Pandemic Recovery Analysis Using the Dynamic Inoperability Input‐Output Model
Identifieur interne : 001C86 ( Istex/Corpus ); précédent : 001C85; suivant : 001C87Pandemic Recovery Analysis Using the Dynamic Inoperability Input‐Output Model
Auteurs : Joost R. Santos ; Mark J. Orsi ; Erik J. BondSource :
- Risk Analysis [ 0272-4332 ] ; 2009-12.
English descriptors
- Teeft :
- Admi, Admi petr brdc, Asce journal, Attack rate, Attack rate pandemic, Available workforce, Brdc, Case study, Cnst, Coal products manufacturing broadcasting, Credit intermediation, Critical infrastructures, Current analysis, Different sectors, Diim, Direct effects, Direct perturbations, Disease control, Disruption, Dynamic analysis, Dynamic extension, Dynamic inoperability model, Dynamic interdependency model, Dynamic model, Economic analysis, Economic effects, Economic impact, Economic impacts, Economic loss, Economic losses, Economic resilience, Economic sectors, Economic structure, Engineering management, Executive order, Factors need, Federal reserve banks, Further enhancements, General accounting, General population, George washington university, Health care services, Homeland security, Indirect effects, Industry resilience, Infrastructure, Infrastructure interdependencies, Infrastructure security partnership, Infrastructure systems, Initial condition, Initial inoperability, Initial inoperability level, Initial inoperability value, Inoperability, Inoperability level, Inoperability levels, Inoperability modeling, Inoperable sectors, Interdependency, Interdependency matrix, Interdependency model, Interdependent sectors, Interesting behavior, Intermediation, International journal, Matrix, Maximum inoperability, Mineral product manufacturing, Mngt, Modeling, Multiple sectors, Nancial vehicles, Next step, October, Orsi, Other sectors, Other services, Other services sector, Othr, Pandemic, Pandemic preparedness, Pandemic recovery analysis, Pandemic scenario, Pandemic scenario recovery period, Particular sector, Parts manufacturing, Peak inoperability, Perturbation, Perturbation inputs, Petr, Production levels, Professional services, Professional services sector, Professional services sectors, Real estate, Recovery period, Recovery process, Recovery time, Regional multiplier system, Relative size, Reserve banks, Residential care facilities, Resilience, Resilience matrix, Retail trade, Risk analysis, Risk assessment, Risk assessment questions, Risk management, Risk scenarios, Rtrd, Scenario, Sector, Sector inoperability, Sector interdependencies, Static model, Support services, Support services petroleum, Systems engineering, Technical services, Technical services construction, Telecommunication, Transportation projects, Unavailability, Wholesale trade, Workforce, Workforce accounting, Workforce availability, Workforce element, Workforce inoperability, Workforce levels, Workforce perturbation, Workforce sectors, Workforce unavailability, World health organization, Wtrd.
Abstract
Economists have long conceptualized and modeled the inherent interdependent relationships among different sectors of the economy. This concept paved the way for input‐output modeling, a methodology that accounts for sector interdependencies governing the magnitude and extent of ripple effects due to changes in the economic structure of a region or nation. Recent extensions to input‐output modeling have enhanced the model's capabilities to account for the impact of an economic perturbation; two such examples are the inoperability input‐output model(1,2) and the dynamic inoperability input‐output model (DIIM).(3) These models introduced sector inoperability, or the inability to satisfy as‐planned production levels, into input‐output modeling. While these models provide insights for understanding the impacts of inoperability, there are several aspects of the current formulation that do not account for complexities associated with certain disasters, such as a pandemic. This article proposes further enhancements to the DIIM to account for economic productivity losses resulting primarily from workforce disruptions. A pandemic is a unique disaster because the majority of its direct impacts are workforce related. The article develops a modeling framework to account for workforce inoperability and recovery factors. The proposed workforce‐explicit enhancements to the DIIM are demonstrated in a case study to simulate a pandemic scenario in the Commonwealth of Virginia.
