Risk-Based Input-Output Modeling and Uncertainty Analysis of Hurricane Impacts on Interdependent Regional Workforce Systems

Hurricanes have the potential to damage critical infrastructure systems, disrupt workforce and commodity flows, and can cause adverse socioeconomic impacts on the affected regions. Workforce disruptions in the aftermath of a hurricane can degrade regional productivity because the majority of business operations are labor-dependent. Furthermore, the recovery process is further exacerbated by the inherent interdependencies among economic sectors, which give rise to direct and indirect economic losses in the affected regional economy. This dissertation research extends the economic input-output (I-O) model to formulate a disaster recovery model for assessing the economic losses triggered by workforce disruptions. The research develops a risk-based framework that can guide the process of assessing and managing hurricane impacts on regional interdependent systems. Furthermore, this research presents an impact analysis model to assess the uncertainties associated with workforce recovery. The uncertainty in workforce disruptions is linked to hurricane intensity levels inducing a statistical dependence relationship between hurricane intensity and the recovery period estimates for each workforce sector. This research, to the best of our knowledge, demonstrates the first attempt to integrate such a statistical dependence relationship with an economic I-O modeling approach. Additionally, the resulting methodology is capable of identifying and prioritizing the most critical workforce sectors on the basis of economic loss and sector inoperability metrics. The identification of such critical sectors supports the decision-making process by narrowing the focus on sectors that incur the greatest production losses due to workforce unavailability