Pandemic wave trends in COVID-19 cases, mobility reduction, and climate parameters in major metropolitan areas in the United States.

In this paper, we analyzed the association among trends in COVID-19 cases, climate, air quality, and mobility changes during the first and second waves of the pandemic in five major metropolitan counties in the United States: Maricopa in Arizona, Cook in Illinois, Los Angeles in California, Suffolk in Massachusetts, and New York County in New York. These areas represent a range of climate conditions, geographies, economies, and state-mandated social distancing restrictions. In the first wave of the pandemic, cases were correlated with humidity in Maricopa, and temperature in Maricopa and Los Angeles. In Suffolk and New York, cases were correlated with mobility changes in recreation, grocery, parks, and transit stations. Neither cases nor death counts were strongly correlated with air quality. Periodic fluctuations in mobility were observed for residential areas during weekends, resulting in stronger correlation coefficients when only weekday datasets were included in the analysis. We also analyzed case-mobility correlations when mobility days were lagged, and found that the strongest correlation in the first wave occurred between 12 and 14 lag days (optimal at 13 days). There was stronger but greater variability in correlation coefficients across metropolitan areas in the first pandemic wave than in the second wave, notably in recreation areas and parks. In the second wave, there was less variability in correlations over lagged time and geographic locations. Overall, we did not find conclusive evidence to support associations between lower cases and climate in all areas. Furthermore, the differences in cases-mobility correlation trends during the two pandemic waves are indicative of the effects of travel restrictions in the early phase of the pandemic and gradual return to travel routines in the later phase. This study highlights the utility of mobility data in understanding the dynamics of disease transmission. It also emphasizes the criticality of timeline and local context in interpreting transmission trends. Mobility data can capture community response to local travel restrictions at different phases of their implementation and provide insights on how these responses evolve over time alongside disease trends.

Managing Physical and Economic Risk for Systems with Multidirectional Network Interdependencies.

Critical infrastructure networks, such as transportation and supply chains, are becoming increasingly interdependent. As the operability of network nodes relies on the operability of connected nodes, network disruptions have the potential to spread across entire networks, having catastrophic consequences in the realms of physical network performance and also economic performance. While risk-informed physical network models and economic models have been well-studied in the literature, there is limited study of how physical features of network performance interact with sector-specific economic performance, particularly as these physical networks recover from disruptions of varying durations. In this article, we create a generalizable framework for integrating Functional Dependency Network Analysis (FDNA) and Dynamic Inoperability Input-Output Models (DIIM), to assess the extent to which disruptions to critical infrastructure could degrade its functionality over a period of time. We demonstrate the framework using disruptive scenarios for a critical transportation network in Virginia, USA. We consider scenarios involving: (a) mild case that is relatively more frequent such as recurring traffic conditions; (b) moderate case involving an incident with a multihour delay, and (c) severe case that is relatively less frequent such as evacuation after a major hurricane. The results will be useful for network managers, policymakers, and stakeholders who are seeking to invest in risk mitigation for network functionality and economic activity.

A Risk Analysis Framework for Cyber Security and Critical Infrastructure Protection of the U.S. Electric Power Grid.

The purpose of this article is to introduce a risk analysis framework to enhance the cyber security of and to protect the critical infrastructure of the electric power grid of the United States. Building on the fundamental questions of risk assessment and management, this framework aims to advance the current risk analysis discussions pertaining to the electric power grid. Most of the previous risk-related studies on the electric power grid focus mainly on the recovery of the network from hurricanes and other natural disasters. In contrast, a disproportionately small number of studies explicitly investigate the vulnerability of the electric power grid to cyber-attack scenarios, and how they could be prevented or mitigated. Such a limited approach leaves the United States vulnerable to foreign and domestic threats (both state-sponsored and "lone wolf") to infiltrate a network that lacks a comprehensive security environment or coordinated government response. By conducting a review of the literature and presenting a risk-based framework, this article underscores the need for a coordinated U.S. cyber security effort toward formulating strategies and responses conducive to protecting the nation against attacks on the electric power grid.

