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Results for “Uncertainty modelling”

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At least 379 records · Page 21Linked to original sources

PubMed1991

A model for estimating abundance of cattle grub (Diptera: Oestridae) from the proportion of uninfested cattle as determined by serology.

A model is presented for determining the abundance of cattle grubs in the backs of calves from the proportion of uninfested calves in a herd. The distribution of grubs in calves' backs was compared with the negative binomial, but no relationships were found among the distribution parameters, suggesting that the negative binomial is an inappropriate choice for the basis of a sampling model. The relationship between the mean number of grubs per animal (mean), variance (delta 2), and proportion of uninfested calves (p0) in a herd was determined and used as the basis for the sampling model. The relationship between p0 and p0e [determined using serology (ELISA)] was evaluated. The variance of estimates of mean grubs per animal based on the regression model and uncertainty due to using p0e as an estimate of p0 was examined. A test of the model indicated that p0e could be used to obtain a reliable estimate of mean grubs per animal and that the method would be applicable for monitoring grub populations, assessing chemical control programmes, and determining release rates of sterile insects for control.

Animals↗

PubMed1997

Physiologically based pharmacokinetic modeling of a homologous series of barbiturates in the rat: a sensitivity analysis.

Sensitivity analysis studies the effects of the inherent variability and uncertainty in model parameters on the model outputs and may be a useful tool at all stages of the pharmacokinetic modeling process. The present study examined the sensitivity of a whole-body physiologically based pharmacokinetic (PBPK) model for the distribution kinetics of nine 5-n-alkyl-5-ethyl barbituric acids in arterial blood and 14 tissues (lung, liver, kidney, stomach, pancreas, spleen, gut, muscle, adipose, skin, bone, heart, brain, testes) after i.v. bolus administration to rats. The aims were to obtain new insights into the model used, to rank the model parameters involved according to their impact on the model outputs and to study the changes in the sensitivity induced by the increase in the lipophilicity of the homologues on ascending the series. Two approaches for sensitivity analysis have been implemented. The first, based on the Matrix Perturbation Theory, uses a sensitivity index defined as the normalized sensitivity of the 2-norm of the model compartmental matrix to perturbations in its entries. The second approach uses the traditional definition of the normalized sensitivity function as the relative change in a model state (a tissue concentration) corresponding to a relative change in a model parameter. Autosensitivity has been defined as sensitivity of a state to any of its parameters; cross-sensitivity as the sensitivity of a state to any other states' parameters. Using the two approaches, the sensitivity of representative tissue concentrations (lung, liver, kidney, stomach, gut, adipose, heart, and brain) to the following model parameters: tissue-to-unbound plasma partition coefficients, tissue blood flows, unbound renal and intrinsic hepatic clearance, permeability surface area product of the brain, have been analyzed. Both the tissues and the parameters were ranked according to their sensitivity and impact. The following general conclusions were drawn: (i) the overall sensitivity of the system to all parameters involved is small due to the weak connectivity of the system structure; (ii) the time course of both the auto- and cross-sensitivity functions for all tissues depends on the dynamics of the tissues themselves, e.g., the higher the perfusion of a tissue, the higher are both its cross-sensitivity to other tissues' parameters and the cross-sensitivities of other tissues to its parameters; and (iii) with a few exceptions, there is not a marked influence of the lipophilicity of the homologues on either the pattern or the values of the sensitivity functions. The estimates of the sensitivity and the subsequent tissue and parameter rankings may be extended to other drugs, sharing the same common structure of the whole body PBPK model, and having similar model parameters. Results show also that the computationally simple Matrix Perturbation Analysis should be used only when an initial idea about the sensitivity of a system is required. If comprehensive information regarding the sensitivity is needed, the numerically expensive Direct Sensitivity Analysis should be used.

Animals↗

PubMed2001

Developing seasonal ammonia emission estimates with an inverse modeling technique.

