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Results for “Uncertainty modelling”

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At least 415 records · Page 23Linked to original sources

PubMed2003

Seabed reflection measurement uncertainty.

The seabed reflection coefficient is a fundamental property of the ocean waveguide. Measurements of the frequency and angular dependence of the reflection coefficient can provide information about the geoacoustic properties of the seabed or can be used as an input to propagation models. The uncertainty of the measurements must be known in order to determine prediction uncertainties for the acoustic field and/or the geoacoustic properties. Analysis indicates that the reflection measurements have a standard deviation from +/- 0.5-1 dB at full angular resolution depending on frequency and experiment geometry. The dominant contribution to the error is source amplitude variability, and a new processing approach was developed that reduces the error for frequencies above a few hundred Hz. Further reduction in the uncertainty can be obtained by averaging in angle, for example, a +/- 1 degrees angle averaging leads to a standard deviation of less than +/- 0.5 dB. Errors in the angle estimate are a few tenths of a degree from 0-34 degrees grazing angle: the crucial angular range for predicting long-range propagation or for geoacoustic property inversion.

Journal Article↗

PubMed2005

Review and assessment of models for predicting the migration of radionuclides through rivers.

The present paper summarises the results of the review and assessment of state-of-the-art models developed for predicting the migration of radionuclides through rivers. The different approaches of the models to predict the behaviour of radionuclides in lotic ecosystems are presented and compared. The models were classified and evaluated according to their main methodological approaches. The results of an exercise of model application to specific contamination scenarios aimed at assessing and comparing the model performances were described. A critical evaluation and analysis of the uncertainty of the models was carried out. The main factors influencing the inherent uncertainty of the models, such as the incompleteness of the actual knowledge and the intrinsic environmental and biological variability of the processes controlling the behaviour of radionuclides in rivers, are analysed.

Decision Making↗

PubMedDate not supplied

Statistical distributions of uncertainty and variability in activated sludge model parameters.

All models used in activated sludge design and analysis use parameters to characterize process performance. The values of these parameters are often assumed based on default values recommended in the literature, but to date, no quantitative estimates of the parameter uncertainties have been published. Similarly, little attention has been given to quantifying site-specific parameter variability, even though its occurrence has been observed several times in the literature. In this paper, universal uncertainty distributions of the model parameters from Activated Sludge Model No. 1 are developed from a database of parameter values reported in the literature using Bayesian statistics. Site-specific distributions of parameter variability were developed using the same techniques. All parameter distributions developed demonstrated that significant uncertainty and variability exist, which could lead to overdesign or plant failure if not considered during the design process.

Bayes Theorem↗

PubMedDate not supplied

Ambulance deployment with the hypercube queuing model.

A computer-implemented mathematical model has been developed to assist planners in the spatial deployment and dispatching of ambulances. The model incorporates uncertainties in the arrival times, locations, and service requirements of patients, building on the branch of operations research known as queuing theory. Several system-performance measures are generated by the model, including mean neighborhood-specific response times, mean utilization of each ambulance, and statistical profiles of ambulance response patterns. This model has been implemented by the Department of Health and Hospitals of the City of Boston.

Ambulances↗

PubMed2005

Cost-effectiveness of cognitive-behavioural therapy and drug interventions for major depression.