Supply Chain Vulnerability Analysis Using Scenario-Based Input-Output Modeling: Application to Port Operations.

Disruptive events such as natural disasters, loss or reduction of resources, work stoppages, and emergent conditions have potential to propagate economic losses across trade networks. In particular, disruptions to the operation of container port activity can be detrimental for international trade and commerce. Risk assessment should anticipate the impact of port operation disruptions with consideration of how priorities change due to uncertain scenarios and guide investments that are effective and feasible for implementation. Priorities for protective measures and continuity of operations planning must consider the economic impact of such disruptions across a variety of scenarios. This article introduces new performance metrics to characterize resiliency in interdependency modeling and also integrates scenario-based methods to measure economic sensitivity to sudden-onset disruptions. The methods will be demonstrated on a U.S. port responsible for handling $36.1 billion of cargo annually. The methods will be useful to port management, private industry supply chain planning, and transportation infrastructure management.

A stochastic recovery model of influenza pandemic effects on interdependent workforce systems.

Outbreaks of infectious diseases, such as pandemics, can result in adverse consequences and major economic losses across various economic sectors. Based on findings from the 2009 A H1N1 pandemic in the National Capital Region (NCR), this paper presents a recovery analysis for workforce disruptions using economic input-output modeling. The model formulation takes into consideration the dynamic interdependencies across sectors in an economic system in addition to the inherent characteristics of the economic sectors. From a macroeconomic perspective, the risk of the influenza disaster can be modeled using two risk metrics. First, there is the level of inoperability, which represents the percentage difference between the ideal production level and the degraded production level. Second, the economic loss metric represents the financial value associated with the reduced output. The contribution of this work revolves around the modeling of uncertainties triggered by new perturbations to interdependent economic sectors within an influenza pandemic timeline. We model the level of inoperability of economic sectors throughout their recovery horizon from the initial outbreak of the disaster using a dynamic model. Moreover, we use the level of inoperability values to quantify the cumulative economic losses incurred by the sectors within the recovery horizon. Finally, we revisit the 2009 NCR pandemic scenario to demonstrate the use of uncertainty analysis in modeling the inoperability and economic loss behaviors due to time-varying perturbations and their associated ripple effects to interdependent economic sectors.

State of the art in risk analysis of workforce criticality influencing disaster preparedness for interdependent systems.

The objective of this article is to discuss a needed paradigm shift in disaster risk analysis to emphasize the role of the workforce in managing the recovery of interdependent infrastructure and economic systems. Much of the work that has been done on disaster risk analysis has focused primarily on preparedness and recovery strategies for disrupted infrastructure systems. The reliability of systems such as transportation, electric power, and telecommunications is crucial in sustaining business processes, supply chains, and regional livelihoods, as well as ensuring the availability of vital services in the aftermath of disasters. There has been a growing momentum in recognizing workforce criticality in the aftermath of disasters; nevertheless, significant gaps still remain in modeling, assessing, and managing workforce disruptions and their associated ripple effects to other interdependent systems. The workforce plays a pivotal role in ensuring that a disrupted region continues to function and subsequently recover from the adverse effects of disasters. With this in mind, this article presents a review of recent studies that have underscored the criticality of workforce sectors in formulating synergistic preparedness and recovery policies for interdependent infrastructure and regional economic systems.

Modeling uncertainties in workforce disruptions from influenza pandemics using dynamic input-output analysis.

Influenza pandemic is a serious disaster that can pose significant disruptions to the workforce and associated economic sectors. This article examines the impact of influenza pandemic on workforce availability within an interdependent set of economic sectors. We introduce a simulation model based on the dynamic input-output model to capture the propagation of pandemic consequences through the National Capital Region (NCR). The analysis conducted in this article is based on the 2009 H1N1 pandemic data. Two metrics were used to assess the impacts of the influenza pandemic on the economic sectors: (i) inoperability, which measures the percentage gap between the as-planned output and the actual output of a sector, and (ii) economic loss, which quantifies the associated monetary value of the degraded output. The inoperability and economic loss metrics generate two different rankings of the critical economic sectors. Results show that most of the critical sectors in terms of inoperability are sectors that are related to hospitals and health-care providers. On the other hand, most of the sectors that are critically ranked in terms of economic loss are sectors with significant total production outputs in the NCR 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.

