Stochastic risk analysis of influenza pandemic on interdependent workforce sectors using input-output modeling

• Main Author: El Haimar, Amine
• Language: EN
• Published: The George Washington University 2015
• Subjects: Operations research
• Online Access: http://pqdtopen.proquest.com/#viewpdf?dispub=3739501

<p> Outbreaks of infectious diseases, such as influenza pandemics, can have significant impacts on interdependent economic sectors, and therefore lead to major economic losses. Based on findings from the 2009 A H1N1 influenza pandemic in the National Capital Region (NCR), this research work presents a risk analysis framework using Input-Output modeling. The present modeling enables to take into consideration the dynamic interdependencies between sectors in an economic system in addition to the inherent characteristics of the economic sectors. The risk of the influenza disaster is captured by two risk metrics. First, there is level of inoperability, which represents the percentage difference between the ideal output and the degraded output. Economic loss measures the dollar value of the degraded output. A primary contribution of this work revolves around the modeling of uncertainties regarding the occurrence of a new perturbation during the recovery of interdependent economic sectors and the resulting consequences due to influenza pandemic. The effect of the new perturbation depends on the nature of the disruption and the probability of its occurrence. The new disruption could lead to either the improvement or the deterioration of the economic sectors during their recovery horizon.</p><p> The level of inoperability of the economic sectors is assessed throughout their recovery horizon from the initial outbreak of the disaster until the after-disaster point of time using a dynamic model. Moreover, the inoperability level values are used to quantify the cumulative economic losses incurred by the sectors during the recovery period. Also, an uncertainty analysis approach is introduced to account for any new perturbation occurring during the recovery horizon of economic sectors. Such uncertainty would serve to assess the potential risk of occurrence of new perturbations and their associated ripple effects. Moreover, a decision-making framework is presented to capture the risk level for the economic sectors and their respective risk metrics. The decision support system is based on a large database of level of inoperability values and economic loss values generated through the simulations of different recovery periods and trajectories.</p>