Spatiotemporal Data

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.

By capturing the spatio-temporal organization of the data using a graph, GraphScan avoids the challenges associated with trying to “fit” incoming data into moving windows of predefined shapes and sizes. Whereas the popular space-time permutation scan statistic [1] attempts to find clusters within spacetime volumes of predefined shape, GraphScan employs no such preconceptions about the form of the clusters.  Instead, clusters are allowed to “evolve” freely to better reflect the structural properties of the data.  Moreover, GraphScan is capable of tracking possible causal relationships between spatio-temporal events.

Objective

This paper proposes an efficient and flexible algorithm applicable to spatio-temporal aberration detection in public health data.

In previous work, we described a non-disease-specific outbreak simulator for the evaluation of outbreak detection algorithms. This Template-Driven Simulator generates disease patterns using user-defined template functions. Estimation of a template function from real outbreak data would enable researchers to repetitively simulate outbreaks that resemble a single real outbreak. These simulated outbreaks can then be used to evaluate outbreak detection algorithms. To demonstrate template estimation, we employ BARD, a disease-specific outbreak model for outdoor aerosol release of B. anthracis. It uses epidemiological and atmospheric dispersion models in conjunction with geographical and meteorological data to generate anthrax cases. The home census block group and time of visit to an emergency department are available for each simulated case.

Objective

In previous work, we developed a Template-Driven Simulator, which is a non-disease specific outbreak simulator that uses templates to describe the temporal or spatial-temporal pattern of an outbreak. Here we address the problem of estimating the template from outbreak data. We then conduct a limited validation of the outbreak simulation model by estimating the template using outbreak data generated from BARD, a sophisticated state-of-the-art anthrax outbreak simulator and detector. This limited validation confirms that the outbreak simulator is capable of generating complicated disease outbreak patterns for evaluating outbreak detection algorithms.

Our toolkit adds statistical trend analysis, interactive plots, and kernel density estimation to an existing spatio-temporal visualization platform. The goal of these tools is to provide both a quick assessment of the current syndromic levels across a large area and then allow the analyst to view the actual data for a specific region or hospital over a period of time along with an indication as to whether or not a given data point is statistically significant. The sample data used for this toolkit come from over 70 emergency rooms throughout the state of Indiana.

Objective

This paper presents a toolkit designed to aid in the assessment of disease outbreak by visualizing spatiotemporal trends and interactively displaying detailed statistical data.