Url:
DOI: 10.1111/j.1539-6924.2009.01328.x
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ISTEX:E9F2695E6FD55BF80A55F5D6206D192388EB37B4Le document en format XML
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need</term><term>Federal reserve banks</term><term>Further enhancements</term><term>General accounting</term><term>General population</term><term>George washington university</term><term>Health care services</term><term>Homeland security</term><term>Indirect effects</term><term>Industry resilience</term><term>Infrastructure</term><term>Infrastructure interdependencies</term><term>Infrastructure security partnership</term><term>Infrastructure systems</term><term>Initial condition</term><term>Initial inoperability</term><term>Initial inoperability level</term><term>Initial inoperability value</term><term>Inoperability</term><term>Inoperability level</term><term>Inoperability levels</term><term>Inoperability modeling</term><term>Inoperable sectors</term><term>Interdependency</term><term>Interdependency matrix</term><term>Interdependency model</term><term>Interdependent sectors</term><term>Interesting behavior</term><term>Intermediation</term><term>International 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This concept paved the way for input‐output modeling, a methodology that accounts for sector interdependencies governing the magnitude and extent of ripple effects due to changes in the economic structure of a region or nation. Recent extensions to input‐output modeling have enhanced the model's capabilities to account for the impact of an economic perturbation; two such examples are the inoperability input‐output model(1,2) and the dynamic inoperability input‐output model (DIIM).(3) These models introduced sector inoperability, or the inability to satisfy as‐planned production levels, into input‐output modeling. While these models provide insights for understanding the impacts of inoperability, there are several aspects of the current formulation that do not account for complexities associated with certain disasters, such as a pandemic. This article proposes further enhancements to the DIIM to account for economic productivity losses resulting primarily from workforce disruptions. A pandemic is a unique disaster because the majority of its direct impacts are workforce related. The article develops a modeling framework to account for workforce inoperability and recovery factors. The proposed workforce‐explicit enhancements to the DIIM are demonstrated in a case study to simulate a pandemic scenario in the Commonwealth of Virginia.</div></front></TEI><istex><corpusName>wiley</corpusName><keywords><teeft><json:string>inoperability</json:string><json:string>workforce</json:string><json:string>economic loss</json:string><json:string>interdependency</json:string><json:string>scenario</json:string><json:string>diim</json:string><json:string>infrastructure</json:string><json:string>pandemic</json:string><json:string>pandemic scenario</json:string><json:string>resilience</json:string><json:string>october</json:string><json:string>admi</json:string><json:string>cnst</json:string><json:string>attack rate</json:string><json:string>rtrd</json:string><json:string>economic impact</json:string><json:string>modeling</json:string><json:string>brdc</json:string><json:string>othr</json:string><json:string>orsi</json:string><json:string>professional services 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Santos</name><affiliations><json:string>Department of Engineering Management and Systems Engineering, The George Washington University, Washington, DC 20052, USA.</json:string><json:string>E-mail: joost@gwu.edu</json:string></affiliations></json:item><json:item><name>Mark J. Orsi</name><affiliations><json:string>Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, USA.</json:string></affiliations></json:item><json:item><name>Erik J. Bond</name><affiliations><json:string>Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, USA.</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>Input‐output</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>pandemic</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>resilience</value></json:item></subject><articleId><json:string>RISA1328</json:string></articleId><arkIstex>ark:/67375/WNG-PNJJKZWQ-W</arkIstex><language><json:string>eng</json:string></language><originalGenre><json:string>article</json:string></originalGenre><abstract>Economists have long conceptualized and modeled the inherent interdependent relationships among different sectors of the economy. This concept paved the way for input‐output modeling, a methodology that accounts for sector interdependencies governing the magnitude and extent of ripple effects due to changes in the economic structure of a region or nation. Recent extensions to input‐output modeling have enhanced the model's capabilities to account for the impact of an economic perturbation; two such examples are the inoperability input‐output model(1,2) and the dynamic inoperability input‐output model (DIIM).