Optimal Investment in Prevention and Recovery for Mitigating Epidemic Risks.

The worldwide healthcare and economic crisis caused by the COVID-19 pandemic highlights the need for a deeper understanding of investing in the mitigation of epidemic risks. To address this, we built a mathematical model to optimize investments into two types of measures for mitigating the risks of epidemic propagation: prevention/containment measures and treatment/recovery measures. The new model explicitly accounts for the characteristics of networks of individuals, as a critical element of epidemic propagation. Subsequent analysis shows that, to combat an epidemic that can cause significant negative impact, optimal investment in either category increases with a higher level of connectivity and intrinsic loss, but it is limited to a fraction of that total potential loss. However, when a fixed and limited mitigation investment is to be apportioned among the two types of measures, the optimal proportion of investment for prevention and containment increases when the investment limit goes up, and when the network connectivity decreases. Our results are consistent with existing studies and can be used to properly interpret what happened in past pandemics as well as to shed light on future and ongoing events such as COVID-19.

Organizational Resilience to Disruption Risks: Developing Metrics and Testing Effectiveness of Operational Strategies.

This study draws from the system resilience literature to propose three different metrics for evaluating the resilience performance of organizations against disruptions: the initial loss due to the disruption, the maximum loss, and the total loss over time. In order to show the usefulness of the developed metrics in practice, we deploy these metrics to study the effectiveness of two resilience strategies: maintaining operational slack and broadening operational scope, by empirically analyzing the performance of manufacturing firms that experienced a disruption during the period from 2005 to the end of 2014. The results show that maintaining certain aspects of operational slack and broadening business scope and geographic scope can affect these different metrics in different ways. Our results help decisionmakers in risk management to gain a better understanding of the conditions under which the recommended strategies actually improve organizations' resilience, as well as the ways in which they may do so.

Building an Interdisciplinary Team for Disaster Response Research: A Data-Driven Approach.

Building an interdisciplinary team is critical to disaster response research as it often deals with acute onset events, short decision horizons, constrained resources, and uncertainties related to rapidly unfolding response environments.  This article examines three teaming mechanisms for interdisciplinary disaster response research, including ad hoc and/or grant proposal driven teams, research center or institute based teams, and teams oriented by matching expertise toward long-term collaborations. Using hurricanes as the response context, it further examines several types of critical data that require interdisciplinary collaboration on collection, integration, and analysis. Last, suggesting a data-driven approach to engaging multiple disciplines, the article advocates building interdisciplinary teams for disaster response research with a long-term goal and an integrated research protocol.

Allocating Resources to Enhance Resilience, with Application to Superstorm Sandy and an Electric Utility.

This article constructs a framework to help a decisionmaker allocate resources to increase his or her organization's resilience to a system disruption, where resilience is measured as a function of the average loss per unit time and the time needed to recover full functionality. Enhancing resilience prior to a disruption involves allocating resources from a fixed budget to reduce the value of one or both of these characteristics. We first look at characterizing the optimal resource allocations associated with several standard allocation functions. Because the resources are being allocated before the disruption, however, the initial loss and recovery time may not be known with certainty. We thus also apply the optimal resource allocation model for resilience to three models of uncertain disruptions: (1) independent probabilities, (2) dependent probabilities, and (3) unknown probabilities. The optimization model is applied to an example of increasing the resilience of an electric power network following Superstorm Sandy.