Probabilistic estimation of national economic losses due to cystic echinococcosis in sheep and goats in Türkiye

Abstract

Cystic echinococcosis (CE) causes substantial direct economic losses in livestock, mainly through condemnation of infected edible offal such as liver and lungs. In this study, we estimated national-level condemnation losses attributable to CE in slaughtered sheep and goats in Türkiye using a probabilistic model informed by a one-year (2023), previously published abattoir surveillance dataset from Siirt Province. In the source dataset, 5119 sheep and 2860 goats were examined, and CE was detected in 569/5119 sheep (11.12%) and 66/2860 goats (2.31%). Organ involvement proportions were derived from reported localization patterns. Prevalence uncertainty was modeled using beta distributions, whereas organ weights and unit prices were represented by triangular distributions. Parameter uncertainty was propagated through Monte Carlo simulation (10,000 iterations), and results were expressed as means and 95% uncertainty intervals (UI). The mean total condemnation loss for the abattoir sample was USD 8060 and was largely driven by sheep liver losses. At the national level, the mean annual loss was estimated at USD 38.41 million (95% UI: 33.97–43.27 million), with sheep contributing USD 37.63 million (95% UI: 33.23–42.45 million) and goats USD 0.78 million (95% UI: 0.68–0.87 million). Scenario analysis suggested that 10%, 25%, and 50% reductions in prevalence would reduce the mean annual loss to USD 34.57 million, USD 28.80 million, and USD 19.20 million, respectively. Sensitivity analysis indicated that the national loss estimate was driven primarily by sheep liver weight (r = 0.760) and liver unit price (r = 0.574). Overall, CE imposes a substantial direct economic burden on Türkiye's small ruminant sector through liver and lung condemnation, with losses concentrated overwhelmingly in sheep. Probabilistic modeling based on abattoir-derived inputs provides decision-relevant national estimates and identifies the parameters that contribute most strongly to uncertainty. © 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.