Abstract
A pandemic outbreak is one of the major planning scenarios considered by emergency-preparedness policymakers. The consequences of a pandemic can significantly affect and disrupt a large spectrum of workforce sectors in today's society. This paper, motivated by the impact of a pandemic, extends the formulation of the dynamic inoperability input–output model (DIIM) to account for economic perturbations resulting from such an event, which creates a time-varying and probabilistic inoperability to the workforce. A pandemic is a unique disaster, because the majority of its direct impacts are workforce related and it does not create significant direct impact to infrastructure. In light of this factor, this paper first develops a method of translating unavailable workforce into a measure of economic-sector inoperability. While previous formulations of the DIIM only allowed for the specification of an initial perturbation, this paper incorporates the fact that a pandemic can cause direct effects to the workforce over the recovery period. Given the uncertainty associated with the impact of a pandemic, this paper develops a simulation framework to account for the possible variations in realizations of the pandemic. The enhancements to the DIIM formulation are incorporated into a MatLab program and then applied to a case study to simulate a pandemic scenario in the Commonwealth of Virginia.
Highlights
A PANDEMIC outbreak is one of the major planning scenarios considered by emergency-preparedness policymakers
In an attempt to address the complexities inherent in a pandemic, this paper develops a dynamic and probabilistic model to account for workforce productivity degradations and resulting workforce-induced economic losses
The terms in the formulation in (2) are derived from the Leontief formulation in (1) and are interpreted as follows: c∗ is a perturbation vector expressed in terms of normalized degraded final demand (i.e., “as-planned” final demand minus actual final demand, divided by the as-planned production level), A∗ is the interdependence matrix which indicates the degree of coupling of the industry sectors, and q is the inoperability vector expressed in terms of normalized economic losses
Summary
A PANDEMIC outbreak is one of the major planning scenarios considered by emergency-preparedness policymakers. A dynamic extension to interdependence modeling is needed to describe the temporal nature of sector recoveries pursuant to a disaster, such as in the case of a pandemic scenario. Given the initial inoperabilities caused by a disaster, the dynamic extension gives the trajectory of recovery based on the interdependence and resilience characteristics of the industry sectors. With the looming fear of a pandemic occurring in our present world, it is imperative to develop models for assessing its consequences and for evaluating the efficacy of available mitigation strategies. In an attempt to address the complexities inherent in a pandemic (and in any disasters), this paper develops a dynamic and probabilistic model to account for workforce productivity degradations and resulting workforce-induced economic losses. The same tool can be used for modeling other regions by customizing the underlying economic and demographic databases
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