Publications

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The objective here is to obtain feedback and seek future directions for an ISDS initiative to establish and update research questions in Informatics, Analytics,Communications, and Systems Research with the greatest perceived impact for improving surveillance practice.Introduction Over the past fifteen years, syndromic surveillance (SyS) has evolved from a set of ad hoc methods used mostly in post-disaster settings, then expanded with broad support and development because of bioterrorism concerns, and subsequently evolved to a mature technology that runs continuously to detect and monitor a wide range of health issues. Continued enhancements needed to meet the challenges of novel health threats with increasingly complex information sources will require technical advances focused on day-to-day public health needs.Since its formation in 2005, the International Society for Disease Surveillance (ISDS) has sought to clarify and coordinate global priorities in surveillance research. As part of a practitioner-driven initiative to identify current research priorities in SyS, ISDS polled its members about capabilities needed by SyS practitioners that could be improved as a result of research efforts. A taskforce of the ISDS Research Committee, consisting of national and global subject matter experts (SMEs) in SyS and ISDS professional staff, carried out the project. This panel will discuss the results and the preferred means to determine and communicate priorities in the future.

Improved validation for models of complex systems has been a primary focus over the past year for the Resilience in Complex Systems Research Challenge. This document describes a set of research directions that are the result of distilling those ideas into three categories of research -- epistemic uncertainty, strong tests, and value of information. The content of this document can be used to transmit valuable information to future research activities, update the Resilience in Complex Systems Research Challenge's roadmap, inform the upcoming FY18 Laboratory Directed Research and Development (LDRD) call and research proposals, and facilitate collaborations between Sandia and external organizations. The recommended research directions can provide topics for collaborative research, development of proposals, workshops, and other opportunities.

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We applied modeling and simulation to examine the real-world tradeoffs between developingcountry public-health improvement and the need to improve the identification, tracking, and security of agents with bio-weapons potential. Traditionally, the international community has applied facility-focused strategies for improving biosecurity and biosafety. This work examines how system-level assessments and improvements can foster biosecurity and biosafety. We modeled medical laboratory resources and capabilities to identify scenarios where biosurveillance goals are transparently aligned with public health needs, and resource are distributed in a way that maximizes their ability to serve patients while minimizing security a nd safety risks. Our modeling platform simulates key processes involved in healthcare system operation, such as sample collection, transport, and analysis at medical laboratories. The research reported here extends the prior art by provided two key compone nts for comparative performance assessment: a model of patient interaction dynamics, and the capability to perform uncertainty quantification. In addition, we have outlined a process for incorporating quantitative biosecurity and biosafety risk measures. Two test problems were used to exercise these research products examine (a) Systemic effects of technological innovation and (b) Right -sizing of laboratory networks.

Open-source indicators have been proposed as a way of tracking and forecasting disease outbreaks. Some, such are meteorological data, are readily available as reanalysis products. Others, such as those derived from our online behavior (web searches, media article etc.) are gathered easily and are more timely than public health reporting. In this study we investigate how these datastreams may be combined to provide useful epidemiological information. The investigation is performed by building data assimilation systems to track influenza in California and dengue in India. The first does not suffer from incomplete data and was chosen to explore disease modeling needs. The second explores the case when observational data is sparse and disease modeling complexities are beside the point. The two test cases are for opposite ends of the disease tracking spectrum. We find that data assimilation systems that produce disease activity maps can be constructed. Further, being able to combine multiple open-source datastreams is a necessity as any one individually is not very informative. The data assimilation systems have very little in common except that they contain disease models, calibration algorithms and some ability to impute missing data. Thus while the data assimilation systems share the goal for accurate forecasting, they are practically designed to compensate for the shortcomings of the datastreams. Thus we expect them to be disease and location-specific.

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There is mounting evidence that alcohol use is significantly linked to lower HCV treatment response rates in interferon-based therapies, though some of the evidence is conflicting. Furthermore, although health care providers recommend reducing or abstaining from alcohol use prior to treatment, many patients do not succeed in doing so. The goal of this meta-analysis was to systematically review and summarize the Englishlanguage literature up through January 30, 2015 regarding the relationship between alcohol use and HCV treatment outcomes, among patients who were not required to abstain from alcohol use in order to receive treatment. Seven pertinent articles studying 1,751 HCV-infected patients were identified. Log-ORs of HCV treatment response for heavy alcohol use and light alcohol use were calculated and compared. We employed a hierarchical Bayesian meta-analytic model to accommodate the small sample size. The summary estimate for the log-OR of HCV treatment response was -0.775 with a 95% credible interval of (-1.397, -0.236). The results of the Bayesian meta-analysis are slightly more conservative compared to those obtained from a boot-strapped, random effects model. We found evidence of heterogeneity (Q = 14.489, p = 0.025), accounting for 60.28% of the variation among log-ORs. Meta-regression to capture the sources of this heterogeneity did not identify any of the covariates investigated as significant. This meta-analysis confirms that heavy alcohol use is associated with decreased HCV treatment response compared to lighter levels of alcohol use. Further research is required to characterize the mechanism by which alcohol use affects HCV treatment response.

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We present a model of behavioral dynamics that combines a social network-based opinion dynamics model with behavioral mapping. The behavioral component is discrete and history-dependent to represent situations in which an individual’s behavior is initially driven by opinion and later constrained by physiological or psychological conditions that serve to maintain the behavior. Individuals are modeled as nodes in a social network connected by directed edges. Parameter sweeps illustrate model behavior and the effects of individual parameters and parameter interactions on model results. Mapping a continuous opinion variable into a discrete behavioral space induces clustering on directed networks. Clusters provide targets of opportunity for influencing the network state; however, the smaller the network the greater the stochasticity and potential variability in outcomes. This has implications both for behaviors that are influenced by close relationships verses those influenced by societal norms and for the effectiveness of strategies for influencing those behaviors.

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Hospital evacuations that occur during, or as a result of, infrastructure outages are complicated and demanding. Loss of infrastructure services can initiate a chain of events with corresponding management challenges. This report describes a modeling case study of the 2001 evacuation of the Memorial Hermann Hospital in Houston, Texas (USA). The study uses a model designed to track such cascading events following loss of infrastructure services and to identify the staff, resources, and operational adaptations required to sustain patient care and/or conduct an evacuation. The model is based on the assumption that a hospital's primary mission is to provide necessary medical care to all of its patients, even when critical infrastructure services to the hospital and surrounding areas are disrupted. Model logic evaluates the hospital's ability to provide an adequate level of care for all of its patients throughout a period of disruption. If hospital resources are insufficient to provide such care, the model recommends an evacuation. Model features also provide information to support evacuation and resource allocation decisions for optimizing care over the entire population of patients. This report documents the application of the model to a scenario designed to resemble the 2001 evacuation of the Memorial Hermann Hospital, demonstrating the model's ability to recreate the timeline of an actual evacuation. The model is also applied to scenarios demonstrating how its output can inform evacuation planning activities and timing.

This report presents a mathematical model of the way in which a hospital uses a variety of resources, utilities and consumables to provide care to a set of in-patients, and how that hospital might adapt to provide treatment to a few patients with a serious infectious disease, like the Ebola virus. The intended purpose of the model is to support requirements planning studies, so that hospitals may be better prepared for situations that are likely to strain their available resources. The current model is a prototype designed to present the basic structural elements of a requirements planning analysis. Some simple illustrati ve experiments establish the mo del's general capabilities. With additional inve stment in model enhancement a nd calibration, this prototype could be developed into a useful planning tool for ho spital administrators and health care policy makers.

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