(3) These models introduced sector inoperability, or the inability to satisfy as‐planned production levels, into input‐output modeling. While these models provide insights for understanding the impacts of inoperability, there are several aspects of the current formulation that do not account for complexities associated with certain disasters, such as a pandemic. This article proposes further enhancements to the DIIM to account for economic productivity losses resulting primarily from workforce disruptions. A pandemic is a unique disaster because the majority of its direct impacts are workforce related. The article develops a modeling framework to account for workforce inoperability and recovery factors. The proposed workforce‐explicit enhancements to the DIIM are demonstrated in a case study to simulate a pandemic scenario in the Commonwealth of Virginia.</abstract><qualityIndicators><score>9.472</score><pdfWordCount>7107</pdfWordCount><pdfCharCount>48295</pdfCharCount><pdfVersion>1.4</pdfVersion><pdfPageCount>16</pdfPageCount><pdfPageSize>594 x 783 pts</pdfPageSize><pdfWordsPerPage>444</pdfWordsPerPage><pdfText>true</pdfText><refBibsNative>true</refBibsNative><abstractWordCount>206</abstractWordCount><abstractCharCount>1482</abstractCharCount><keywordCount>3</keywordCount></qualityIndicators><title>Pandemic Recovery Analysis Using the Dynamic Inoperability Input‐Output Model</title><pmid><json:string>19961556</json:string></pmid><genre><json:string>article</json:string></genre><host><title>Risk Analysis</title><language><json:string>unknown</json:string></language><doi><json:string>10.1111/(ISSN)1539-6924</json:string></doi><issn><json:string>0272-4332</json:string></issn><eissn><json:string>1539-6924</json:string></eissn><publisherId><json:string>RISA</json:string></publisherId><volume>29</volume><issue>12</issue><pages><first>1743</first><last>1758</last><total>16</total></pages><genre><json:string>journal</json:string></genre></host><namedEntities><unitex><date><json:string>2009</json:string><json:string>1918</json:string></date><geogName></geogName><orgName><json:string>The Infrastructure Security Partnership (TISP)</json:string><json:string>Department of Homeland Security</json:string><json:string>Department of Systems and Information</json:string><json:string>WHO</json:string><json:string>The George Washington University</json:string><json:string>U.S. Department of Commerce, Bureau of Economic Analysis</json:string><json:string>New Zealand Ministry of Economic Development</json:string><json:string>National Cooperative Highway Research Program (NCHRP)</json:string><json:string>BEA</json:string><json:string>General Accounting Office (GAO)</json:string><json:string>Society for Risk Analysis</json:string><json:string>National Cooperative Highway Research Program</json:string><json:string>University of Virginia</json:string><json:string>World Health Organization (WHO)</json:string><json:string>National Infrastructure Protection Plan</json:string><json:string>Virginia Department of Emergency Management</json:string><json:string>Centers for Disease Control and Prevention (CDC)</json:string><json:string>Bureau of Economic Analysis</json:string><json:string>Department of Engineering Management and Systems Engineering</json:string><json:string>Engineering Systems</json:string><json:string>Department of Homeland Security (DHS)</json:string><json:string>American, the Spanish</json:string><json:string>Bureau of Economic Analysis (BEA)</json:string><json:string>DHS</json:string></orgName><orgName_funder></orgName_funder><orgName_provider></orgName_provider><persName><json:string>REAL Professional</json:string><json:string>Erik J. 