RE-PLAN: An Extensible Software Architecture to Facilitate Disaster Response Planning

Computational tools are needed to make data-driven disaster mitigation planning accessible to planners and policymakers without the need for programming or geographic information systems expertise. To address this problem, we have created modules to facilitate quantitative analyses pertinent to a variety of different disaster scenarios. These modules, which comprise the REsponse PLan ANalyzer framework, may be used to create tools for specific disaster scenarios that allow planners to harness large amounts of disparate data and execute computational models through a point-and-click interface. Bio-E, a user-friendly tool built using this framework, was designed to develop and analyze the feasibility of ad hoc clinics for treating populations following a biological emergency event. In this paper, the design and implementation of the RE-PLAN framework are described, and the functionality of the modules used in the Bio-E biological emergency mitigation tools are demonstrated.

[1]  L. Rotz,et al.  Advances in detecting and responding to threats from bioterrorism and emerging infectious disease , 2004, Nature Medicine.

[2]  Young M. Lee Analyzing dispensing plan for emergency medical supplies in the event of bioterrorism , 2008, 2008 Winter Simulation Conference.

[3]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[4]  A. Hulth,et al.  Web Queries as a Source for Syndromic Surveillance , 2009, PloS one.

[5]  Diane J. Cook,et al.  Monitoring Influenza Trends through Mining Social Media , 2009, BIOCOMP.

[6]  P. Baccam,et al.  Public health response to an anthrax attack: an evaluation of vaccination policy options. , 2007, Biosecurity and bioterrorism : biodefense strategy, practice, and science.

[7]  Armin R. Mikler,et al.  A Novel Space Partitioning Algorithm to Improve Current Practices in Facility Placement , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[8]  Nathaniel Hupert,et al.  Recommendations for Modeling Disaster Responses in Public Health and Medicine: A Position Paper of the Society for Medical Decision Making , 2009, Medical decision making : an international journal of the Society for Medical Decision Making.

[9]  Brian A. Jackson,et al.  Initial Evaluation of the Cities Readiness Initiative , 2009 .

[10]  Rita Mrvos,et al.  Disease surveillance and nonprescription medication sales can predict increases in poison exposure , 2008, Journal of Medical Toxicology.

[11]  David L. Craft,et al.  Emergency response to an anthrax attack , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Armin R. Mikler,et al.  Computational Tools For EvaluatingBioemergency Contingency Plans , 2009 .

[13]  Daniel Zeng,et al.  Syndromic Surveillance Data Sources and Collection Strategies , 2009, Infectious Disease Informatics.

[14]  Brian McCue,et al.  Optimizing a District of Columbia Strategic National Stockpile dispensing center. , 2005, Journal of public health management and practice : JPHMP.

[15]  Armin R. Mikler,et al.  RE-PLAN: a computational framework for REsponse PLan ANalysis , 2010, Int. J. Funct. Informatics Pers. Medicine.

[16]  Chien-Hung Chen,et al.  Modeling and Optimizing the Public-Health Infrastructure for Emergency Response , 2009, Interfaces.

[17]  Franz Aurenhammer,et al.  Voronoi diagrams—a survey of a fundamental geometric data structure , 1991, CSUR.

[18]  Jeffrey W. Herrmann,et al.  A Routing and Scheduling Approach for Planning Medication Distribution , 2009 .

[19]  David L. Craft,et al.  Emergency response to a smallpox attack: The case for mass vaccination , 2002, Proceedings of the National Academy of Sciences of the United States of America.