Methodological Comparison of SMR and Poisson-Gamma Disease Mapping Models for Brucella abortus Risk Estimation in Cattle in Libya

الملخص

Background: Brucellosis, primarily caused by Brucella abortus, is a zoonotic disease of major concern in Libya, where cattle husbandry forms an integral component of rural livelihoods and where the post-2011 collapse of veterinary surveillance infrastructure has heightened underreporting risk. Aim: This study aimed to demonstrate and compare two disease-mapping approaches, the classical Standardized Mortality Ratio (SMR) method and the Bayesian Poisson-Gamma model, for estimating the relative risk (RR) of B. abortus in cattle across all 22 official Libyan administrative districts, using district-level case counts constructed to be illustrative of patterns reported in the literature rather than official surveillance records. Methods: Illustrative case-count data, informed by patterns reported in published Libyan cattle and small-ruminant seroprevalence studies and aggregate veterinary indicators for the 2018–2024 period, were analysed using R and ArcGIS to generate relative risk estimates and thematic disease maps under both models. Results: The SMR method produced unstable, zero-valued estimates for districts with no reported cases (Tripoli, Benghazi, and Ghat), masking their true underlying risk. The Poisson-Gamma model, by contrast, generated smoothed, non-zero estimates for all districts, reclassifying these zero-case districts from “no observed risk” to defensible low-risk categories and revealing more plausible spatial risk patterns consistent with known underreporting in urban and remote veterinary surveillance. Both models consistently identified Zawiya and Jufra as very high-risk districts and Jafara and Zawara as high-risk districts, aligning with spatial patterns reported in prior Libyan seroprevalence research on the western coastal strip and central oasis settlements. Conclusion: As a methodological demonstration, the Poisson-Gamma model shows superior performance for brucellosis risk mapping in data-sparse post-conflict settings, such as Libya, and its adoption for future brucellosis surveillance is recommended once verified and district-level cattle surveillance data become available.