Topological (graph-theoretic) analysis of critical infrastructure networks provides insight on several aspects of resilience. Graph clustering or community detection, which identifies densely connected components in a graph, has been employed for analysis. In this paper, we propose employing consensus clustering, which is a technique to determine consensus from a collection of different clusters on an input, such that the resulting clustering is robust to disruptions, where a disruption is represented as loss of one or more vertices or edges in the graph. Using two critical infrastructure networks as case studies, we empirically demonstrate the need to compute consensus clustering in order to address the drastic changes in the topology due to disruptions in the network.

When new technology, such as artificial intelligence (AI), is introduced into an existing workflow it may impact risk by mitigating some vulnerabilities and threats in the workflow while introducing others. We present a versatile methodology for assessing the vulnerabilities and threats that impact overall risk in a workflow to inform technology integration. Our method involves a four step assessment of risk including a qualitative expert knowledge elicitation, identification of risk components, quantitative data collection and analysis based on a formula for generating a risk score. The quantification of risk can be used to guide technology integration. We describe our methodology and demonstrate its utility by applying it to the Derivative Classification review process. This work was funded by the Department of Energy (DOE).

The US Department of Homeland Security (DHS) defines risk as the "potential for an adverse outcome assessed as a function of threats, vulnerabilities, and consequences associated with an incident, event, or occurrence." National security risk analyses are conducted across a spectrum of threats, such as nuclear terrorism to pandemic diseases. These analyses often rely on historical data and/or data derived from simulation or expert judgment to quantify and model the various elements of risk. This chapter focuses on the evolution of DHS's definition of the elements of security risk, i.e. threat, vulnerability, and consequence. An overview of the modeling methods and approaches typically adopted for characterizing these risk components will also be discussed.

International trade provides an avenue for terrorists to smuggle illicit materials into a target country. Policymakers need models and decision support tools that are both reliable and germane to inform their responses to this. Game theoretic approaches have been used in the analysis of counterterrorism strategies, but the models involved have often been small and abstracted. To address this need, we have developed a multi-game model to account for both security-related and economic aspects of counter-smuggling interdiction efforts. We use different games to represent different types of interactions, and these games are then coupled to each other to form an integrated model. In this paper, we demonstrate the model's capabilities on a representative system of ports (both foreign and domestic), trade routes, and commodities. We specifically investigate the impacts of changes in screening policies at domestic ports, detection device capabilities, shipping subsidies, and worldwide corruption levels. In these studies, we find that economic factors play a large role in the interdiction task and provide correspondingly important tools for deterrence.

The 2017 U.S. National Security Strategy indicates a need to reduce risk and build resilience against targeted and natural hazards. Simultaneously, these targeted and natural hazards pose a risk beyond the immediate system for which they are impacting due to the interconnectivity of our social, cyber, and infrastructure networks. Each stakeholder-including, but not limited to, policy makers and system operators-have competing and cooperative interests for the ultimate functionality of the individual and overarching system. These multi-criteria and multi-stakeholder problems require a risk assessment and resource allocation tool that is generalizable for multiple stakeholders in order to inform optimal decision making practices in an effort to prepare for any given hazard. This research aims to incorporate resilience alternatives alongside risk assessment metrics using an optimization based decision making framework based upon a multiple-criteria decision making (MCDM) methodology. It will demonstrate a Resilient Infrastructure Decision Support (RIDS) toolset with a top-down decision analysis and bottom-up risk assessment. Through the use of combined simulated and truth-grounded data, infrastructure asset and portfolio-level resource allocation will be determined and multi-stakeholder decision making patterns will be evaluated. Boston Logan Airport and Port Authority will be used as a case study to inform and evaluate the toolset and results can be grounded based upon stakeholder buy-in and historical event inter-comparison. Results intend to be informative to a game theoretical framework.

Understanding aviation transportation infrastructure system behavior and coupling with communication networks is essential for securing and restoring functionality against cyber-enabled threats. While significant progress has been made in the past decade on developing infrastructure resilience theories based on network structure and operations, translating and generalizing them to real-world practice has often been challenging due to imperfect data and inapplicability of modeling assumptions. These typically include: 1) stylized network structures without uncertainty, 2) node homogeneity, 3) static criticality measures, and 4) unrealistic cascade models originating from single points of failure. This paper presents the modeling perspectives and approaches that aim to address these theory-to-practice challenges using a well-grounded network-of-networks (NoN) construct. Real-world modeling challenges are identified and a network theory-guided conceptual NoN model is developed that may be operationalized with the U.S. national airspace system airport network and Federal Aviation Administration (FAA) communication network as an application domain.

