There is limited closed-form statistical theory to indicate how well the prospective space-time permutation scan statistic will perform in the detection of localized excess illness activity. Instead, detection methods can be applied to simulated data to gain insight about detection performance. Such results are dependent on the way outbreaks are simulated and the nature of the background data. As an alternative, we explore an empirical approach in which the membership of a large health plan is used to represent a community and detection performance is assessed in samples from the larger group.
Objective
Our goal was to assess the impact of sentinel sample size and criteria for a signal on performance of daily prospective space-time permutation detection by comparing results in varying size random samples from a large health plan to results found in the full membership.