<?xml version='1.0' encoding='UTF-8'?><?xml-stylesheet href='static/style.xsl' type='text/xsl'?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-20T08:44:41Z</responseDate><request verb="GetRecord" identifier="oai:ecommons.cornell.edu:1813/31072" metadataPrefix="dim">https://ecommons.cornell.edu/server/oai/request</request><GetRecord><record><header><identifier>oai:ecommons.cornell.edu:1813/31072</identifier><datestamp>2026-05-14T13:48:58Z</datestamp><setSpec>com_1813_35</setSpec><setSpec>col_1813_47</setSpec></header><metadata><dim:dim xmlns:dim="http://www.dspace.org/xmlns/dspace/dim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.dspace.org/schema/dim.xsd">
   <dim:field mdschema="dc" element="contributor" qualifier="author" lang="en_US">King, Kathleen</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="chair" lang="en_US">Muckstadt, John Anthony</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="committeeMember" lang="en_US">Williamson, David P</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="committeeMember" lang="en_US">Henderson, Shane G.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="committeeMember" lang="en_US">Topaloglu, Huseyin</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="accessioned">2013-01-31T19:44:09Z</dim:field>
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   <dim:field mdschema="dc" element="date" qualifier="issued" lang="en_US">2012-08-20</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="uri">https://hdl.handle.net/1813/31072</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="bibid">7959805</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract" lang="en_US">Public health emergencies require rapid responses from federal, state, and local authorities to prevent widespread mortality and morbidity. However, existing response plans seldom account for the variety of risks and uncertainties inherent in emergency scenarios. Our goal is to construct models that will help policy makers respond effectively to two different potential emergencies: an inhalational anthrax bioterrorist attack and an influenza pandemic. We present a three-echelon capacitated distribution network model of the United States' antibiotic mass-dispensing system for responding to a large-scale anthrax attack. We construct two inventory allocation policies and present a numerical study that compares their performance to that of planned allocation methods. We also present detailed simulation models of an antibiotic-dispensing clinic and the multi-echelon supply chain that operate to support such clinics. Along with the results of our earlier numerical study, these simulations can be used to demonstrate the importance of flexible clinic staffing plans, show the value of centralized command and control during emergency response operations, and provide other public health policy insights. Finally, we investigate the value of using the commercial pharmaceutical supply chain to dispense antiviral medication during an influenza pandemic. We construct historically-based regional antiviral demand scenarios, simulate the performance of the supply chain, and  describe inventory allocation and staffing models that could be used to improve system operations.</dim:field>
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   <dim:field mdschema="dc" element="subject" lang="en_US">emergency preparedness</dim:field>
   <dim:field mdschema="dc" element="subject" lang="en_US">supply chain</dim:field>
   <dim:field mdschema="dc" element="subject" lang="en_US">anthrax</dim:field>
   <dim:field mdschema="dc" element="title" lang="en_US">Logistical Models For Planning And Operating Medical Countermeasure Distribution Networks During Public Health Emergencies</dim:field>
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   <dim:field mdschema="thesis" element="degree" qualifier="discipline">Operations Research</dim:field>
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   <dim:field mdschema="thesis" element="degree" qualifier="level">Doctor of Philosophy</dim:field>
   <dim:field mdschema="thesis" element="degree" qualifier="name">Ph. D., Operations Research</dim:field>
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   	&lt;Title>Logistical Models For Planning And Operating Medical Countermeasure Distribution Networks During Public Health Emergencies&lt;/Title>
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   	&lt;PublicationDate>2012-08-20&lt;/PublicationDate>
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        	&lt;DisplayName>King, Kathleen&lt;/DisplayName>
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    &lt;Keyword>emergency preparedness&lt;/Keyword>
    &lt;Keyword>supply chain&lt;/Keyword>
    &lt;Keyword>anthrax&lt;/Keyword>
   	&lt;Abstract>Public health emergencies require rapid responses from federal, state, and local authorities to prevent widespread mortality and morbidity. However, existing response plans seldom account for the variety of risks and uncertainties inherent in emergency scenarios. Our goal is to construct models that will help policy makers respond effectively to two different potential emergencies: an inhalational anthrax bioterrorist attack and an influenza pandemic. We present a three-echelon capacitated distribution network model of the United States&amp;apos; antibiotic mass-dispensing system for responding to a large-scale anthrax attack. We construct two inventory allocation policies and present a numerical study that compares their performance to that of planned allocation methods. We also present detailed simulation models of an antibiotic-dispensing clinic and the multi-echelon supply chain that operate to support such clinics. Along with the results of our earlier numerical study, these simulations can be used to demonstrate the importance of flexible clinic staffing plans, show the value of centralized command and control during emergency response operations, and provide other public health policy insights. Finally, we investigate the value of using the commercial pharmaceutical supply chain to dispense antiviral medication during an influenza pandemic. We construct historically-based regional antiviral demand scenarios, simulate the performance of the supply chain, and  describe inventory allocation and staffing models that could be used to improve system operations.&lt;/Abstract>
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