At-Risk Populations and Biological Emergency Planning: Complexities in the Who and Where of Vulnerability

Authors: Brian T Richardson*, University of North Texas
Topics: Hazards and Vulnerability, Spatial Analysis & Modeling
Keywords: At-risk, vulnerability, bio-emergency, spatial disaggregation, spatial mismatch
Session Type: Paper
Day: 4/13/2018
Start / End Time: 5:20 PM / 7:00 PM
Room: Napoleon C2, Sheraton 3rd Floor
Presentation File: No File Uploaded


In 2006, the United States Congress passed the Pandemic All-Hazards Preparedness Act (PAHPA) which mandated that all emergency preparedness planning shall address at-risk populations. Further, in 2013, the reauthorization of this act, known as PAHPRA, defined at-risk individuals as “children, older adults, pregnant women, and individuals who may need additional response assistance.” This vague definition leaves emergency managers, planners, and public health officials with the difficult task of understanding what it means to be at-risk and what that subsequent knowledge informs in the scope of emergency planning. With the increase of terrorist activity around the globe, planning for a biological emergency has become more important than ever. In order to properly allocate resources for a bio-emergency, planners must understand who within their population is considered at-risk and where these populations are located. In the context of a bio-emergency, this research focuses on two main questions: How should emergency managers, planners, and public health administrators collect, analyze, and use at-risk (vulnerable) population data when making resource allocation decisions? Are there negative implications when using common geographic practices to spatially quantify these populations? My thesis addresses these questions by creating a basic framework, or taxonomy, of vulnerability that will inform resource allocation decisions based on at-risk populations at the local scale. Further, areal interpolation and dasymetric mapping techniques are tested, using a synthetic data set, to highlight issues concerning these common disaggregation methods in regards to at-risk population distribution.

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