Description
The use of spatially-based methods and algorithms in epidemiology and surveillance presents privacy challenges for researchers and public health agencies. We describe a novel method for anonymizing individuals in public health datasets, by transposing their spatial locations through a process informed by the underlying population density. Further, we measure the impact of blurring patient locations on detection of spatial clustering as measured by the SaTScan purely-spatial Bernoulli scanning statistic.