Retrospective on U.S. Health Risk Assessment: How Others Can Benefit

Dr. Paustenbach reviews the scientific underpinnings of about twenty years of health risk assessment practice and their implications for environmental policy. He observes that more than 600 peer-reviewed papers provide a wealth of information that can save other countries billions of dollars. He also briefly reviews risk-assessment practices outside the U.S

Quantifying Uncertainty for Early Life Cycle Cost Estimates

Disclaimer: The views represented in this report are those of the authors and do not reflect the official policy position of the Navy, the Department of Defense, or the federal government.Excerpt from the Proceedings of the Tenth Annual Acquisition Research Symposium Cost EstimatingNaval Postgraduate School Acquisition Research ProgramPrepared for the Naval Postgraduate School, Monterey, CANaval Postgraduate School Acquisition Research ProgramApproved for public release; distribution is unlimited

Impute the Missing Data through Fuzzy Expert System for the Medical Data Diagnosis

Data Processing with missing attribute values based on fuzzy sets theory. By matching attribute-value pairs among the same core or reduce of the original data set, the assigned value preserves the characteristics of the original data set. Malaria represents major public health problems in the tropics. The harmful effects of malaria parasites to the human body cannot be underestimated. In this paper, a fuzzy expert system for the management of malaria (FESMM) was presented for providing decision support platform to malaria researchers, The fuzzy expert system was designed based on clinical observations, medical diagnosis and the expert�s knowledge. We selected 15 cases with Malaria and computed the missing results that were in the range of common attribute element by the domain experts

Bayesian Multi-Model Frameworks - Properly Addressing Conceptual Uncertainty in Applied Modelling

