Browsing by Author "Fricker, Ronald D. Jr."

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    Academic Leadership
    Fricker, Ronald D. Jr. (2016-10)
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    Africa Knowledge, Data Source, and Analytic Effort (KDAE) Exploration
    Deveans, T.; Lechtenberg-Kasten, S.; Buttrey, Samuel E.; Fricker, Ronald D. Jr.; Appleget, J. A.; Kulzy, W. W. (Tradoc Analysis Center, 2012-08)
    The TRADOC Analysis Center (TRAC), Naval Postgraduate School (NPS), and other Department of Defense (DoD) organizations are currently conducting large data capture and analysis efforts on areas all around the world. As efforts in the US Central Command (CENTCOM) Area of Responsibility (AOR), particularly in both Iraq and Afghanistan draw down, many senior decision makers expect that the US African Command (AFRICOM) AOR will be the focus of future efforts in the coming years. This project will first build an assessment framework focused on the AFRICOM AOR identifying what data we would ideally like to gather and measure in a COIN environment, and then by actually gathering the data points from a multitude of sources we can identify gaps in the available data. Concurrently, this effort will develop the necessary software within the DaViTo (Data Visualization Tool), an open source, government owned exploratory data analysis tool, in order to allow the end user to construct an assessment framework utilizing a customized weighting scheme along with the ability to display results. Finally, this project will develop a scenario methodology and a small Proof of Principle use case in Nigeria by conducting factor analysis of survey data and will use Generalized Linear Models (GLMs) in order to predict future issue stance scores and observed attitudes and behaviors of the population that will directly support TRAC’s Irregular Warfare Tactical Wargame (IW TWG).
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    Analysis and Evaluation of Social Network Anomaly Detection
    Zhao, Meng John (Virginia Tech, 2017-10-27)
    As social networks become more prevalent, there is significant interest in studying these network data, the focus often being on detecting anomalous events. This area of research is referred to as social network surveillance or social network change detection. While there are a variety of proposed methods suitable for different monitoring situations, two important issues have yet to be completely addressed in network surveillance literature. First, performance assessments using simulated data to evaluate the statistical performance of a particular method. Second, the study of aggregated data in social network surveillance. The research presented tackle these issues in two parts, evaluation of a popular anomaly detection method and investigation of the effects of different aggregation levels on network anomaly detection.
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    Assessing the Early Aberration Reporting System's Ability to Locally Detect the 2009 Influenza Pandemic
    Hagen, K. S.; Fricker, Ronald D. Jr.; Hanni, K. D.; Barnes, S.; Michie, K. (2011-05)
    The Early Aberration Reporting System (EARS) is used by some local health departments (LHDs) to monitor emergency room and clinic data for disease outbreaks. Using actual chief complaint data from local public health clinics, we evaluate how EARS—both the baseline system distributed by the CDC and two variants implemented by one LHD—perform at locally detecting the 2009 influenza A H1N1 pandemic. We also compare the EARS methods to a CUSUM-based method. We find that the baseline EARS system performed poorly in comparison to one of the LHD variants and the CUSUM-based method. These results suggest that changes in how syndromes are defined can substantially improve EARS performance. The results also show that incorporating algorithms that use more historical data will improve EARS performance for routine surveillance by local health departments.
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    Assessing the Effects of Individual Augmentation (IA) on Active Component Navy Enlisted and Officer Retention
    Fricker, Ronald D. Jr.; Buttrey, Samuel E. (Naval Postgraduate School, 2008-08)
    This report summarizes the results of an analysis of whether individual augmentation (IA) deployment affects retention rates for Navy enlisted personnel and junior officers. The analysis compared retention rates between those personnel who have been deployed via IA to equivalent cohorts of Navy personnel who have not been on an IA deployment. Retention rates were compared in three different ways: aggregate comparisons, comparisons by individual demographic categories, and comparisons based on standard statistical modeling techniques (logistic regression), in order to simultaneously control for all the demographic and other observable characteristics. Overall, the analysis found little evidence that IA deployment is hurting retention rates among those who have experienced one or more IA deployments. In fact, in almost all of the comparisons, the retention rates of those who have had one or more IA deployments were higher than the retention rates of their Navy colleagues who have only been on conventional Navy deployments. The only categories where lower retention rates were definitively identified were for E-3s and E-4s, though the decrease in retention rates was only about one percent.
