Mapping cystic echinococcosis in Italian cattle: A spatiotemporal analysis of surveillance data (2019-2023).

Cystic echinococcosis (CE), caused by Echinococcus granulosus sensu lato, is a neglected zoonotic disease with considerable economic and public health impacts. In Italy, higher CE prevalence has historically been observed in southern and insular regions, but recent data on spatial and temporal patterns are scarce. While cattle are not a source of transmission, they serve as useful indicators of regional prevalence. This study assessed the spatial and temporal distribution of CE in cattle across nine regions of central, southern, and insular Italy from 2019 to 2023. A total of 4441 samples were collected during routine abattoir inspections, and infected animals and farms were identified via post-mortem examination. A Bayesian spatial model with a temporal component was used to estimate relative risk (RR) at the provincial level. Overall prevalence was 29.6 % (95 % CI: 28.2-30.9 %). Sardinia exhibited the highest prevalence (67.8 %), while Apulia had the lowest (24.0 %). Several provinces in Sardinia and Sicily consistently appeared as high-risk areas, with RR exceeding 2.0 in some years. In contrast, provinces in Molise and Apulia showed lower or near-average risk. A temporal peak was observed in 2020, followed by a decline to near-neutral levels by 2023. These findings align with previous studies highlighting the burden of CE in southern and insular Italy and underscore considerable spatial heterogeneity. Continued surveillance, application of proven control strategies (e.g. dog deworming, sheep vaccination), and improved diagnostics are essential to reducing CE burden and informing targeted interventions in endemic regions. Surveillance reveals marked CE prevalence heterogeneity across Italian regions. Sardinia and Sicily show the highest burden of CE in slaughtered cattle. CE prevalence has increased compared to earlier studies in the same regions. Cystic echinococcosis,Epidemiology,Surveillance,Modelling,Spatial Modelling.