Space-time detection of disease clusters can be a computationally intensive task which defies the real time constraint for disease surveillance. At the same time, it has been shown that using exact patient locations, instead of their representative administrative regions, result in higher detection rates and accuracy while improving upon detection timeliness. Using such higher spatial resolution data, however, further exacerbates the computational burden on real time surveillance. The critical need for real time processing and interpretation of data dictate highly responsive models that may be best achievable utilizing high performance computing platforms.
Objective
Space-time detection techniques often require computationally intense searching in both the time and space domains. We introduce a high performance computing technique for parallelizing a variation of space-time permutation scan statistic applied to real data of varying spatial resolutions and demonstrate the efficiency of the technique by comparing the parallelized performance under different spatial resolutions with that of serial computation.