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.

Public Health Relevance

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. 2. Specific Aims Research focusing on the computational analysis and optimization of existing bio-emergency response plans has been gaining momentum due to the 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 Preven- tion (CDC) and in the Pandemic and All-Hazards Preparedness Act (PAHPA)[71]. A response plan may seem feasible when considering the spatial distribution of Points of Dispensing (PODs) within a given geographic re- gion, overall population density, and predicted traffic demands. However, the plan may fail to serve particular subpopulations, consequently resulting in Access Disparities during a bio-emergency. Differences stemming from social, behavioral, cultural, economic, and health characteristics of diverse subpopulations may induce the need for additional targeted resources in a bio-emergency response plan. The CDC [16] recognizes lan- guage, literacy, medical conditions and disabilities (physical, mental, cognitive, or sensory), isolation (cultural, geographic, or social), and age as major indicators of vulnerability, which may impede access to the assigned PODs during a bio-emergency. In order to develop an effective bio-emergency response plan that minimizes access disparities for vulnerable subpopulations, methodology that addresses the aggregate needs of the pop- ulation to be served is critical and has thus far not been developed. The large amount of data that must be considered in the process of response plan design necessitates the development of computational tools to aid public health experts in preparing for potential bio-emergencies. Further, the efficient and effective arrangement of PODs in a given locale dictates the use of Geographic In- formation Systems (GIS) to incorporate spatial and demographic information. The proposed study is a collab- orative effort that brings together researchers with expertise in social-behavioral epidemiology, medical geog- raphy, and computational science. The primary goal of the proposed work is to build upon our successful de- velopment of basic infrastructure for response plan analysis [52][59] and to further refine the planning process to minimize access disparities by specifically addressing relevant vulnerabilities in the response plan design. The study is structured into the following specific aims: Aim 1: Design and implement computational methodology to evaluate reach and efficacy of existing re- sponse plans in populations with diverse vulnerabilities as defined by the CDC and PAHPA. Sub Aim 1a: develop computational methodology to quantify access disparities using indicators recognized by the CDC and the PAHPA. Sub Aim 1b: develop computational methodology that integrates public transportation infrastructure into the response plan design process as part of the vulnerability analysis. Aim 2: Optimize reach and efficacy of response plans for populations with diverse vulnerabilities. Sub Aim 2a: adjust and optimize POD placement to increase plans'reach by means of public transportation and add/modify infrastructural components such as bus routes or stops to minimize access disparities. Sub Aim 2b: adjust and optimize the placement of specific POD resources to address the needs of vulnerable populations. Aim 3: Integrate the analysis and optimization methodologies developed in Aims 1 and 2 into a compu- tational framework for deployment and evaluation at selected Texas Health Departments. Sub Aim 3a: integrate methodologies into a unified computational framework for deployment at test sites. Sub Aim 3b: validate response plans by testing against available regional data sources including Census de- mographics and health data. Project Implications The study will yield optimization algorithms to identify POD placement and corresponding resource alloca- tion under specified resource and time constraints efficiently. The implementation of resulting response plans shall reduce access disparities. The implications of the proposed study include an improvement of best prac- tices 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 distribu- tion of diverse populations with specific vulnerabilities. The proposed methods will be applicable across dif- ferent geographic regions in the US with different characteristics, thus facilitating coordination of planning and response. DESCRIPTION (provided by applicant): 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.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
5R01LM011647-02
Application #
8708210
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Vanbiervliet, Alan
Project Start
Project End
Budget Start
Budget End
Support Year
2
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of North Texas
Department
Biostatistics & Other Math Sci
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
City
Denton
State
TX
Country
United States
Zip Code
76203
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