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DIIM</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>Blackwell Publishing Inc</publisher><pubPlace>Malden, USA</pubPlace><availability><licence>© 2009 Society for Risk Analysis</licence></availability><date type="published" when="2009-12"></date></publicationStmt><notesStmt><note type="content-type" subtype="article" source="article" scheme="https://content-type.data.istex.fr/ark:/67375/XTP-6N5SZHKN-D">article</note><note type="publication-type" subtype="journal" scheme="https://publication-type.data.istex.fr/ark:/67375/JMC-0GLKJH51-B">journal</note></notesStmt><sourceDesc><biblStruct type="article"><analytic><title level="a" type="main">Pandemic Recovery Analysis Using the Dynamic Inoperability Input‐Output Model</title><title level="a" type="short">Pandemic Recovery Analysis Using the DIIM</title><author xml:id="author-0000" role="corresp"><persName><forename type="first">Joost R.</forename><surname>Santos</surname></persName><affiliation><orgName type="division">Department of Engineering Management and Systems Engineering</orgName><orgName type="institution">The George Washington University</orgName><address><addrLine>Washington</addrLine><addrLine>DC 20052, USA.</addrLine><country key="US" xml:lang="en">UNITED STATES</country></address></affiliation></author><author xml:id="author-0001"><persName><forename type="first">Mark J.</forename><surname>Orsi</surname></persName><affiliation><orgName type="division">Department of Systems and Information Engineering</orgName><orgName type="institution">University of Virginia</orgName><address><addrLine>Charlottesville</addrLine><addrLine>VA, USA.</addrLine><country key="US" xml:lang="en">UNITED STATES</country></address></affiliation></author><author xml:id="author-0002"><persName><forename type="first">Erik J.</forename><surname>Bond</surname></persName><affiliation><orgName type="division">Department of Systems and Information Engineering</orgName><orgName type="institution">University of Virginia</orgName><address><addrLine>Charlottesville</addrLine><addrLine>VA, USA.</addrLine><country key="US" xml:lang="en">UNITED STATES</country></address></affiliation></author><idno type="istex">E9F2695E6FD55BF80A55F5D6206D192388EB37B4</idno><idno type="ark">ark:/67375/WNG-PNJJKZWQ-W</idno><idno type="DOI">10.1111/j.1539-6924.2009.01328.x</idno><idno type="unit">RISA1328</idno><idno type="toTypesetVersion">file:RISA.RISA1328.pdf</idno></analytic><monogr><title level="j" type="main">Risk Analysis</title><title level="j" type="alt">RISK ANALYSIS</title><idno type="pISSN">0272-4332</idno><idno type="eISSN">1539-6924</idno><idno type="book-DOI">10.1111/(ISSN)1539-6924</idno><idno type="book-part-DOI">10.1111/risk.2009.29.issue-12</idno><idno type="product">RISA</idno><idno type="publisherDivision">ST</idno><imprint><biblScope unit="vol">29</biblScope><biblScope unit="issue">12</biblScope><biblScope unit="page" from="1743">1743</biblScope><biblScope 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<ref target="http://www.tei-c.org/">We use TEI</ref></desc></application></appInfo></encodingDesc><profileDesc><abstract xml:lang="en" style="main"><p>Economists have long conceptualized and modeled the inherent interdependent relationships among different sectors of the economy. This concept paved the way for input‐output modeling, a methodology that accounts for sector interdependencies governing the magnitude and extent of ripple effects due to changes in the economic structure of a region or nation. Recent extensions to input‐output modeling have enhanced the model's capabilities to account for the impact of an economic perturbation; two such examples are the inoperability input‐output model<hi rend="superscript">(</hi><ref type="bibr" target="#b1 #b2"><hi rend="superscript">1,2</hi></ref><hi rend="superscript">)</hi> and the dynamic inoperability input‐output model (DIIM).<hi rend="superscript">(</hi><ref type="bibr" target="#b3"><hi rend="superscript">3</hi></ref><hi rend="superscript">)</hi> These models introduced sector inoperability, or the inability to satisfy as‐planned production levels, into input‐output modeling. While these models provide insights for understanding the impacts of inoperability, there are several aspects of the current formulation that do not account for complexities associated with certain disasters, such as a pandemic. This article proposes further enhancements to the DIIM to account for economic productivity losses resulting primarily from workforce disruptions. A pandemic is a unique disaster because the majority of its direct impacts are workforce related. The article develops a modeling framework to account for workforce inoperability and recovery factors. The proposed workforce‐explicit enhancements to the DIIM are demonstrated in a case study to simulate a pandemic scenario in the Commonwealth of Virginia.