OBJECTIVE: Antidepressant drugs and cognitive-behavioural therapy (CBT) are effective treatment options for depression and are recommended by clinical practice guidelines. As part of the Assessing Cost-effectiveness -- Mental Health project we evaluate the available evidence on costs and benefits of CBT and drugs in the episodic and maintenance treatment of major depression. METHOD: The cost-effectiveness is modelled from a health-care perspective as the cost per disability-adjusted life year. Interventions are targeted at people with major depression who currently seek care but receive non-evidence based treatment. Uncertainty in model inputs is tested using Monte Carlo simulation methods. RESULTS: All interventions for major depression examined have a favourable incremental cost-effectiveness ratio under Australian health service conditions. Bibliotherapy, group CBT, individual CBT by a psychologist on a public salary and tricyclic antidepressants (TCAs) are very cost-effective treatment options falling below 10,000 Australian dollars per disability-adjusted life year (DALY) even when taking the upper limit of the uncertainty interval into account. Maintenance treatment with selective serotonin re-uptake inhibitors (SSRIs) is the most expensive option (ranging from 17,000 Australian dollars to 20,000 Australian dollars per DALY) but still well below 50,000 Australian dollars, which is considered the affordable threshold. CONCLUSIONS: A range of cost-effective interventions for episodes of major depression exists and is currently underutilized. Maintenance treatment strategies are required to significantly reduce the burden of depression, but the cost of long-term drug treatment for the large number of depressed people is high if SSRIs are the drug of choice. Key policy issues with regard to expanded provision of CBT concern the availability of suitably trained providers and the funding mechanisms for therapy in primary care.

Antidepressive Agents↗

PubMed1990

The concept of probability in safety assessments of technological systems.

Safety assessments of technological systems, such as nuclear power plants, chemical process facilities, and hazardous waste repositories, require the investigation of the occurrence and consequences of rare events. The subjectivistic (Bayesian) theory of probability is the appropriate framework within which expert opinions, which are essential to the quantification process, can be combined with experimental results and statistical observations to produce quantitative measures of the risks from these systems. A distinction is made between uncertainties in physical models and state-of-knowledge uncertainties about the parameters and assumptions of these models. The proper role of past and future relative frequencies and several issues associated with elicitation and use of expert opinions are discussed.

Equipment Design↗

PubMedDate not supplied

Assessing uncertainty in cost-effectiveness analyses: application to a complex decision model.

A framework for quantifying uncertainty about costs, effectiveness measures, and marginal cost-effectiveness ratios in complex decision models is presented. This type of application requires special techniques because of the multiple sources of information and the model-based combination of data. The authors discuss two alternative approaches, one based on Bayesian inference and the other on resampling. While computationally intensive, these are flexible in handling complex distributional assumptions and a variety of outcome measures of interest. These concepts are illustrated using a simplified model. Then the extension to a complex decision model using the stroke-prevention policy model is described.

Bayes Theorem↗

PubMed2002

Prediction of AVM obliteration after stereotactic radiotherapy using radiobiological modelling.

This study was carried out in order to derive the radiobiological parameters of the dose-response relation for the obliteration of arteriovenous malformation (AVM) following single fraction stereotactic radiotherapy. Furthermore, the accuracy by which the linear Poisson model predicts the probability of obliteration and how the haemorrhage history, location and volume of the AVM influence its radiosensitivity are investigated. The study patient material consists of 85 patients who received radiation for AVM therapy. Radiation-induced AVM obliterations were assessed on the basis of post-irradiation angiographies and other radiological findings. For each patient the dose delivered to the clinical target volume and the clinical treatment outcome were available. These data were used in a maximum likelihood analysis to calculate the best estimates of the parameters of the linear Poisson model. The uncertainties of these parameters were also calculated and their individual influence on the dose-response curve was studied. AVM radiosensitivity was assumed to be the same for all the patients. The radiobiological model used was proved suitable for predicting the treatment outcome pattern of the studied patient material. The radiobiological parameters of the model were calculated for different AVM locations, bleeding histories and AVM sizes. The range of parameter variability had considerable effect on the dose-response curve of AVM. The correlation between the dosimetric data and their corresponding clinical effect could be accurately modelled using the linear Poisson model. The derived response parameters can be introduced into the clinical routine with the calculated accuracy assuming the same methodology in target definition and delineation. The known volume dependence of AVM radiosensitivity was confirmed. Moreover, a trend relating AVM location with its radiosensitivity was observed.

Adolescent↗

PubMed2005

A model (in)validation approach to gait classification.