Significant uncertainty exists in magnitude and variability of ammonia (NH3) emissions, which are needed for air quality modeling of aerosols and deposition of nitrogen compounds. Approximately 85% of NH3 emissions are estimated to come from agricultural nonpoint sources. We suspect a strong seasonal pattern in NH 3 emissions; however, current NH3 emission inventories lack intra-annual variability. Annually averaged NH 3 emissions could significantly affect model-predicted concentrations and wet and dry deposition of nitrogen-containing compounds. We apply a Kalman filter inverse modeling technique to deduce monthly NH3 emissions for the eastern U.S. Final products of this research will include monthly emissions estimates from each season. Results for January and June 1990 are currently available and are presented here. The U.S. Environmental Protection Agency (USEPA) Community Multiscale Air Quality (CMAQ) model and ammonium (NH4+) wet concentration data from the National Atmospheric Deposition Program (NADP) network are used. The inverse modeling technique estimates the emission adjustments that provide optimal modeled results with respect to wet NH4+ concentrations, observational data error, and emission uncertainty. Our results suggest that annual average NH 3 emissions estimates should be decreased by 64% for January 1990 and increased by 25% for June 1990. These results illustrate the strong differences that are anticipated for NH3 emissions.

Agriculture↗

PubMed1998

Uncertainty analysis methods for comparing predictive models and biomarkers: A case study of dietary methyl mercury exposure.

Biologically based markers (biomarkers) are currently used to provide information on exposure, health effects, and individual susceptibility to chemical and radiological wastes. However, the development and validation of biomarkers are expensive and time consuming. To determine whether biomarker development and use offer potential improvements to risk models based on predictive relationships or assumed values, we explore the use of uncertainty analysis applied to exposure models for dietary methyl mercury intake. We compare exposure estimates based on self-reported fish intake and measured fish mercury concentrations with biomarker-based exposure estimates (i.e., hair or blood mercury concentrations) using a published data set covering 1 month of exposure. Such a comparison of exposure model predictions allowed estimation of bias and random error associated with each exposure model. From these analyses, both bias and random error were found to be important components of uncertainty regarding biomarker-based exposure estimates, while the diary-based exposure estimate was susceptible to bias. Application of the proposed methods to a simple case study demonstrates their utility in estimating the contribution of population variability and measurement error in specific applications of biomarkers to environmental exposure and risk assessment. Such analyses can guide risk analysts and managers in the appropriate validation, use, and interpretation of exposure biomarker information.

Animals↗

PubMed1998

Numerical errors and uncertainties in finite-element modeling of trabecular bone.

Although micromechanical finite-element models are being increasingly used to help interpret the results of bio-mechanical tests, there has not yet been a systematic study of the numerical errors and uncertainties that occur with these methods. In this work, finite-element models of human L1 vertebra have been used to analyze the sensitivity of the calculated elastic moduli to resolution, boundary conditions, and variations in the Poisson's ratio of the tissue material. Our results indicate that discretization of the bone architecture, inherent in the tomography process, leads to an underestimate in the calculated elastic moduli of about 20% at 20 microm resolution; these errors vary roughly linearly with the size of the image voxels. However, it turns out that there is a cancellation of errors between the softening introduced by the discretization of the bone architecture and the excess bending resistance of eight-node hexahedral finite elements. Our empirical finding is that eight-node cubic elements of the same size as the image voxels lead to the most accurate calculation for a given number of elements, with errors of less than 5% at 20 microm resolution. Comparisons with mechanical testing are also hindered by uncertainties in the grip conditions: our results show that these uncertainties are of comparable magnitude to the systematic differences in mechanical testing methods. Both discretization errors and uncertainties in grip conditions have a smaller effect on relative moduli, used when comparing between different specimens or different load directions, than on an absolute modulus. The effects of variations in the Poisson's ratio of the bone tissue were found to be negligible.

Bone and Bones↗

PubMed2004

The use of probabilistic decision models in technology assessment : the case of total hip replacement.

