Bayesian modeling of temporal properties of infectious disease in a college student population

A Bayesian statistical model is developed for analysis of the time-evolving properties of infectious disease, with a particular focus on viruses. The model employs a latent semi-Markovian state process, and the state-transition statistics are driven by three terms: (i) a general time-evolving trend of the overall population, (ii) a semi-periodic term that accounts for effects caused by the days of the week, and (iii) a regression term that relates the probability of infection to covariates (here, specifically, to the Google Flu Trends data). Computations are performed using Markov Chain Monte Carlo sampling. Results are presented using a novel data set: daily self-reported symptom scores from hundreds of Duke University undergraduate students, collected over three academic years. The illnesses associated with these students are (imperfectly) labeled using real-time (RT) polymerase chain reaction (PCR) testing for several viruses, and gene-expression data were also analyzed. The statistical analysis is performed on the daily, self-reported symptom scores, and the RT PCR and gene-expression data are employed for analysis and interpretation of the model results.     

Sequential analysis of 3m supersaturated factorial experiments

In this paper, designs that allow for the estimation of all main effects, and the detection and estimation of significant interactions in fewer runs than used in traditional approaches are examined. Specifically, a method for sequentially analyzing data from 3m fractional factorial experiments is presented. The procedure is illustrated through two examples.     

Employee Engagement and Organizational Behavior Management

No abstract available for this article.     

Out of Sight,Out of Mind? Geographic and Social Predictors of Flood Risk Awareness

The persistent gap in flood risk awareness in Canada, and elsewhere in North America, is a continual source of worry for researchers and emergency managers; many people living in at‐risk places are simply unaware of risks and of their proximity to hazards. This study seeks to understand which residents were aware of flood risk, using unique representative survey data of Calgary residents living in the city's flood‐prone neighborhoods collected after the devastating and costly 2013 Southern Alberta Flood. The article uses logistic regression models to analyze which residents were aware of risk to their homes. Findings indicate that, in addition to various demographic predictors, many of the geographic predictors (including the elevation of one's home relative to the river) are significant predictors of awareness. Having a direct sight line to one of Calgary's two rivers is also a significant predictor in some of the models, suggesting that the visibility of hazards matters for flood risk perception, although this effect fades when many of the geographic predictors are added. Finally, the models indicate that several variables related to local, neighborhood‐based social networks are significant as well. These findings reveal that both physical surroundings and social context are important for understanding risk awareness. The article concludes by discussing the relevance for social science research on disasters and hazards, as well as for planners and emergency managers.     

Managing unmanageable physicians: leadership, stewardship and disruptive behavior

Physician executives would undoubtedly prefer to spend the bulk of their time inspiring great employees and building productive teams instead of struggling with disruptive physician behavior. Explore ways to diminish the frequency and intensity of disruptive behavior in your organization and identify your role, rights, responsibilities and accountabilities as a physician leader.     

Trust, Confidence, and the 2008 Global Financial Crisis

The 2008 global financial crisis has been compared to a "once-in-a-century credit tsunami," a disaster in which the loss of trust and confidence played key precipitating roles and the recovery from which will require the restoration of these crucial factors. Drawing on the analogy between the financial crisis and environmental and technological hazards, recent research on the role of trust and confidence in the latter is used to provide a perspective on the former. Whereas "trust" and "confidence" are used interchangeably and without explicit definition in most discussions of the financial crisis, this perspective uses the TCC model of cooperation to clearly distinguish between the two and to demonstrate how this distinction can lead to an improved understanding of the crisis. The roles of trust and confidence—both in precipitation and in possible recovery—are discussed for each of the three major sets of actors in the crisis, the regulators, the banks, and the public. The roles of trust and confidence in the larger context of risk management are also examined; trust being associated with political approaches, confidence with technical. Finally, the various stances that government can take with regard to trust—such as supportive or skeptical—are considered. Overall, it is argued that a clear understanding of trust and confidence and a close examination of the specific, concrete circumstances of a crisis—revealing when either trust or confidence is appropriate—can lead to useful insights for both recovery and prevention of future occurrences.