Prevalence and Spatial Distribution Modelling of Fasciola Species Infection in Sheep in Northwest Ethiopia.

Authors

Andnet Yirga Assefa, Selamawit Tilahun Anteneh, Moges Maru, Endris Aman Ali, Bizuneh Tsehayneh Azene, Simachew Getaneh Endalamaw, Yeshwas Ferede Alemu, Alebachew Tilahun Wassie, Zewdu Seyoum Tarekegn

Published in

Veterinary journal (London, England : 1997). Pages 106828. Aug 15, 2026. Epub Aug 15, 2026.

Abstract

Fasciolosis, caused by Fasciola spp., is a neglected parasitic disease with significant veterinary and economic implications globally. The aim of the study was to estimate the prevalence, identify risk factors, and predict the spatial distribution of Fasciola spp. infection in sheep in northwestern Ethiopia. A cross-sectional study was conducted between June and August 2023 in six districts across three Gondar administrative zones. A total of 399 sheep were randomly selected, and faecal samples were examined using the sedimentation technique. Logistic regression was used to assess risk factors, and ecological niche modelling was performed using the Maximum Entropy (MaxEnt) algorithm based on georeferenced occurrence data. The overall prevalence was 33.3% (95% CI: 28.7-37.9). Infection was significantly associated with a lack of deworming (adjusted odds ratio [AOR] = 1.9; 95% CI: 1.1-3.2), poor body condition (AOR = 1.7; 95% CI: 1.1-2.6), and female sex (AOR = 1.6; 95% CI: 1.05-2.5). Among environmental predictors, temperature annual range (BIO7; 41.4%) and enhanced vegetation index (EVI; 24.7%) had the greatest impact on model performance. The model identified highly suitable areas in the South and Central Gondar zones, notably around Lake Tana, whereas lower suitability predicted in drier and less vegetated areas. Overall, these findings reveal a considerable fasciolosis burden and identify key environmental and host-related drivers of Fasciola spp. infection. The identification of potentially suitable areas, including those in previously unsampled locations, highlights the usefulness of ecological niche modelling in guiding targeted surveillance and control strategies in endemic, resource-constrained environments.

PMID:
42603697
Bibliographic data and abstract were imported from PubMed on 16 Aug 2026.

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