Spent fuel monitoring and characterization has been central to safeguards and nuclear facility monitoring for many years. The Digital Cerenkov Viewing Device (DCVD) has been used since the 1980s as a method of defect detection in spent fuel. In recent years, the accounting for large quantities of spent fuel before storage has renewed interest in this relatively quick and inexpensive method. This has an impact not only in safeguards, but also for nuclear power facilities, as accounting can be a long, arduous, and costly process. Additionally, the DCVD demonstrates limited accuracy in more complex cases such as substitution of a fuel rod with steel or a partial defect detection. A second method, gamma emission tomography (GET) has been explored as an improved defect detection method, but is much more expensive and invasive than DCVD. The present investigation identifies deficiencies in both methods and proposes a combination of data gathered from each method to address these deficiencies for improved spent fuel characterization. Initial results are promising, showing 97% detection of a single missing fuel rod when the data types are combined, versus approximately 50% and 70%, respectively, for DCVD and GET data on their own. These classification results are obtained with algorithms derived from facial recognition and applied to this problem, yielding unique accuracy in near real time while also maintaining the information barrier between output and measurement desired in safeguards.

Pursuit-evasion is a multi-agent sequential decision problem in which a group of agents coordinate their traversal of a spatial domain to locate an agent trying to evade them. Pursuit-evasion problems arise in a number of important application domains including defense and route planning. Learning to coordinate pursuer behaviors so as to minimize the time to capture of the evader is challenging because of a large action space and sparse noisy state information; consequently, previous approaches have relied primarily on heuristics. We propose a variant of Thompson Sampling for pursuit-evasion that allows for the application of existing model-based planning algorithms. The proposed approach is general that it allows for an arbitrary number of pursuers, a general spatial domain and the integration of side information. We derive posterior convergence rates and regret bounds for the proposed method, which are the first of their kind in this context. Through a suite of simulation experiments, we find that Thompson Sampling for pursuit-evasion significantly reduces time-to-capture and increases capture rate by at least 9% relative to competing algorithms.

This paper describes a tool that will allow public health analysts to estimate infectious disease risk at the country level as a function of different international transportation modes. The prototype focuses on a cholera epidemic originating within Latin America or the Caribbean, but it can be expanded to consider other pathogens as well. This effort leverages previous work in collaboration with the Centers for Disease Control and Prevention to develop the International Travel to Community Impact (IT-CI) model, which analyzes and assesses potential international disease outbreaks then estimates the associated impacts to U.S. communities and the nation as a whole and orient it for use Outside the Continental United States (OCONUS). For brevity, we refer to this refined model as OIT-CI. First, we developed an operationalized meta-population spatial cholera model for Latin America and the Caribbean at the secondary administrative-level boundary. Secondly, we developed a robust function of human airline critical to approximating mixing patterns in the meta-population model. In the prototype version currently presented here, OIT-CI models a cholera epidemic originating in a Latin American or Caribbean country and spreading via airline transportation routes. Disease spread is modeled at the country level using a patch model with a connectivity function based on demographic, geospatial, and human transportation data. We have also identified data to estimate the water and health-related infrastructure capabilities of each country to include this potential impact on disease transmission. OIT-CI utilizes these data and modeling constructs to estimate the cholera risk, as a function of attack rate, for each country consistent [1]. This estimation will be completed by providing an order of magnitude risk estimate (e.g., 1 percent, 10 percent, 50 percent, 100 percent) for a cholera outbreak originating within and spreading to Latin American and Caribbean countries at secondary level boundaries (i.e., states or administrative districts). To create a product that is both useful and desirable, feedback from end users of OIT-CI will be incorporated into the model software and visualization design.

This catalog is intended to be a comprehensive listing of repair parts, components, kits, and consumable items used on the equipment deployed at SLD sites worldwide. The catalog covers detection, CAS, network, ancillary equipment, and tools. The catalog is backed by a Master Parts Database which is used to generate the standard report views of the catalog. The master parts database is a relational database containing a record for every part in the master parts catalog along with supporting tables for normalizing fields in the records. The database also includes supporting queries, database maintenance forms, and reports.

