Pandemic Recovery Analysis Using the Dynamic Inoperability Input‐Output Model
Identifieur interne : 002D37 ( Main/Exploration ); précédent : 002D36; suivant : 002D38Pandemic Recovery Analysis Using the Dynamic Inoperability Input‐Output Model
Auteurs : Joost R. Santos [États-Unis] ; Mark J. Orsi [États-Unis] ; Erik J. Bond [États-Unis]Source :
- 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
Affiliations:
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resilience</term><term>Economic sectors</term><term>Economic structure</term><term>Engineering management</term><term>Executive order</term><term>Factors 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 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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><affiliations><list><country><li>États-Unis</li></country><region><li>District de Columbia</li><li>Virginie</li></region></list><tree><country name="États-Unis"><region name="District de Columbia"><name sortKey="Santos, Joost R" sort="Santos, Joost R" uniqKey="Santos J" first="Joost R." last="Santos">Joost R. Santos</name></region><name sortKey="Bond, Erik J" sort="Bond, Erik J" uniqKey="Bond E" first="Erik J." last="Bond">Erik J. Bond</name><name sortKey="Orsi, Mark J" sort="Orsi, Mark J" uniqKey="Orsi M" first="Mark J." last="Orsi">Mark J. Orsi</name><name sortKey="Santos, Joost R" sort="Santos, Joost R" uniqKey="Santos J" first="Joost R." last="Santos">Joost R. Santos</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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