Risk-based input-output analysis of influenza epidemic consequences on interdependent workforce sectors.

Outbreaks of contagious diseases underscore the ever-looming threat of new epidemics. Compared to other disasters that inflict physical damage to infrastructure systems, epidemics can have more devastating and prolonged impacts on the population. This article investigates the interdependent economic and productivity risks resulting from epidemic-induced workforce absenteeism. In particular, we develop a dynamic input-output model capable of generating sector-disaggregated economic losses based on different magnitudes of workforce disruptions. An ex post analysis of the 2009 H1N1 pandemic in the national capital region (NCR) reveals the distribution of consequences across different economic sectors. Consequences are categorized into two metrics: (i) economic loss, which measures the magnitude of monetary losses incurred in each sector, and (ii) inoperability, which measures the normalized monetary losses incurred in each sector relative to the total economic output of that sector. For a simulated mild pandemic scenario in NCR, two distinct rankings are generated using the economic loss and inoperability metrics. Results indicate that the majority of the critical sectors ranked according to the economic loss metric comprise of sectors that contribute the most to the NCR's gross domestic product (e.g., federal government enterprises). In contrast, the majority of the critical sectors generated by the inoperability metric include sectors that are involved with epidemic management (e.g., hospitals). Hence, prioritizing sectors for recovery necessitates consideration of the balance between economic loss, inoperability, and other objectives. Although applied specifically to the NCR, the proposed methodology can be customized for other regions.

Multiobjective prioritization methodology and decision support system for evaluating inventory enhancement strategies for disrupted interdependent sectors.

Disruptions in the production of commodities and services resulting from disasters influence the vital functions of infrastructure and economic sectors within a region. The interdependencies inherent among these sectors trigger the faster propagation of disaster consequences that are often associated with a wider range of inoperability and amplified losses. This article evaluates the impact of inventory-enhanced policies for disrupted interdependent sectors to improve the disaster preparedness capability of dynamic inoperability input-output models (DIIM). In this article, we develop the dynamic cross-prioritization plot (DCPP)--a prioritization methodology capable of identifying and dynamically updating the critical sectors based on preference assignments to different objectives. The DCPP integrates the risk assessment metrics (e.g., economic loss and inoperability), which are independently analyzed in the DIIM. We develop a computer-based DCPP tool to determine the priority for inventory enhancement with user preference and resource availability as new dimensions. A baseline inventory case for the state of Virginia revealed a high concentration of (i) manufacturing sectors under the inoperability objective and (ii) service sectors under the economic loss objective. Simulation of enhanced inventory policies for selected critical manufacturing sectors has reduced the recovery period by approximately four days and the expected total economic loss by $33 million. Although the article focuses on enhancing inventory levels in manufacturing sectors, complementary analysis is recommended to manage the resilience of the service sectors. The flexibility of the proposed DCPP as a decision support tool can also be extended to accommodate analysis in other regions and disaster scenarios.

Risk analysis of Safety Service Patrol (SSP) systems in Virginia.

The transportation infrastructure is a vital backbone of any regional economy as it supports workforce mobility, tourism, and a host of socioeconomic activities. In this article, we specifically examine the incident management function of the transportation infrastructure. In many metropolitan regions, incident management is handled primarily by safety service patrols (SSPs), which monitor and resolve roadway incidents. In Virginia, SSP allocation across highway networks is based typically on average vehicle speeds and incident volumes. This article implements a probabilistic network model that partitions "business as usual" traffic flow with extreme-event scenarios. Results of simulated network scenarios reveal that flexible SSP configurations can improve incident resolution times relative to predetermined SSP assignments.

