Principal Investigator: Adam Rose
Other Researchers: Fynnwin Prager, Dan Wei, Shilpika Lahri
Several analytical frameworks exist to perform analyses of the economic impacts of biological risks. These include a set of guidelines and computerized systems associated with formal “terrorism risk assessments” for chemical, biological, radiological, and nuclear (CBRN) threats (see, e.g., BTRA, 2008; ITRA, 2011). They also include decades of research and applications on economic impact analysis. This literature has several branches that include economic welfare analysis (EPA, 2010; Meltzer, 2008), benefit-cost analysis (Boardman et al., 2001; Just et al., 2010) and the more modern term for broad-based impact analysis — economic consequence analysis (ECA) (Rose, 2009, 2014). The various guidelines and approaches have a common core of concepts, but differ significantly in terms of their scope (direct vs. direct plus indirect effects), number of individual impact categories examined within each of the direct/indirect bifurcations, and underlying assumptions. The assumptions often stem from limitations of modeling capabilities and data. Over time, there has been a general movement to broaden the scope to include more types of effects. The economic tools of analysis differ as well between the analytical frameworks, further adding to the distinctions. However, here too the momentum has been toward state of the art models of the entire economy, capable of analyzing the broad range of impacts, as is the case for computable general equilibrium (CGE) analysis (Rose, 2005; Dixon et al., 2010; Rose et al., 2014). The purpose of this report is to further develop the framework for economic consequence analysis as applied to biothreats. The focus is on identifying the impact categories that should be included in a comprehensive assessment needed to improve biosurveillance. Another focus is to identify the best modeling approaches for estimating these impacts. Still another is to identify approaches best suited to real-time analysis and prediction that are key to biosurveillance warnings.