Publications

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Infectious diseases can spread rapidly through healthcare facilities, resulting in widespread illness among vulnerable patients. Computational models of disease spread are useful for evaluating mitigation strategies under different scenarios. This report describes two infectious disease models built for the US Department of Veteran Affairs (VA) motivated by a Varicella outbreak in a VA facility. The first model simulates disease spread within a notional contact network representing staff and patients. Several interventions, along with initial infection counts and intervention delay, were evaluated for effectiveness at preventing disease spread. The second model adds staff categories, location, scheduling, and variable contact rates to improve resolution. This model achieved more accurate infection counts and enabled a more rigorous evaluation of comparative effectiveness of interventions.

As with other large healthcare organizations, medical adverse events at the Department of Veterans Affairs (VA) facilities can expose patients to unforeseen negative risks. VHA leadership recognizes that properly handled disclosure of adverse events can minimize potential harm to patients and negative consequences for the effective functioning of the organization. The work documented here seeks to help improve the disclosure process by situating it within the broader theoretical framework of issues management, and to identify opportunities for process improvement through modeling disclosure and reactions to disclosure. The computational model will allow a variety of disclosure actions to be tested across a range of incident scenarios. Our conceptual model will be refined in collaboration with domain experts, especially by continuing to draw on insights from VA Study of the Communication of Adverse Large-Scale Events (SCALE) project researchers.

Chronic infection with Hepatitis C virus (HCV) results in cirrhosis, liver cancer and death. As the nations largest provider of care for HCV, US Veterans Health Administration (VHA) invests extensive resources in the diagnosis and treatment of the disease. This report documents modeling and analysis of HCV treatment dynamics performed for the VHA aimed at improving service delivery efficiency. System dynamics modeling of disease treatment demonstrated the benefits of early detection and the role of comorbidities in disease progress and patient mortality. Preliminary modeling showed that adherence to rigorous treatment protocols is a primary determinant of treatment success. In depth meta-analysis revealed correlations of adherence and various psycho-social factors. This initial meta-analysis indicates areas where substantial improvement in patient outcomes can potentially result from VA programs which incorporate these factors into their design.

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International forces in Afghanistan have experienced difficulties in developing constructive engagements with the Afghan population, an experience familiar to a wide range of international agencies working in underdeveloped and developing nations around the world. Recently, forces have begun deploying Female Engagement Teams, female military units who engage directly with women in occupied communities, resulting inmore positive relationships with those communities as a whole. In this paper, we explore the hypothesis that the structure of community-based social networks strongly contributes to the effectiveness of the Female Engagement Team strategy, specifically considering gender-based differences in network community structure. We find that the ability to address both female and male network components provides a superior ability to affect opinions in the network, and can provide an effective means of counteracting influences from opposition forces. © 2012 Springer-Verlag.

Complex Adaptive Systems of Systems, or CASoS, are vastly complex physical-socio-technical systems which we must understand to design a secure future for the nation. The Phoenix initiative implements CASoS Engineering principles combining the bottom up Complex Systems and Complex Adaptive Systems view with the top down Systems Engineering and System-of-Systems view. CASoS Engineering theory and practice must be conducted together to develop a discipline that is grounded in reality, extends our understanding of how CASoS behave and allows us to better control the outcomes. The pull of applications (real world problems) is critical to this effort, as is the articulation of a CASoS Engineering Framework that grounds an engineering approach in the theory of complex adaptive systems of systems. Successful application of the CASoS Engineering Framework requires modeling, simulation and analysis (MS and A) capabilities and the cultivation of a CASoS Engineering Community of Practice through knowledge sharing and facilitation. The CASoS Engineering Environment, itself a complex adaptive system of systems, constitutes the two platforms that provide these capabilities.

Complex Adaptive Systems of Systems, or CASoS, are vastly complex ecological, sociological, economic and/or technical systems which must be recognized and reckoned with to design a secure future for the nation and the world. Design within CASoS requires the fostering of a new discipline, CASoS Engineering, and the building of capability to support it. Towards this primary objective, we created the Phoenix Pilot as a crucible from which systemization of the new discipline could emerge. Using a wide range of applications, Phoenix has begun building both theoretical foundations and capability for: the integration of Applications to continuously build common understanding and capability; a Framework for defining problems, designing and testing solutions, and actualizing these solutions within the CASoS of interest; and an engineering Environment required for 'the doing' of CASoS Engineering. In a secondary objective, we applied CASoS Engineering principles to begin to build a foundation for design in context of Global CASoS

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Infrastructures are networks of dynamically interacting systems designed for the flow of information, energy, and materials. Under certain circumstances, disturbances from a targeted attack or natural disasters can cause cascading failures within and between infrastructures that result in significant service losses and long recovery times. Reliable interdependency models that can capture such multi-network cascading do not exist. The research reported here has extended Sandia's infrastructure modeling capabilities by: (1) addressing interdependencies among networks, (2) incorporating adaptive behavioral models into the network models, and (3) providing mechanisms for evaluating vulnerability to targeted attack and unforeseen disruptions. We have applied these capabilities to evaluate the robustness of various systems, and to identify factors that control the scale and duration of disruption. This capability lays the foundation for developing advanced system security solutions that encompass both external shocks and internal dynamics.

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