This research follows the Updated Guidelines for Evaluating Public Health Surveillance Systems, Recommendations from the Guidelines Working Group, published by the Centers for Disease Control and Prevention nearly a decade ago. Since then, models have been developed and complex systems have evolved with a breadth of disparate data to detect or forecast chemical, biological, and radiological events that have a significant impact on the One Health landscape. How the attributes identified in 2001 relate to the new range of event-based biosurveillance technologies is unclear. This article frames the continuum of event-based biosurveillance systems (that fuse media reports from the internet), models (ie, computational that forecast disease occurrence), and constructs (ie, descriptive analytical reports) through an operational lens (ie, aspects and attributes associated with operational considerations in the development, testing, and validation of the event-based biosurveillance methods and models and their use in an operational environment). A workshop was held in 2010 to scientifically identify, develop, and vet a set of attributes for event-based biosurveillance. Subject matter experts were invited from 7 federal government agencies and 6 different academic institutions pursuing research in biosurveillance event detection. We describe 8 attribute families for the characterization of event-based biosurveillance: event, readiness, operational aspects, geographic coverage, population coverage, input data, output, and cost. Ultimately, the analyses provide a framework from which the broad scope, complexity, and relevant issues germane to event-based biosurveillance useful in an operational environment can be characterized.

The Office of the Second Line of Defense (SLD) is part of the Department of Energy‘s (DOE) National Nuclear Security Administration (NNSA). The SLD Program accomplishes its critical global security mission by forming cooperative relationships with partner countries to install passive radiation detection systems that augment traditional inspection and law enforcement measures by alerting border officials to the presence of special nuclear or other radiological materials in cross-border traffic. An important tenet of the program is to work collaboratively with these countries to establish the necessary processes, procedures, infrastructure and conditions that will enable them to fully assume the financial and technical responsibilities for operating the equipment. As the number of operational deployments grows, the SLD Program faces an increasingly complex logistics process to promote the timely and efficient supply of spare parts.

The National Strategy for Pandemic Influenza outlines a plan for community response to a potential pandemic. In this outline, state and local communities are charged with enhancing their preparedness. In order to help public health officials better understand these charges, we have developed a visual analytics toolkit (PanViz) for analyzing the effect of decision measures implemented during a simulated pandemic influenza scenario. Spread vectors based on the point of origin and distance traveled over time are calculated and the factors of age distribution and population density are taken into effect. Healthcare officials are able to explore the effects of the pandemic on the population through a geographical spatiotemporal view, moving forward and backward through time and inserting decision points at various days to determine the impact. Linked statistical displays are also shown, providing county level summaries of data in terms of the number of sick, hospitalized and dead as a result of the outbreak. Currently, this tool has been deployed in Indiana State Department of Health planning and preparedness exercises, and as an educational tool for demonstrating the impact of social distancing strategies during the recent H1N1 (swine flu) outbreak.

This report summarizes a study and corresponding model development conducted in support of the United States Pacific Command (USPACOM) as part of the Federal Energy Management Program (FEMP) American Reinvestment and Recovery Act (ARRA). This research was aimed at developing a mathematical programming framework and accompanying optimization methodology in order to simultaneously evaluate energy efficiency (EE) and renewable energy (RE) opportunities. Once developed, this research then demonstrated this methodology at a USPACOM installation - Camp H.M. Smith, Hawaii. We believe this is the first time such an integrated, joint EE and RE optimization methodology has been constructed and demonstrated.

Federal, State, and local decision makers and public health officials must prepare and exercise complex plans to contend with a variety of possible mass casualty events, such as pandemic influenza. Through the provision of quick look tools (QLTs) focused on mass casualty events, such planning can be done with higher accuracy and more realism through the combination of interactive simulation and visualization in these tools. If an event happens, the QLTs can then be employed to rapidly assess and execute alternative mitigation strategies, and thereby minimize casualties. This can be achieved by conducting numerous 'what-if' assessments prior to any event in order to assess potential health impacts (e.g., number of sick individuals), required community resources (e.g., vaccinations and hospital beds), and optimal mitigative decision strategies (e.g., school closures) during the course of a pandemic. In this presentation, we overview and demonstrate a pandemic influenza QLT, discuss some of the modeling methods and construct and visual analytic components and interface, and outline additional development concepts. These include the incorporation of a user selectable infectious disease palette, simultaneous visualization of decision alternatives, additional resource elements associated with emergency response (e.g., first responders and medical professionals), and provisions for other potential disaster events.

