A holistic model to assess risk factors of fasciolosis in Ankole cattle.

Abstract

In recent decades, remote sensing (RS) technology and geographical information systems (GIS) were increasingly used as tools for epidemiological studies and the control of zoonotic diseases. Fasciolosis, a zoonotic disease caused by a trematode parasite (Fasciola spp.), is a good candidate for the application of RS and GIS in epidemiology because it is strongly influenced by the environment, i.e. the habitat of the intermediate host. In this study, we examined variables which may increase the fasciolosis risk of Ankole cattle in the degraded and overgrazed Mutara rangelands of north-eastern Rwanda. The risk variables considered included three environmental variables (normalized difference vegetation index, NDVI; normalized difference moisture index, NDMI; normalized difference water index, NDWI), two landscape metric variables (rangeland proportion, building density), two geological variables (poorly-drained soil proportion, elevation) and three animal husbandry variables (herd size, adult proportion and the body condition score). Fasciola spp. prevalence was used as the dependent variable, sampling season as a fixed factor and four principal components (PCs, condensed from the ten risk variables) as covariates in a univariate General Linear Model. Fasciola spp. prevalence was positively correlated to rangeland proportion, cattle herd size in rural areas, adult proportion and individual body condition. Moreover, high Fasciola spp. prevalence was found in densely vegetated areas with high moisture (high values of NDVI and NDMI), in combination with large proportions of poorly-drained soil at low elevations. Future investigations should focus on increased sampling across the Mutara rangelands to prepare a predictive, spatial fasciolosis risk map that would help to further improve sustainable land-use management.