coherent analysis tool to identify the extent to which the geostatistic area can objectively support understanding on animal rabies and saw it as being highly instrumental in coping with gaps in the data.