Ebert David

Complex, highly parameterized data models are often used to detect syndromic outbreaks. Unfortunately, such models can pose greater maintenance challenges as parameter variations increase. As such, our work focuses on whether day-of-the-week (DoW) effects may (or may not) show little variation across hospitals.

Objective

This paper investigates the existence of the DoW effect across twenty-six hospitals within the Indiana Public Health Emergency Surveillance System. We will consider both the impact of each DoW and the impact of individual hospitals.

Numerous methods have been applied to the problem of modeling temporal properties of disease surveillance data; the ESSENCE system contains a widely used approach (1). STL (2) is a flexible, wellproven method for temporal modeling that decomposes the series into frequency components. A periodic component like DW can be exactly periodic or evolve through time. STL is based on loess (3), which can model a numeric response as a function of any explanatory variables. After the STL modeling of the counts, we will add patient address and produce a timespace modeling using both STL and more general loess methods.

Objective

Use the STL local-regression (loess) decomposition procedure and transformation to model the univariate time-series characteristics of chief-complaint daily counts as a first step in a time and spatial modeling. Develop visualization tools for model display and checking.

This paper describes our integrated visual analytics framework for analyzing both human emergency room data and veterinary hospital data.

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.