This paper addresses the problem of human gait classification from a robust model (in)validation perspective. The main idea is to associate to each class of gaits a nominal model, subject to bounded uncertainty and measurement noise. In this context, the problem of recognizing an activity from a sequence of frames can be formulated as the problem of determining whether this sequence could have been generated by a given (model, uncertainty, and noise) triple. By exploiting interpolation theory, this problem can be recast into a nonconvex optimization. In order to efficiently solve it, we propose two convex relaxations, one deterministic and one stochastic. As we illustrate experimentally, these relaxations achieve over 83 percent and 86 percent success rates, respectively, even in the face of noisy data.

Algorithms↗

PubMed2000

[How does model- and situation-specific lack of knowledge affect causal inferences?].

A model proposed by Thüring (1991) for inferences based on causal knowledge was empirically tested. According to this model, two variables affect the certainty with which a causal inference is concluded: insufficiency (model-specific uncertainty) and ambiguity (situation-specific uncertainty). Within an experiment these two variables were manipulated. Both had a very significant (p < .01) influence on causal inferences. In respect to its quantity, variation of ambiguity had the effect predicted in the model. Concerning insufficiency, distinct differences between predicted and empirical ratings were found. Reasons for these deviations and model modifications resulting therefrom are discussed.

Adult↗

PubMed2005

[How about the uncertainty in the haplotypes in the population-based KORA studies?].

In the KORA surveys, numerous candidate genes in the context of type 2 diabetes, myocardial infarction, atherosclerosis or obesity are under investigation. Current focus is on genotyping single nucleotide polymorphism (SNPs). Haplotypes are also of increasing interest: haplotypes are combinations of alleles within a certain section of one chromosome. Analysing haplotypes in genetic association studies is often more efficient than studying the SNPs separately. A statistical problem in this context is the reconstruction of the phase: genotyping the SNPs determines the alleles of an individual at one particular locus of the DNA, but does not reveal which allele is located on which one of the two chromosomes. This information is required when talking about haplotypes. There are statistical approaches to identify the most likely two haplotypes of an individual given the genotypes. However, a certain error in prognosis is unavoidable. There are also errors in the genotypes. These errors are assumed to be small for one SNP but can accumulate over the SNPs involved in one haplotype and thus can induce further uncertainty in the haplotype. It is therefore the aim of our project to quantify the uncertainties in the haplotypes particularly for genes investigated in the KORA surveys. We conduct computer simulations based on the haplotypes and their frequencies observed in the KORA individuals and compare the results with simulations based on mathematical modelling of the evolutionary process ("coalescent models"). The uncertainties in the haplotypes have an impact on the search for association between genes and disease: an association may not be detected as the haplotype uncertainty obscures the haplotype frequency differences between cases and controls. It is a further aim of our project to elucidate the extent of this problem and to develop strategies for reducing it.

Adult↗

PubMed2005

Land application of treated sewage sludge: quantifying pathogen risks from consumption of crops.

AIMS: To predict the number of humans in the UK infected through consumption of root crops grown on agricultural land to which treated sewage sludge has been applied in accordance with the current regulations and guidance (Safe Sludge Matrix). METHODS AND RESULTS: Quantitative risk assessments based on the source, pathway, receptor approach are developed for seven pathogens, namely salmonellas, Listeria monocytogenes, campylobacters, Escherichia coli O157, Cryptosporidium parvum, Giardia, and enteroviruses. Using laboratory data for pathogen destruction by mesophilic anaerobic digestion, and not extrapolating experimental data for pathogen decay in soil to the full 30-month harvest interval specified by the Matrix, predicts 50 Giardia infections per year, but less than one infection per year for the other six pathogens. Assuming linear decay in the soil, a 12-month harvest interval eliminates the risks from all seven pathogens; the highest predicted being one infection of C. parvum in the UK every 45 years. Computer simulations show that a protective effect from binding of pathogens to particulate matter could potentially exaggerate the observed rate of decay in experimental systems. CONCLUSIONS: The results confirm, assuming pathogens behave according to our current understanding, that the risks to humans from consumption of vegetable crops are remote. Furthermore the harvest intervals stipulated by the Safe Sludge Matrix compensate for potential lapses in the operational efficiency of sludge treatment. SIGNIFICANCE AND IMPACT OF THE STUDY: The models demonstrate the huge potential impact of decay in the soil over the 12/30-month intervals specified by the Matrix, although lack of knowledge on the exact nature of soil decay processes is a source of uncertainty. The models enable the sensitivity of the predicted risks to changes in the operational efficiency of sewage sludge treatment to be assessed.