We use models to understand or predict a system. Often, there are multiple plausible but competing model concepts. Hence, modelling is associated with conceptual uncertainty, i.e., the question about proper handling of such model alternatives. For mathematical models, it is possible to quantify their plausibility based on data and rate them accordingly. Bayesian probability calculus offers several formal multi-model frameworks to rate models in a finite set and to quantify their conceptual uncertainty as model weights. These frameworks are Bayesian model selection and averaging (BMS/BMA), Pseudo-BMS/BMA and Bayesian Stacking. The goal of this dissertation is to facilitate proper utilization of these Bayesian multi-model frameworks. They follow different principles in model rating, which is why derived model weights have to be interpreted differently, too. These principles always concern the model setting, i.e., how the models in the set relate to one another and the true model of the system that generated observed data. This relation is formalized in model scores that are used for model weighting within each framework. The scores resemble framework-specific compromises between the ability of a model to fit the data and the therefore required model complexity. Hence, first, the scores are investigated systematically regarding their respective take on model complexity and are allocated in a developed classification scheme. This shows that BMS/BMA always pursues to identify the true model in the set, that Pseudo-BMS/BMA searches the model with largest predictive power despite none of the models being the true one, and that, on that condition, Bayesian Stacking seeks reliability in prediction by combining predictive distributions of multiple models. An application example with numerical models illustrates these behaviours and demonstrates which misinterpretations of model weights impend, if a certain framework is applied despite being unsuitable for the underlying model setting. Regarding applied modelling, first, a new setting is proposed that allows to identify a ``quasi-true'' model in a set. Second, Bayesian Bootstrapping is employed to take into account that rating of predictive capability is based on only limited data. To ensure that the Bayesian multi-model frameworks are employed properly and goal-oriented, a guideline is set up. With respect to a clearly defined modelling goal and the allocation of available models to the respective setting, it leads to the suitable multi-model framework. Aside of the three investigated frameworks, this guideline further contains an additional one that allows to identify a (quasi-)true model if it is composed of a linear combination of the model alternatives in the set. The gained insights enable a broad range of users in science practice to properly employ Bayesian multi-model frameworks in order to quantify and handle conceptual uncertainty. Thus, maximum reliability in system understanding and prediction with multiple models can be achieved. Further, the insights pave the way for systematic model development and improvement.Wir benutzen Modelle, um ein System zu verstehen oder vorherzusagen. Oft gibt es dabei mehrere plausible aber konkurrierende Modellkonzepte. Daher geht Modellierung einher mit konzeptioneller Unsicherheit, also der Frage nach dem angemessenen Umgang mit solchen Modellalternativen. Bei mathematischen Modellen ist es möglich, die Plausibilität jedes Modells anhand von Daten des Systems zu quantifizieren und Modelle entsprechend zu bewerten. Bayes'sche Wahrscheinlichkeitsrechnung bietet dazu verschiedene formale Multi-Modellrahmen, um Modellalternativen in einem endlichen Set zu bewerten und ihre konzeptionelle Unsicherheit als Modellgewichte zu beziffern. Diese Rahmen sind Bayes'sche Modellwahl und -mittelung (BMS/BMA), Pseudo-BMS/BMA und Bayes'sche Modellstapelung. Das Ziel dieser Dissertation ist es, den adäquaten Umgang mit diesen Bayes'schen Multi-Modellrahmen zu ermöglichen. Sie folgen unterschiedlichen Prinzipien in der Modellbewertung weshalb die abgeleiteten Modellgewichte auch unterschiedlich zu interpretieren sind. Diese Prinzipien beziehen sich immer auf das Modellsetting, also darauf, wie sich die Modelle im Set zueinander und auf das wahre Modell des Systems beziehen, welches bereits gemessene Daten erzeugt hat. Dieser Bezug ist in Kenngrößen formalisiert, die innerhalb jedes Rahmens der Modellgewichtung dienen. Die Kenngrößen stellen rahmenspezifische Kompromisse dar, zwischen der Fähigkeit eines Modells die Daten zu treffen und der dazu benötigten Modellkomplexität. Daher werden die Kenngrößen zunächst systematisch auf ihre jeweilige Bewertung von Modellkomplexität untersucht und in einem entsprechend entwickelten Klassifikationschema zugeordnet. Dabei zeigt sich, dass BMS/BMA stets verfolgt das wahre Modell im Set zu identifizieren, dass Pseudo-BMS/BMA das Modell mit der höchsten Vorsagekraft sucht, obwohl kein wahres Modell verfügbar ist, und dass Bayes'sche Modellstapelung unter dieser Bedingung Verlässlichkeit von Vorhersagen anstrebt, indem die Vorhersageverteilungen mehrerer Modelle kombiniert werden. Ein Anwendungsbeispiel mit numerischen Modellen verdeutlicht diese Verhaltenweisen und zeigt auf, welche Fehlinterpretationen der Modellgewichte drohen, wenn ein bestimmter Rahmen angewandt wird, obwohl er nicht zum zugrundeliegenden Modellsetting passt. Mit Bezug auf anwendungsorientierte Modellierung wird dabei erstens ein neues Setting vorgestellt, das es ermöglicht, ein ``quasi-wahres'' Modell in einem Set zu identifizieren. Zweitens wird Bayes'sches Bootstrapping eingesetzt um bei der Bewertung der Vorhersagegüte zu berücksichtigen, dass diese auf Basis weniger Daten erfolgt. Um zu gewährleisten, dass die Bayes'schen Multi-Modellrahmen angemessen und zielführend eingesetzt werden, wird schließlich ein Leitfaden erstellt. Anhand eines klar definierten Modellierungszieles und der Einordnung der gegebenen Modelle in das entspechende Setting leitet dieser zum geeigneten Multi-Modellrahmen. Neben den drei untersuchten Rahmen enthält dieser Leitfaden zudem einen weiteren, der es ermöglicht ein (quasi-)wahres Modell zu identifizieren, wenn dieses aus einer Linearkombination der Modellalternativen im Set besteht. Die gewonnenen Erkenntnisse ermöglichen es einer breiten Anwenderschaft in Wissenschaft und Praxis, Bayes'sche Multi-Modellrahmen zur Quantifizierung und Handhabung konzeptioneller Unsicherheit adäquat einzusetzen. Dadurch lässt sich maximale Verlässlichkeit in Systemverständis und -vorhersage durch mehrere Modelle erreichen. Die Erkenntnisse ebnen darüber hinaus den Weg für systematische Modellentwicklung und -verbesserung

Towards Managing and Understanding the Risk of Underwater Terrorism

This dissertation proposes a methodology to manage and understand the risk of underwater terrorism to critical infrastructures utilizing the parameters of the risk equation. Current methods frequently rely on statistical methods, which suffer from a lack of appropriate historical data to produce distributions and do not integrate epistemic uncertainty. Other methods rely on locating subject matter experts who can provide judgment and then undertaking an associated validation of these judgments. Using experimentation, data from unclassified successful, or near successful, underwater attacks are analyzed and instantiated as a network graph with the key characteristics of the risk of terrorism represented as nodes and the relationship between the key characteristics forming the edges. The values of the key characteristics, instantiated as the length of the edges, are defaulted to absolute uncertainty, the state where there is no information for, or against, a particular causal factor. To facilitate obtaining the value of the nodes, the Malice spectrum is formally defined which provides a dimensionless, methodology independent model to determine the value of any given parameter. The methodology produces a meta-model constructed from the relationships between the parameters of the risk equation, which determines a relative risk value

Global burden of melioidosis in 2015: a systematic review and data synthesis.