A risk-based approach for identifying key economic and infrastructure systems.

This article introduces approaches for identifying key interdependent infrastructure sectors based on the inventory dynamic inoperability input-output model, which integrates an inventory model and a risk-based interdependency model. An identification of such key sectors narrows a policymaker's focus on sectors providing most impact and receiving most impact from inventory-caused delays in inoperability resulting from disruptive events. A case study illustrates the practical insights of the key sector approaches derived from a value of workforce-centered production inoperability from Bureau of Economic Analysis data.

Estimating Workforce-Related Economic Impact of a Pandemic on the Commonwealth of Virginia.

A pandemic outbreak is one of the major planning scenarios considered by emergency-preparedness policymakers. The consequences of a pandemic can significantly affect and disrupt a large spectrum of workforce sectors in today's society. This paper, motivated by the impact of a pandemic, extends the formulation of the dynamic inoperability input-output model (DIIM) to account for economic perturbations resulting from such an event, which creates a time-varying and probabilistic inoperability to the workforce. A pandemic is a unique disaster, because the majority of its direct impacts are workforce related and it does not create significant direct impact to infrastructure. In light of this factor, this paper first develops a method of translating unavailable workforce into a measure of economic-sector inoperability. While previous formulations of the DIIM only allowed for the specification of an initial perturbation, this paper incorporates the fact that a pandemic can cause direct effects to the workforce over the recovery period. Given the uncertainty associated with the impact of a pandemic, this paper develops a simulation framework to account for the possible variations in realizations of the pandemic. The enhancements to the DIIM formulation are incorporated into a MatLab program and then applied to a case study to simulate a pandemic scenario in the Commonwealth of Virginia.

Pandemic recovery analysis using the dynamic inoperability input-output model.

Economists have long conceptualized and modeled the inherent interdependent relationships among different sectors of the economy. This concept paved the way for input-output modeling, a methodology that accounts for sector interdependencies governing the magnitude and extent of ripple effects due to changes in the economic structure of a region or nation. Recent extensions to input-output modeling have enhanced the model's capabilities to account for the impact of an economic perturbation; two such examples are the inoperability input-output model((1,2)) and the dynamic inoperability input-output model (DIIM).((3)) These models introduced sector inoperability, or the inability to satisfy as-planned production levels, into input-output modeling. While these models provide insights for understanding the impacts of inoperability, there are several aspects of the current formulation that do not account for complexities associated with certain disasters, such as a pandemic. This article proposes further enhancements to the DIIM to account for economic productivity losses resulting primarily from workforce disruptions. A pandemic is a unique disaster because the majority of its direct impacts are workforce related. The article develops a modeling framework to account for workforce inoperability and recovery factors. The proposed workforce-explicit enhancements to the DIIM are demonstrated in a case study to simulate a pandemic scenario in the Commonwealth of Virginia.

International trade inoperability input-output model (IT-IIM): theory and application.

The inoperability input-output model (IIM) has been used for analyzing disruptions due to man-made or natural disasters that can adversely affect the operation of economic systems or critical infrastructures. Taking economic perturbation for each sector as inputs, the IIM provides the degree of economic production impacts on all industry sectors as the outputs for the model. The current version of the IIM does not provide a separate analysis for the international trade component of the inoperability. If an important port of entry (e.g., Port of Los Angeles) is disrupted, then international trade inoperability becomes a highly relevant subject for analysis. To complement the current IIM, this article develops the International Trade-IIM (IT-IIM). The IT-IIM investigates the resulting international trade inoperability for all industry sectors resulting from disruptions to a major port of entry. Similar to traditional IIM analysis, the inoperability metrics that the IT-IIM provides can be used to prioritize economic sectors based on the losses they could potentially incur. The IT-IIM is used to analyze two types of direct perturbations: (1) the reduced capacity of ports of entry, including harbors and airports (e.g., a shutdown of any port of entry); and (2) restrictions on commercial goods that foreign countries trade with the base nation (e.g., embargo).