There is a need to better understand and describe social intelligence in the realm of dealing with mass casualty events, such as pandemic influenza, earthquakes, or other natural or manmade disasters. This social intelligence is needed to be able to accurately feed and drive models and simulations that attempt to describe and quantify human responses to such mass casualty events and potential mitigation strategies that might be used to minimize their impacts by reducing numbers of deaths, injuries, and other societal (e.g., economic) consequences. We propose to attempt to gain a better understanding of social intelligence and socially driven human responses through the use of games and game like interfaces with an application toward infectious diseases.

We present our methodology and stochastic discrete-event simulation developed to model the screening of passengers for pandemic influenza at the US port-of-entry airports. Our model uniquely combines epidemiology modelling, evolving infected states and conditions of passengers over time, and operational considerations of screening in a single simulation. The simulation begins with international aircraft arrivals to the US. Passengers are then randomly assigned to one of three states – not infected, infected with pandemic influenza and infected with other respiratory illness. Passengers then pass through various screening layers (i.e. pre-departure screening, en route screening, primary screening and secondary screening) and ultimately exit the system. We track the status of each passenger over time, with a special emphasis on false negatives (i.e. passengers infected with pandemic influenza, but are not identified as such) as these passengers pose a significant threat as they could unknowingly spread the pandemic influenza virus throughout our nation.

The methods used to evaluate automation tools are a critical part of the development process. In general, the most meaningful measure of an automation method from an operational standpoint is its effect on productivity. Both timed comparison between manual and automation based-extraction, as well as measures of spatial accuracy are needed. In this paper, we introduce the notion of correspondence to evaluate spatial accuracy of an automated update method. Over time, existing vector data becomes outdated because 1) land cover changes occur, or 2) more accurate overhead images are acquired, and/or vector data resolution requirements by the user may increase. Therefore, an automated vector data updating process has the potential to significantly increase productivity, particularly as existing worldwide vector database holdings increase in size, and become outdated more quickly. In this paper we apply the proposed evaluation methodology specifically to the process of automated updating of existing road centerline vectors. The operational scenario assumes that the accuracy of the existing vector data is in effect outdated with respect to newly acquired imagery. Whether the particular approach used is referred to as 1) vector-to-image registration, or 2) vector data updating-based automated feature extraction (AFE), it is open to interpretation of the application and bias of the developer or user. The objective of this paper is to present a quantitative and meaningful evaluation methodology of spatial accuracy for automated vector data updating methods.

Background: A stochastic discrete event simulation model was developed to assess the effectiveness of passenger screening for Pandemic Influenza (PI) at U.S. airport foreign entry.Methods: International passengers arriving at 18 U.S. airports from Asia, Europe, South America, and Canada were assigned to one of three states: not infected, infected with PI, infected with other respiratory illness. Passengers passed through layered screening then exited the model. 80% screening effectiveness was assumed for symptomatic passengers; 6% asymptomatic passengers.Results: In the first 100 days of a global pandemic, U.S. airport screening would evaluate over 17 M passengers with 800 K secondary screenings. 11,570 PI infected passengers (majority asymptomatic) would enter the U.S. undetected from all 18 airports. Foreign airport departure screening significantly decreased the false negative (infected/undetected) passengers. U.S. attack rates: no screening (26.9%-30.9%); screening (26.4%-30.6%); however airport screening results in 800 K-1.8 M less U.S. PI cases; 16 K-35 K less deaths (2% fatality rate). Antiviral medications for travel contact prophylaxis (10 contacts/PI passenger) were high - 8.8 M. False positives from all 18 airports: 100-200/day.Conclusions: Foreign shore exit screening greatly reduces numbers of PI infected passengers. U.S. airport screening identifies 50% infected individuals; efficacy is limited by the asymptomatic PI infected. Screening will not significantly delay arrival of PI via international air transport, but will reduce the rate of new US cases and subsequent deaths. (C) 2009 Elsevier Ltd. All rights reserved.