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    Assessing the Methodology for Testing Body Armor
    Fricker, Ronald D. Jr.; Wilson, Alyson G. (2010-08-01)
    Conference presentation
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    Assessing What Distinguishes Highly Cited from Less-Cited Papers Published in Interfaces
    Fricker, Ronald D. Jr.; Hamrick, TA; Brown, G. G. (2010)
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    Attrition Models of the Ardennes Campaign
    Fricker, Ronald D. Jr. (1998)
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    Biosurveillance: Detecting, Tracking, and Mitigating the Effects of Natural Disease and Bioterrorism
    Fricker, Ronald D. Jr. (Wiley, 2008)
    Biosurveillance is the regular collection, analysis, and interpretation of health and health-related data for indicators of diseases and other outbreaks by public health organizations. Motivated by the threat of bioterrorism, biosurveillance systems are being developed and implemented around the world. The goal of these systems has been expanded to include both early event detection and situational awareness, so that the focus is not simply on detection, but also on response and consequence management. Whether they are useful for detecting bioterrorism or not, there seems to be consensus that these biosurveillance systems are likely to be useful for detecting and responding to natural disease outbreaks such as seasonal and pandemic flu, and thus they have the potential to significantly advance and modernize the practice of public health surveillance.
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    Cognitive Alignment with Performance Targeted Training Intervention Model: CAPTIM
    Kennedy, Q.; Nesbitt, J. K.; Alt, J. K.; Fricker, Ronald D. Jr. (Naval Postgraduate School, 2015-02)
    In this technical report, we propose that the use of two simple behavioral measures, in conjunction with neurophysiological measures, can be used to create a training intervention that has the potential to provide: (1) real-time notification as to when a training intervention is needed, and (2) real-time information as to the type of training intervention that should be employed. The Cognitive Alignment with Performance Targeted Training Intervention Model (CAPTTIM) determines if a trainee's cognitive state is aligned or misaligned with actual performance. When misalignment occurs, it indicates that a training intervention is needed. Neurophysiological markers as captured by eyetracking and electroencephalography (EEG) can assist in determining why misalignment between cognitive state and performance occurred, leading to more effective and targeted training intervention. Because all measures are captured continuously in real time, this model has the potential to increase training efficiency and effectiveness in a variety of training domains. The model is illustrated with two case studies.
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    Comparing syndromic surveillance detection methods: EARS' versus a CUSUM-based methodology
    Fricker, Ronald D. Jr.; Hegler, B. L.; Dunfee, D. A. (2008-07-30)
    This paper compares the performance of three detection methods, entitled C1, C2, and C3, that are implemented in the early aberration reporting system (EARS) and other syndromic surveillance systems versus the CUSUM applied to model-based prediction errors. The cumulative sum (CUSUM) performed significantly better than the EARS' methods across all of the scenarios we evaluated. These scenarios consisted of various combinations of large and small background disease incidence rates, seasonal cycles from large to small (as well as no cycle), daily effects, and various types and levels of random daily variation. This leads us to recommend replacing the C1, C2, and C3 methods in existing syndromic surveillance systems with an appropriately implemented CUSUM method.
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    Data Science vs. Statistics: What's the Difference?
    Fricker, Ronald D. Jr. (2015-08-10)
    2015 Joint Statistical Meetings "Roundtable Discussion" presentation
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    Directionally Sensitive MCUSUM and MEWMA Procedures with Application to Biosurveillance
    Fricker, Ronald D. Jr.; Knitt, MC; Hu, CX (2008)
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    Educating Military Operations Research Practitioners
    Fricker, Ronald D. Jr.; Dell, R. F. (2014-07)
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    Educating Military Operations Research Practitioners
    Fricker, Ronald D. Jr.; Brown, G. G.; DeGrange, W.; Dell, R. F. (2015)