A framework for linking cybersecurity metrics to the modeling of macroeconomic interdependencies.

Hierarchical decision making is a multidimensional process involving management of multiple objectives (with associated metrics and tradeoffs in terms of costs, benefits, and risks), which span various levels of a large-scale system. The nation is a hierarchical system as it consists multiple classes of decisionmakers and stakeholders ranging from national policymakers to operators of specific critical infrastructure subsystems. Critical infrastructures (e.g., transportation, telecommunications, power, banking, etc.) are highly complex and interconnected. These interconnections take the form of flows of information, shared security, and physical flows of commodities, among others. In recent years, economic and infrastructure sectors have become increasingly dependent on networked information systems for efficient operations and timely delivery of products and services. In order to ensure the stability, sustainability, and operability of our critical economic and infrastructure sectors, it is imperative to understand their inherent physical and economic linkages, in addition to their cyber interdependencies. An interdependency model based on a transformation of the Leontief input-output (I-O) model can be used for modeling: (1) the steady-state economic effects triggered by a consumption shift in a given sector (or set of sectors); and (2) the resulting ripple effects to other sectors. The inoperability metric is calculated for each sector; this is achieved by converting the economic impact (typically in monetary units) into a percentage value relative to the size of the sector. Disruptive events such as terrorist attacks, natural disasters, and large-scale accidents have historically shown cascading effects on both consumption and production. Hence, a dynamic model extension is necessary to demonstrate the interplay between combined demand and supply effects. The result is a foundational framework for modeling cybersecurity scenarios for the oil and gas sector. A hypothetical case study examines a cyber attack that causes a 5-week shortfall in the crude oil supply in the Gulf Coast area.

Extreme risk analysis of interdependent economic and infrastructure sectors.

Willful attacks or natural disasters pose extreme risks to sectors of the economy. An extreme-event analysis extension is proposed for the Inoperability Input-Output Model (IIM) and the Dynamic IIM (DIIM), which are analytical methodologies for assessing the propagated consequences of initial disruptions to a set of sectors. The article discusses two major risk categories that the economy typically experiences following extreme events: (i) significant changes in consumption patterns due to lingering public fear and (ii) adjustments to the production outputs of the interdependent economic sectors that are necessary to match prevailing consumption levels during the recovery period. Probability distributions associated with changes in the consumption of directly affected sectors are generated based on trends, forecasts, and expert evidence to assess the expected losses of the economy. Analytical formulations are derived to quantify the extreme risks associated with a set of initially affected sectors. In addition, Monte Carlo simulation is used to handle the more complex calculations required for a larger set of sectors and general types of probability distributions. A two-sector example is provided at the end of the article to illustrate the proposed extreme risk model formulations.

Modeling the demand reduction input-output (I-O) inoperability due to terrorism of interconnected infrastructures.

Interdependency analysis in the context of this article is a process of assessing and managing risks inherent in a system of interconnected entities (e.g., infrastructures or industry sectors). Invoking the principles of input-output (I-O) and decomposition analysis, the article offers a framework for describing how terrorism-induced perturbations can propagate due to interconnectedness. Data published by the Bureau of Economic Analysis Division of the U.S. Department of Commerce is utilized to present applications to serve as test beds for the proposed framework. Specifically, a case study estimating the economic impact of airline demand perturbations to national-level U.S. sectors is made possible using I-O matrices. A ranking of the affected sectors according to their vulnerability to perturbations originating from a primary sector (e.g., air transportation) can serve as important input to risk management. For example, limited resources can be prioritized for the "top-n" sectors that are perceived to suffer the greatest economic losses due to terrorism. In addition, regional decomposition via location quotients enables the analysis of local-level terrorism events. The Regional I-O Multiplier System II (RIMS II) Division of the U.S. Department of Commerce is the agency responsible for releasing the regional multipliers for various geographical resolutions (economic areas, states, and counties). A regional-level case study demonstrates a process of estimating the economic impact of transportation-related scenarios on industry sectors within Economic Area 010 (the New York metropolitan region and vicinities).