Worldwide hotspots and ecological drivers of canine Echinococcus granulosus sensu lato: Space-time scan statistics and Maxent modelling from a systematic evidence base

Abstract

Cystic echinococcosis caused by Echinococcus granulosus sensu lato remains a major One Health concern. In this study, we conducted a PRISMA-guided systematic review (Web of Science, 2005-2025) and compiled a georeferenced dataset of canine E. granulosus s.l. infections derived from published field studies. After data curation and harmonization, 169 location-year records (representing 159 unique spatial locations in SaTScan) were included in the space-time analysis. Space-time cluster detection using SaTScan (Bernoulli model; annual resolution) identified 14 clusters, of which 13 were statistically significant (P < 0.001). After excluding zero-radius clusters, seven regional clusters were retained for spatial interpretation, of which six were statistically significant. Major clusters were located in East/Central Asia, South America, Europe-North Africa, and the Middle East, indicating geographically structured and temporally bounded patterns of elevated infection risk rather than a homogeneous global distribution. In parallel, ecological niche modelling using Maxent (WorldClim bioclimatic variables and elevation; 2.5 arc-min resolution; 10 bootstrap replicates) demonstrated good model performance (training AUC = 0.914; test AUC = 0.910). Annual mean temperature (54.8% contribution) and elevation (23.5%) were the dominant predictors, with additional contributions from precipitation-related variables. Predicted suitability showed a unimodal response to climatic gradients, with highest suitability under moderate temperature and precipitation conditions. Using literature-derived records from published studies, our analyses identified spatial clusters and environmental variables associated with the distribution of canine E. granulosus s.l. These findings provide a broad, evidence-based risk assessment rather than a fully representative global surveillance picture. Overall, this integrated framework combining space-time clustering and ecological niche modelling provides a robust, evidence-based approach to identify priority areas for surveillance and targeted control of canine-mediated transmission.