</p></abstract><textClass><keywords xml:lang="en"><term xml:id="k1">Input‐output</term><term xml:id="k2">pandemic</term><term xml:id="k3">resilience</term></keywords><keywords rend="tocHeading1"><term>Original Research Articles</term></keywords></textClass><langUsage><language ident="en"></language></langUsage></profileDesc><revisionDesc><change when="2019-12-20" who="#istex" xml:id="pub2tei">formatting</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/ark:/67375/WNG-PNJJKZWQ-W/fulltext.txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Wiley, elements deleted: body"><istex:xmlDeclaration>version="1.0" encoding="UTF-8" standalone="yes"</istex:xmlDeclaration><istex:document><component version="2.0" type="serialArticle" xml:lang="en"><header><publicationMeta level="product"><publisherInfo><publisherName>Blackwell Publishing Inc</publisherName><publisherLoc>Malden, USA</publisherLoc></publisherInfo><doi origin="wiley" registered="yes">10.1111/(ISSN)1539-6924</doi><issn type="print">0272-4332</issn><issn type="electronic">1539-6924</issn><idGroup><id type="product" value="RISA"></id><id type="publisherDivision" value="ST"></id></idGroup><titleGroup><title type="main" sort="RISK ANALYSIS">Risk Analysis</title><title type="subtitle">An International Journal</title></titleGroup></publicationMeta><publicationMeta level="part" position="12012"><doi origin="wiley">10.1111/risk.2009.29.issue-12</doi><numberingGroup><numbering type="journalVolume" number="29">29</numbering><numbering type="journalIssue" number="12">12</numbering></numberingGroup><coverDate startDate="2009-12">December 2009</coverDate></publicationMeta><publicationMeta level="unit" type="article" position="9" status="forIssue"><doi origin="wiley">10.1111/j.1539-6924.2009.01328.x</doi><idGroup><id type="unit" value="RISA1328"></id></idGroup><countGroup><count type="pageTotal" number="16"></count></countGroup><titleGroup><title type="tocHeading1"><i>Original Research Articles</i></title></titleGroup><copyright>© 2009 Society for Risk Analysis</copyright><eventGroup><event type="firstOnline" date="2009-12-02"></event><event type="publishedOnlineFinalForm" date="2009-12-02"></event><event type="xmlConverted" agent="Converter:BPG_TO_WML3G version:2.3.5 mode:FullText source:FullText result:FullText" date="2010-04-07"></event><event type="xmlConverted" agent="Converter:WILEY_ML3G_TO_WILEY_ML3GV2 version:4.0.1" date="2014-03-20"></event><event type="xmlConverted" agent="Converter:WML3G_To_WML3G version:4.1.7 mode:FullText,remove_FC" date="2014-11-03"></event></eventGroup><numberingGroup><numbering type="pageFirst" number="1743">1743</numbering><numbering type="pageLast" number="1758">1758</numbering></numberingGroup><correspondenceTo>
*Address correspondence to Joost R. Santos, Department of Engineering Management and Systems Engineering, The George Washington University, 1776 G St. NW Room 164, Washington, DC 20052, USA; tel: (202)‐994‐1249; fax: (202) 994‐0245; <email>joost@gwu.edu</email>.</correspondenceTo><linkGroup><link type="toTypesetVersion" href="file:RISA.RISA1328.pdf"></link></linkGroup></publicationMeta><contentMeta><countGroup><count type="figureTotal" number="15"></count><count type="tableTotal" number="3"></count><count type="formulaTotal" number="7"></count><count type="referenceTotal" number="37"></count><count type="linksCrossRef" number="85"></count></countGroup><titleGroup><title type="main">Pandemic Recovery Analysis Using the Dynamic Inoperability Input‐Output Model</title><title type="shortAuthors"><b>Santos, Orsi, and Bond</b></title><title type="short"><b>Pandemic Recovery Analysis Using the DIIM</b></title></titleGroup><creators><creator creatorRole="author" xml:id="cr1" affiliationRef="#a1" corresponding="yes"><personName><givenNames>Joost R.</givenNames><familyName>Santos</familyName></personName></creator><creator creatorRole="author" xml:id="cr2" affiliationRef="#a2"><personName><givenNames>Mark J.</givenNames><familyName>Orsi</familyName></personName></creator><creator creatorRole="author" xml:id="cr3" affiliationRef="#a2"><personName><givenNames>Erik J.</givenNames><familyName>Bond</familyName></personName></creator></creators><affiliationGroup><affiliation xml:id="a1" countryCode="US"><unparsedAffiliation>Department of Engineering Management and Systems Engineering, The George Washington University, Washington, DC 20052, USA.</unparsedAffiliation></affiliation><affiliation xml:id="a2" countryCode="US"><unparsedAffiliation>Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, USA.