Robert T. Brigantic

A pandemic influenza modeling and visualization tool

Abstract 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.

U.S. airport entry screening in response to pandemic influenza: modeling and analysis.

Summary 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 17M passengers with 800K 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 800K–1.8M less U.S. PI cases; 16K–35K less deaths (2% fatality rate). Antiviral medications for travel contact prophylaxis (10 contacts/PI passenger) were high – 8.8M. 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.

A simulation approach to estimating aircraft mission capable rates for the United States Air Force

This paper presents the results of a simulation model designed to estimate aircraft mission capable rates (MCR) for the United States Air Force. This simulation model originated out of the need to estimate the MCR for different modernization schemes to be implemented on the Air Force C-5 Galaxy aircraft. Assigned to the Air Mobility Command (AMC), the C-5 is one of our nations only two strategic airlift aircraft that can carry large outsize cargo (e.g., helicopters and tanks). At the same time, the C-5 is one of the Air Forces least reliable aircraft. This means that AMC has a deficiency in meeting all of its wartime cargo airlift missions. To address this problem, AMC embarked on a year-long analysis of alternatives (AoA) study in 1999 to determine the best value solution for the Air Force to meet its cargo airlift requirements. Integral to this analysis is the aforementioned simulation model used to estimate C-5 MCR. This paper reviews the different alternatives examined in the AoA and presents the details of the simulation effort to estimate the MCR for these different options.

Assessing the continuum of event-based biosurveillance through an operational lens.

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.

An evaluation methodology for vector data updating

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.

A Methodology for Automated Vector-to-Image Registration

Registration and alignment of feature (e.g., vector) and raster geospatial data is a difficult and time-consuming process when performed manually. This paper presents an approach for vector-to-raster registration. Candidate features are auto-extracted and vectorized from imagery, which are the basis to compare against existing vector layer(s) to be registered. Given that automated feature extraction (AFE) methods are imperfect, the objective is to determine and gather a sufficient signal-to-noise ratio from AFE upon which to base a registration process between vector data sets. Two vector registration methods were investigated. The first is based on an algebraic structural algorithm (ASA) in which structural components (e.g., angles, lengths and areas) are used as similarity metrics. The second is based on a similarity transformation of local features (STLF) in which a 4-parameter transformation is used to align features on a local basis. Experiments were performed to register road vector data to commercial panchromatic and multispectral QuickBird imagery.

Automated Defect Detection in Spent Nuclear Fuel Using Combined Cerenkov Radiation and Gamma Emission Tomography Data

Abstract 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.

Outside the continental United States international travel and contagion impact quick look tool

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.

Second Line of Defense Master Spares Catalog

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.

Second Line of Defense Spares Program Assessment

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.