Developing and evaluating outbreak detection is challenging for many reasons. A central difficulty is that the data the detection algorithms are “trained” on are often relatively short historical samples and thus do not represent the full range of possible background scenarios. Once developed, the same dearth of historical data complicates evaluation. In systems where only a count of cases is provided, plausible synthetic data are relatively easy to generate. When precise location data is available, simple approaches to generating hypothetical cases is more difficult.
Advances in epidemiological modeling have allowed for increasingly realistic simulations of infectious disease spread in highly detailed synthetic populations. These agent-based simulations are capable of better representing real-world stochastic disease transmission process and thus show highly variable results even under identical initial conditions. Due to their ability to mimic a wide range outcomes and more fully represent the unknowns in a system, models of this class have become increasingly used to help inform decisions about public policies about hypothetical situations (eg pandemic influenza [1]). This characteristic also makes them a powerful tool to represent the processes that create surveillance information.
Objective
Developing and evaluating detection algorithms in noisy surveillance data is complicated by a lack of realistic noise, meaning the surveillance data stream when nothing of public health interest is happening. These jobs are even more complex when data on the precise location of cases is available. This paper describes a methodology for plausible generation of such noise using agent-based models of infectious disease transmission based on highly resolved dynamic social networks.