</unparsedAffiliation></affiliation></affiliationGroup><keywordGroup xml:lang="en"><keyword xml:id="k1">Input‐output</keyword><keyword xml:id="k2">pandemic</keyword><keyword xml:id="k3">resilience</keyword></keywordGroup><abstractGroup><abstract type="main" xml:lang="en"><p>Economists have long conceptualized and modeled the inherent interdependent relationships among different sectors of the economy. This concept paved the way for input‐output modeling, a methodology that accounts for sector interdependencies governing the magnitude and extent of ripple effects due to changes in the economic structure of a region or nation. Recent extensions to input‐output modeling have enhanced the model's capabilities to account for the impact of an economic perturbation; two such examples are the inoperability input‐output model<sup>(</sup><link href="#b1 #b2"><sup>1,2</sup></link><sup>)</sup> and the dynamic inoperability input‐output model (DIIM).<sup>(</sup><link href="#b3"><sup>3</sup></link><sup>)</sup> These models introduced sector inoperability, or the inability to satisfy as‐planned production levels, into input‐output modeling. While these models provide insights for understanding the impacts of inoperability, there are several aspects of the current formulation that do not account for complexities associated with certain disasters, such as a pandemic. This article proposes further enhancements to the DIIM to account for economic productivity losses resulting primarily from workforce disruptions. A pandemic is a unique disaster because the majority of its direct impacts are workforce related. The article develops a modeling framework to account for workforce inoperability and recovery factors. The proposed workforce‐explicit enhancements to the DIIM are demonstrated in a case study to simulate a pandemic scenario in the Commonwealth of Virginia.</p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Pandemic Recovery Analysis Using the Dynamic Inoperability Input‐Output Model</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>Pandemic Recovery Analysis Using the DIIM</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Pandemic Recovery Analysis Using the Dynamic Inoperability Input‐Output Model</title></titleInfo><name type="personal"><namePart type="given">Joost R.</namePart><namePart type="family">Santos</namePart><affiliation>Department of Engineering Management and Systems Engineering, The George Washington University, Washington, DC 20052, USA.</affiliation><affiliation>E-mail: joost@gwu.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Mark J.</namePart><namePart type="family">Orsi</namePart><affiliation>Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, USA.</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Erik J.</namePart><namePart type="family">Bond</namePart><affiliation>Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA, USA.</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-6N5SZHKN-D">article</genre><originInfo><publisher>Blackwell Publishing Inc</publisher><place><placeTerm type="text">Malden, USA</placeTerm></place><dateIssued encoding="w3cdtf">2009-12</dateIssued><copyrightDate encoding="w3cdtf">2009</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><extent unit="figures">15</extent><extent unit="tables">3</extent><extent unit="formulas">7</extent><extent unit="references">37</extent><extent unit="linksCrossRef">85</extent></physicalDescription><abstract lang="en">Economists have long conceptualized and modeled the inherent interdependent relationships among different sectors of the economy. This concept paved the way for input‐output modeling, a methodology that accounts for sector interdependencies governing the magnitude and extent of ripple effects due to changes in the economic structure of a region or nation. Recent extensions to input‐output modeling have enhanced the model's capabilities to account for the impact of an economic perturbation; two such examples are the inoperability input‐output model(1,2) and the dynamic inoperability input‐output model (DIIM).(3) These models introduced sector inoperability, or the inability to satisfy as‐planned production levels, into input‐output modeling. While these models provide insights for understanding the impacts of inoperability, there are several aspects of the current formulation that do not account for complexities associated with certain disasters, such as a pandemic. This article proposes further enhancements to the DIIM to account for economic productivity losses resulting primarily from workforce disruptions. A pandemic is a unique disaster because the majority of its direct impacts are workforce related. The article develops a modeling framework to account for workforce inoperability and recovery factors. 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