Research focusing on the computational analysis and optimization of existing bio-emergency response plans has been gaining momentum due to the constant threat of adverse events, including the accidental or deliberate release of biochemical substances. Demographic indicators of vulnerability, such as lack of personal or public transportation and language barriers, have been identified by the Centers for Disease Control and Prevention (CDC) and in the Pandemic and All-Hazards Preparedness Act (PAHPA). Although a response plan may seem feasible when considering the spatial distribution of Points of Dispensing (PODs) within a given geographic region, overall population density, and predicted traffic demands, it may fail to reach specific subpopulations, thereby resulting in access disparities. The proposed study is a collaborative effort involving researchers with expertise in social-behavioral epidemiology, medical geography, and computational science. The overarching goal of the proposed study is to minimize access disparities in existing bio-emergency response plans. The focus of this proposal is on the development of computational methods that facilitates the identification of vulnerable populations and guide the modification of existing response plan so as to minimize populations that are not reached by a particular response plan developed for a specific geographic region. The primary goal of the proposed work is to augment methods for response plan design and analysis and to further refine the planning process to minimize access disparities by specifically addressing relevant vulnerabilities during the planning process. The study will yield optimization algorithms to identify possible modifications to POD placement and corresponding resource allocation to facilitate the inclusion of populations with diverse needs. The implementation of resulting response plans shall reduce health disparities by minimizing access disparities to POD locations and services. The implications of the proposed study include an improvement of "best practices" in response plan development and analysis. Specifically, the proposed computational tools shall standardize the planning process to maximize the reach of bio-emergency response plans with respect to the spatial distribution of diverse populations with specific vulnerabilities. The study utilizes data from the state of Texas and its counties to design, integrate, implement, and to evaluate.
The study will yield computational methods to modify existing bio-emergency response plans with specific placement of Point of Dispensing (POD) and corresponding resource allocation to facilitate the inclusion of populations with diverse needs, thereby minimizing access disparities. The implications of the proposed study include an improvement of best practices in response plan development and analysis. The proposed computational tools shall standardize the planning process to maximize the reach of bio-emergency response plans with respect to the spatial distribution of diverse populations with specific vulnerabilities.
|O'Neill 2nd, Martin; Mikler, Armin R; Indrakanti, Saratchandra et al. (2014) RE-PLAN: An Extensible Software Architecture to Facilitate Disaster Response Planning. IEEE Trans Syst Man Cybern Syst 44:1569-1583|