Input-output modeling with stochastic extensions: An application to an influenza pandemic scenario

Abstract

Disasters such as influenza pandemics can disrupt the operations of interdependent infrastructure and economic sectors, and consequently lead to significant economic losses. The magnitude of such consequences depends on the type, size, and activity of the economic sector. Moreover, the magnitude of such consequences depends on the degree of interdependencies between the economic sectors. This paper presents a simulation and analysis of the impacts of such a disaster on the economic sectors in a given region. We introduce a stochastic simulation model based on the dynamic inoperability input-output model (DIIM) to model the cascading effects of a disruptive event in the U.S. National Capital Region (NCR). The analysis conducted in this work is based on the 2009 H1N1 pandemic data. Two metrics were used to assess the impacts of the influenza pandemic on sectors: (i) inoperability, which is a measure of the percentage gap between the as-planned output and the actual output, and (ii) economic loss, which is a measure of the monetary value of the degraded output. The inoperability and economic loss metrics generate two different rankings of the economic sectors. Findings show that most of the critical sectors in terms of inoperability are sectors that are related to hospitals and healthcare-related providers. On the other hand, most of the sectors that are critically ranked in terms of economic loss are sectors with significant production outputs in the NCR region such as federal government agencies. Therefore, policy recommendations relating to potential mitigation and recovery strategies should take into account the balance between the inoperability and economic loss metrics.