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Extreme weather events have significant economic and social impacts, disrupting essential public services like electricity, phone communication, and transportation. This study seeks to understand the performance and resilience of critical infrastructure systems in Houston, Texas, using Hurricane Harvey (2017) as a case study. We surveyed 500 Houston Metropolitan Statistical Area residents after Hurricane Harvey’s landfall about disruption experience in electricity, water, phone/cellphone, internet, public transportation, workplace, and grocery stores. Our household survey data revealed the proportion and duration of disruption in each system. Approximately 70% of respondents reported experiencing electricity outages, while half (51%) had no access to water for up to six days. Two-thirds of surveyed households lacked internet access, and 50% had their phone services disconnected. Additionally, around 71% of respondents were unable to commute to work, and 73% were unable to purchase groceries for their families during this period. We incorporated the household survey responses into the Dynamic Inoperability Input-Output Model (DIIM) to estimate inoperability and economic losses across interconnected sectors. The projected economic loss was estimated to be in the range of $6.7- $9.7 billion when sensitivity analysis is performed with respect to the number of working days. Understanding the resilience of each sector and the inherent interdependencies among them can provide beneficial insight to policymakers for disaster risk management, notably preparedness and recovery planning for future events. 

Artificial intelligence (AI) methods have revolutionized and redefined the landscape of data analysis in business, healthcare, and technology. These methods have innovated the applied mathematics, computer science, and engineering fields and are showing considerable potential for risk science, especially in the disaster risk domain. The disaster risk field has yet to define itself as a necessary application domain for AI implementation by defining how to responsibly balance AI and disaster risk. (1) How is AI being used for disaster risk applications; and how are these applications addressing the principles and assumptions of risk science, (2) What are the benefits of AI being used for risk applications; and what are the benefits of applying risk principles and assumptions for AI‐based applications, (3) What are the synergies between AI and risk science applications, and (4) What are the characteristics of effective use of fundamental risk principles and assumptions for AI‐based applications? This study develops and disseminates an online survey questionnaire that leverages expertise from risk and AI professionals to identify the most important characteristics related to AI and risk, then presents a framework for gauging how AI and disaster risk can be balanced. This study is the first to develop a classification system for applying risk principles for AI‐based applications. This classification contributes to understanding of AI and risk by exploring how AI can be used to manage risk, how AI methods introduce new or additional risk, and whether fundamental risk principles and assumptions are sufficient for AI‐based applications.

 

Critical infrastructures are ubiquitous and their interdependencies have become more complex leading to their uncertain behaviors in the aftermath of disasters. The article develops an integrated economic input–output model that incorporates household‐level survey data from Hurricane Sandy, which made its landfall in 2012. In this survey, 427 respondents who were living in the state of New Jersey during Hurricane Sandy were used in the study. The integration of their responses allowed us to show the probability and duration of various types of critical infrastructure failures due to a catastrophic hurricane event and estimate the economic losses across different sectors. The percentage of disruption and recovery period for various infrastructure systems were extracted from the survey, which were then utilized in the economic input–output model comprising of 71 economic sectors. Sectors were then ranked according to: (i) inoperability, the percentage in which a sector is disrupted relative to its ideal level, and (ii) economic loss, the monetary worth of business interruption caused by the disaster. With the combined infrastructure disruptions in the state of New Jersey, the model estimated an economic loss of $36 billion, which is consistent with published estimates. Results from this article can provide insights for future disaster preparedness and resilience planning.

 

In this study, we utilize an input–output (I–O) model to perform an ex-post analysis of the COVID-19 pandemic workforce disruptions in the Philippines. Unlike most disasters that debilitate physical infrastructure systems, the impact of disease pandemics like COVID-19 is mostly concentrated on the workforce. Workforce availability was adversely affected by lockdowns as well as by actual illness. The approach in this paper is to use Philippine I–O data for multiple years and generate Dirichlet probability distributions for the Leontief requirements matrix (i.e., the normalized sectoral transactions matrix) to address uncertainties in the parameters. Then, we estimated the workforce dependency ratio based on a literature survey and then computed the resilience index in each economic sector. For example, sectors that depend heavily on the physical presence of their workforce (e.g., construction, agriculture, manufacturing) incur more opportunity losses compared to sectors where workforce can telework (e.g., online retail, education, business process outsourcing). Our study estimated the 50th percentile economic losses in the range of PhP 3.3 trillion (with telework) to PhP 4.8 trillion (without telework), which is consistent with independently published reports. The study provides insights into the direct and indirect economic impacts of workforce disruptions in emerging economies and will contribute to the general domain of disaster risk management. 

To manage limited resources available to protect against cybersecurity threats, organizations must use risk management approach to prioritize investments in protection capabilities. Currently, there is no commonly accepted methodology for cybersecurity professionals that considers one of the key elements of risk function - threat landscape - to identify gaps (blinds spots) where cybersecurity protections do not exist and where future investments are needed. This paper discusses a new, threat-based approach for evaluation of cybersecurity architectures that allows organizations to look at their cybersecurity protections from the standpoint of an adversary. The approach is based on a methodology developed by the Department of Defense and further expanded by the Department of Homeland Security. The threat-based approach uses a cyber threat framework to enumerate all threat actions previously observed in the wild and scores protections (cybersecurity architectural capabilities) against each threat action for their ability to: a) detect; b) protect against; and c) help in recovery from the threat action. The answers form a matrix called capability coverage map - a visual representation of protections coverage, gaps, and overlaps against threats. To allow for prioritization, threat actions can be organized in a threat heat map - a visual representation of threat actions' prevalence and maneuverability that can be overlaid on top of a coverage map. The paper demonstrates a new threat modeling methodology and recommends future research to establish a decision-making framework for designing cybersecurity architectures (capability portfolios) that maximize protections (described as coverage in terms of protect, detect, and respond functions) against known cybersecurity threats.