BACKGROUND: Melioidosis is an infectious disease caused by the environmental bacterium Burkholderia pseudomallei. It is often fatal, with a high prevalence in tropical areas. Clinical presentation can vary from abscess formation to pneumonia and sepsis. We assessed the global burden of melioidosis, expressed in disability-adjusted life-years (DALYs), for 2015. METHODS: We did a systematic review of the peer-reviewed literature for human melioidosis cases between Jan 1, 1990, and Dec 31, 2015. Quantitative data for cases of melioidosis were extracted, including mortality, age, sex, infectious and post-infectious sequelae, antibiotic treatment, and symptom duration. These data were combined with established disability weights and expert panel discussions to construct an incidence-based disease model. The disease model was integrated with established global incidence and mortality estimates to calculate global melioidosis DALYs. The study is registered with PROSPERO, number CRD42018106372. FINDINGS: 2888 articles were screened, of which 475 eligible studies containing quantitative data were retained. Pneumonia, intra-abdominal abscess, and sepsis were the most common outcomes, with pneumonia occurring in 3633 (35·7%, 95% uncertainty interval [UI] 34·8-36·6) of 10 175 patients, intra-abdominal abscess in 1619 (18·3%, 17·5-19·1) of 8830 patients, and sepsis in 1526 (18·0%, 17·2-18·8) of 8469 patients. We estimate that in 2015, the global burden of melioidosis was 4·6 million DALYs (UI 3·2-6·6) or 84·3 per 100 000 people (57·5-120·0). Years of life lost accounted for 98·9% (UI 97·7-99·5) of the total DALYs, and years lived with disability accounted for 1·1% (0·5-2·3). INTERPRETATION: Melioidosis causes a larger disease burden than many other tropical diseases that are recognised as neglected, and so it should be reconsidered as a major neglected tropical disease. FUNDING: European Society of Clinical Microbiology and Infectious Diseases (ESCMID) Research Grant 2018, AMC PhD Scholarship, The Netherlands Organisation for Scientific Research (NWO), H2020 Marie Skłodowska-Curie Innovative Training Network European Sepsis Academy

J Expo Sci Environ Epidemiol

IntroductionTo estimate occupational exposures to electromagnetic fields (EMF) for the INTEROCC study, a database of source-based measurements extracted from published and unpublished literature resources had been previously constructed. The aim of the current work was to summarize these measurements into a source-exposure matrix (SEM), accounting for their quality and relevance.MethodsA novel methodology for combining available measurements was developed, based on order statistics and log-normal distribution characteristics. Arithmetic and geometric means, and estimates of variability and maximum exposure were calculated by EMF source, frequency band and dosimetry type. Mean estimates were weighted by our confidence on the pooled measurements.ResultsThe SEM contains confidence-weighted mean and maximum estimates for 312 EMF exposure sources (from 0 Hz to 300 GHz). Operator position geometric mean electric field levels for RF sources ranged between 0.8 V/m (plasma etcher) and 320 V/m (RF sealer), while magnetic fields ranged from 0.02 A/m (speed radar) to 0.6 A/m (microwave heating). For ELF sources, electric fields ranged between 0.2 V/m (electric forklift) and 11,700 V/m (HVTL-hotsticks), while magnetic fields ranged between 0.14 \u3bcT (visual display terminals) and 17 \u3bcT (TIG welding).ConclusionThe methodology developed allowed the construction of the first EMF-SEM and may be used to summarize similar exposure data for other physical or chemical agents.CC999999/ImCDC/Intramural CDC HHS/United StatesR01 CA124759/CA/NCI NIH HHS/United StatesMOP-42525/CIHR/CanadaDepartment of Health/United Kingdom2017-08-28T00:00:00Z27827378PMC55732066062vault:2445