OBJECTIVE To identify the spatial and temporal clusters of Barmah Forest virus (BFV) disease in Queensland in Australia, using geographical information systems and spatial scan statistic (SaTScan).   
 METHODS We obtained BFV disease cases, population and statistical local areas (SLAs) boundary data from Queensland Health and Australian Bureau of Statistics, respectively, during 1992-2008 for Queensland. A retrospective Poisson-based analysis using SaTScan software and method was conducted to identify both purely spatial and space-time BFV disease high-rate clusters. A spatial cluster size of a proportion of the population and a 200 km radius and varying time windows from 1 to 12 months were chosen (for the space-time analysis).   
 RESULTS The spatial scan statistic detected a most likely significant purely spatial cluster (including 23 SLAs) and a most likely significant space-time cluster (including 24 SLAs) in approximately the same location. Significant secondary clusters were also identified from both the analyses in several locations.   
 CONCLUSIONS This study provides evidence of the existence of statistically significant BFV disease clusters in Queensland, Australia. The study also demonstrated the relevance and applicability of SaTScan in analysing ongoing surveillance data to identify clusters to facilitate the development of effective BFV disease prevention and control strategies in Queensland, Australia.
True