Animals↗

PubMed2005

Decision making in the face of uncertainty and resource constraints: examples from trauma imaging.

The purpose of this review is to illustrate how tools and concepts from decision and cost-effectiveness analyses can be used to help make decisions in the face of uncertainty and resource constraints, select appropriate subjects for imaging, choose between competing imaging modalities, and prioritize future research. Examples from trauma imaging illustrate the use of the presented tools. The author advocates the PROACTIVE approach in deciding which imaging strategies are cost-effective (PRO for defining the problem, reframing the problem from multiple perspectives, and focusing on the objective; ACT for expanding the alternatives, considering the consequences and associated chances of each alternative, and identifying the trade-offs involved; IVE for integrating the evidence and values, optimizing the value of interest, and exploring uncertainty). Simulation models play an important role in the assessment of imaging strategies by helping to identify alternative strategies and to integrate the best-available evidence related to risks, benefits, patient values, and costs. Exploring the uncertainty in the evidence and assessing the value of obtaining more information can help prioritize future research and guide study design.

Cerebral Angiography↗

PubMed2004

Partial life-cycle toxicity and bioconcentration modeling of perfluorooctanesulfonate in the northern leopard frog (Rana pipiens).

A number of recent monitoring studies have demonstrated elevated concentrations of perfluorooctanesulfonate (PFOS) in humans and wildlife throughout the world. Although no longer manufactured in the United States, the global distribution and relative persistence of PFOS indicates a need to understand its potential ecological effects. Presently, little is known concerning toxicity of PFOS in chronic exposures with aquatic species. Therefore, we evaluated the effects of PFOS on survival and development of the northern leopard frog (Rana pipiens) from early embryogenesis through complete metamorphosis. Exposures were conducted via water at measured PFOS concentrations ranging from 0.03 to 10 mg/L. Animals exposed to 10 mg/L began dying within approximately two weeks of test initiation. Survival was not affected by PFOS at lower concentrations; however, time to metamorphosis was delayed and growth reduced in the 3-mg/L treatment group. Tadpoles readily accumulated PFOS directly from water. Using a one-compartment bioaccumulation model, growth was shown to have a modest impact on steady-state PFOS concentrations. Variability in observed growth rates and the possible contribution of a size-dependent decrease in PFOS elimination rate contributed uncertainty to modeling efforts. Nevertheless, fitted uptake and elimination rate constants were comparable to those determined in earlier studies with juvenile rainbow trout. Overall, our studies suggest that R. pipiens is not exceptionally sensitive to PFOS in terms of either direct toxicity or bioconcentration potential of the chemical.

Alkanesulfonic Acids↗

PubMed2000

Estimating the time to extinction in an island population of song sparrows.

