NIH 1R01 LM011647-01 | Center for Computational Epidemiologyand Response Analysis (CeCERA)

NIH 1R01 LM011647-01

"Minimizing Access Disparities in Bio Emergency Response Planning"


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 Prevention (CDC) and in the Pandemic and All-Hazards Preparedness Act (PAHPA). 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. 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 recognizes language, 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 population to be served is critical and has thus far not been developed.

The large amount of data required for response plan design suggests that computational tools can aid
public health experts in preparing for bio-emergencies. Further, the efficient and effective arrangement of PODs in a given locale dictates the use of Geographic Information Systems (GIS) to incorporate spatial and demographic information. The proposed study is a collaborative effort that brings together researchers with expertise in social-behavioral epidemiology, medical geography, and computational science. The primary goal of the proposed work is to build upon the RE-PLAN Framework for response plan analysis, 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:

Specific Aims

  • Aim 1:
    Design and implement computational methodology to evaluate reach and efficacy of existing response plans in populations with diverse vulnerabilities as defined by the CDC and PAHPA.
    • Sub Aim 1(a):
      Develop computational methodology to quantify access disparities using indicators recognized by the CDC and the PAHPA.
    • Sub Aim 1(b):
      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 2(a):
      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 2(b):
      Adjust and optimize the placement of specific POD resources (i.e. language translators) to address the needs of vulnerable populations.
  • Aim 3:
    Integrate the analysis and optimization methodologies developed in Aims 1 and 2 into a computational framework.
    • Sub Aim 3(a):
      Implement methodologies into a unified computational framework.
    • Sub Aim 3(b):
      Conduct continual user-centered usability testing to maximize software utility.
    • Sub Aim 3(c):
      Validate response plans by testing against available regional data sources including U.S. Census demographics and health data.

Project Implications

The study will yield optimization algorithms to identify POD placement and corresponding resource allocation 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 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. The proposed methods will be applicable across different geographic regions in the US with different characteristics, thus facilitating coordination of planning and response.


Contact Information

Armin R. Mikler
(940) 565-4279