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Linkov I.,U.S. Army | Wood M.D.,U.S. Army | Ditmer R.,STRATCON LLC | Cox A.,Cox Associates Consulting | Ross R.,Risk science Branch
Environment Systems and Decisions | Year: 2013

Individuals make decisions every day in group contexts which vary in size, structure, and purpose. The US Department of Defense (DoD) is a large organization composed of many groups, and like many organizations, it has a vested interest in improving the performance of its affiliated groups, especially as it concerns risk-informed decision-making. This article discusses current foibles and considerations for decision-making in DoD groups as identified through a workshop with experts in risk-informed decision-making, cognitive science, and military operations. Experts noted that terms associated with risk-informed decision-making were often misconstrued, that formal decision-making frameworks are underutilized, and that many considerations should be taken into account when attempting to improve decision-making performance. © 2013 Springer Science+Business Media New York (outside the USA). Source

Conn P.B.,Cornell University | Conn P.B.,National Oceanic and Atmospheric Administration | Cooch E.G.,Cornell University | Caley P.,Risk science Branch
Journal of Ornithology | Year: 2012

Force-of-infection (FOI; the instantaneous rate at which susceptible individuals acquire infection) is an important summary parameter in many disease studies. This parameter controls the propensity of diseases and parasites to spread through populations and often depends on the degree of contact between susceptible and infected individuals. Longitudinal studies are perhaps capable of providing the most information about FOI; however, inference can also be drawn from cross-sectional age-prevalence data in certain situations (for instance, when disease is endemic in a population with little temporal variation in vital rates). In this paper, we provide a review of FOI as it relates to the study of marked animals, highlighting difficulties with obtaining parameter estimates with the intended interpretation. We also provide several alternatives for accounting for detection probability when estimating FOI. We primarily concentrate on the analysis of cross-sectional age-prevalence data, where previous approaches have traditionally assumed that the probability of sampling an individual is the same regardless of disease status or age class. Since this assumption is likely to be violated in many wildlife populations, we work to extend existing statistical methodology to account for potential differences in capture probability. Our approach requires that data be gathered such that capture-recapture or removal estimators of abundance may be employed. We use simulation to investigate the importance of accounting for differences in detectability, demonstrating a potential for substantial bias when detectability is ignored. Finally, we illustrate our approach by analyzing age-prevalence data from a removal study of ferrets in New Zealand. Interest in this case focused on quantifying age-specific susceptibility of ferrets to bovine tuberculosis. © 2010 Dt. Ornithologen-Gesellschaft e.V. (outside the USA). Source

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