We estimated and modelled how uncertainties in stochastic population dynamics and biases in parameter estimates affect the accuracy of the projections of a small island population of song sparrows which was enumerated every spring for 24 years. The estimate of the density regulation in a theta-logistic model (theta = 1.09 suggests that the dynamics are nearly logistic, with specific growth rate r1 = 0.99 and carrying capacity K = 41.54. The song sparrow population was strongly influenced by demographic (ŝigma2(d) = 0.66) and environmental (ŝigma2(d) = 0.41) stochasticity. Bootstrap replicates of the different parameters revealed that the uncertainties in the estimates of the specific growth rate r1 and the density regulation theta were larger than the uncertainties in the environmental variance sigma2(e) and the carrying capacity K. We introduce the concept of the population prediction interval (PPI), which is a stochastic interval which includes the unknown population size with probability (1 - alpha). The width of the PPI increased rapidly with time because of uncertainties in the estimates of density regulation as well as demographic and environmental variance in the stochastic population dynamics. Accepting a 10% probability of extinction within 100 years, neglecting uncertainties in the parameters will lead to a 33% overestimation of the time it takes for the extinction barrier (population size X = 1) to be included into the PPI. This study shows that ignoring uncertainties in population dynamics produces a substantial underestimation of the extinction risk.

Animals↗

PubMed1994

Incorporation of pharmacokinetics in noncancer risk assessment: example with chloropentafluorobenzene.

Noncancer risk assessment traditionally relies on applied dose measures, such as concentration in inhaled air or in drinking water, to characterize no-effect levels or low-effect levels in animal experiments. Safety factors are then incorporated to address the uncertainties associated with extrapolating across species, dose levels, and routes of exposure, as well as to account for the potential impact of variability of human response. A risk assessment for chloropentafluorobenzene (CPFB) was performed in which a physiologically based pharmacokinetic model was employed to calculate an internal measure of effective tissue dose appropriate to each toxic endpoint. The model accurately describes the kinetics of CPFB in both rodents and primates. The model calculations of internal dose at the no-effect and low-effect levels in animals were compared with those calculated for potential human exposure scenarios. These calculations were then used in place of default interspecies and route-to-route safety factors to determine safe human exposure conditions. Estimates of the impact of model parameter uncertainty, as estimated by a Monte Carlo technique, also were incorporated into the assessment. The approach used for CPFB is recommended as a general methodology for noncancer risk assessment whenever the necessary pharmacokinetic data can be obtained.

Animals↗

PubMed1999

Using outcomes data to identify best medical practice: the role of policy models.

Increasingly, physicians are attempting to incorporate best evidence into their clinical decision making. However, best evidence takes a variety of forms, including clinical trials, cohort studies, administrative data, and patient preference data. Incorporating multiple data sources in a way that informs complex clinical decisions is a substantial analytical challenge. One approach to this challenge is to develop a simulation/decision model that explicitly represents the natural history of disease and the impact of treatments on that natural history. The model should be requisite--that is, sufficient in form to address the decision problem--but not overly complex. Such a model can be of value because it (1) allows a variety of viewpoints to be considered, (2) incorporates the best scientific evidence, and (3) permits sensitivity analyses to evaluate the impact of alternative clinical scenarios and uncertainty in model inputs. The Stroke Prevention Policy Model (SPPM) illustrates this approach. The SPPM is a simulation model designed to predict the best among various treatment alternatives for preventing strokes. Similar models can be applied to treatment outcomes for liver disease.

Benchmarking↗

PubMed2005

Sensitive parameters in predicting exposure contaminants concentration in a risk assessment process.

A sensitivity analysis (SA) was conducted on the analytical models considered in the risk-based corrective-action (RBCA) methodology of risk analysis, as developed by the American Society for Testing of Materials (ASTM), to predict a contaminant's concentration in the affected medium at the point of human exposure. These models are of interest because evaluations regarding the best approach to contaminated site remediation are shifting toward increased use of risk-based decision, and the ASTM RBCA methodology represents the most effective and internationally widely used standardized guide for risk assessment process. This paper identifies key physical and chemical parameters that need additional precision and accuracy consideration in order to reduce uncertainty in models prediction, thereby saving time, money and engineering effort in the data collection process. SA was performed applying a variance-based method to organic contaminants migration models with reference to soil-to-groundwater leaching ingestion exposure scenario. Results indicate that model output strongly depends on the organic-carbon partition coefficient, organic-carbon content, net infiltration, Darcy velocity, source-receptor distance, and first-order decay constant.

Benzene↗