Anderson David

Emergency management during a disaster entails innumerable challenges. Each disaster uniquely shapes the types and timing of information needed both to manage the disaster and to measure the impact on available resources, the environment, and community systems. Traditional public health surveillance methods typically preclude providing a real-time, comprehensive estimate of public health impacts related to the disaster while the disaster is unfolding. Traditional methods can also be resource intensive, costly, require active cooperation of medical systems involved in a disaster response, and are often conducted post-disaster.

Syndromic surveillance of emergency department chief complaints and over-the-counter medication sales was reinstituted in the Austin area in the fall of 2010. In 2011, the Austin area was hit with three natural disasters: a winter ice storm; a summer of extreme heat/extended drought; and a week of significant wildfires. Each disaster varied greatly in type, size, intensity, and duration. The Austin/Travis County Health and Human Services Department, in partnership with Austin/Travis County EMS, was able for the first time to provide near-real time data to emergency managers on the potential health impact during each of the 2011 disasters using the syndromic and EMS electronic data systems. The data were used to provide situational awareness and guide selected response actions during the course of the disaster, as well as, document potential areas for future mitigation efforts.

Objective

Using case studies of three natural disasters that occurred in the Austin, Texas Metro area in 2011, demonstrate the role syndromic surveillance and emergency medical services data played during the response to each different type of disaster.

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.