There is increasing recognition that decision modelling is central to health technology assessment and, in particular, to analyses to support formal decision making regarding the funding of the use of new technologies. In part, the key role of decision analysis stems from the need to handle multiple sources of uncertainty in the available evidence. The use of probabilistic decision analysis is a means of reflecting the parameter uncertainty in models and presenting this in a comprehensible manner to decision makers. In this article, we demonstrate the potential role of probabilistic models using the case study of total hip replacement surgery.A cost-effectiveness model was constructed to compare the Charnley and Spectron hip prostheses in terms of lifetime costs and quality-adjusted life-years (QALYs). Revision rates were estimated from the Swedish National Total Hip Arthroplasty Register (1992-2000); the risk of revision with the Spectron prosthesis relative to the Charnley prosthesis was 0.67 (95% confidence interval [CI] 0.32, 1.02) for early revisions and 0.26 (95% CI 0.07, 0.46) for late revisions. This lower revision risk resulted in the Spectron generating more QALYs than the Charnley prosthesis. Based on mean costs and QALYs, the Spectron results in cost savings in younger patients, and generates incremental cost-effectiveness ratios of between pound1000 and pound16 000 in older patient groups. The probabilistic results from the model indicated that, if it is assumed that decision makers are willing to pay up to pound20 000 per additional QALY, the probability of the Spectron being the more cost-effective prosthesis ranged between 70% and 100%, depending on the age and sex of the patient.This article looks at the application of probabilistic decision modelling using total hip replacement as a case study to emphasis the need for decision models to quantify all sources of parameter uncertainty and to clearly distinguish parameter uncertainty from subgroup heterogeneity.

Adult↗

PubMed2001

Responding to uncertainty in nursing practice.

Uncertainty is a fact of life for practising clinicians and cannot be avoided. This paper outlines the model of uncertainty presented by Katz (1988, Cambridge University Press, Cambridge, UK. pp. 544-565) and examines the descriptive and normative power of three broad theoretical and strategic approaches to dealing with uncertainty: rationality, bounded rationality and intuition. It concludes that nursing research and development (R&D) must acknowledge uncertainty more fully in its R&D agenda and that good-quality evaluation studies which directly compare intuitive with rational-analytical approaches for given clinical problems should be a dominant feature of future R&D.

Adaptation, Psychological↗

PubMed2004

Modeling the prevalence of Bacillus cereus spores during the production of a cooked chilled vegetable product.

In minimally processed vegetable foods, pathogenic spore-forming bacteria pose a significant hazard. As part of a quantitative risk assessment, we used Bayesian belief methods to model the uncertainty and variability of the number of Bacillus cereus spores that can be found in packets of a vegetable puree. The model combines specific information from the manufacturer, experimental data on inactivation of spores, and expert opinion concerning spore concentrations in the raw vegetables and ingredients. Sensitivity analysis revealed that spore contamination of added ingredients contributes most uncertainty to the assessment. The assessment produced a quantitative estimate of the prevalence of B. cereus spores in packets of vegetable puree at the end point of the manufacturing process.

Bacillus cereus↗

PubMed2004

Bayesian methods for analysis of binary outcome data in cluster randomized trials on the absolute risk scale.

A Bayesian hierarchical modelling approach to the analysis of cluster randomized trials has advantages in terms of allowing for full parameter uncertainty, flexible modelling of covariates and variance structure, and use of prior information. Previously, such modelling of binary outcome data required use of a log-odds ratio scale for the treatment effect estimate and an approximation linking the intracluster correlation (ICC) to the between-cluster variance on a log-odds scale. In this paper we develop this method to allow estimation on the absolute risk scale, which facilitates clinical interpretation of both the treatment effect and the between-cluster variance. We describe a range of models and apply them to data from a trial of different interventions to promote secondary prevention of coronary heart disease in primary care. We demonstrate how these models can be used to incorporate prior data about typical ICCs, to derive a posterior distribution for the number needed to treat, and to consider both cluster and individual level covariates. Using these methods, we can benefit from the advantages of Bayesian modelling of binary outcome data at the same time as providing results on a clinically interpretable scale.

Bayes Theorem↗

PubMed2002

Use of Akaike information criteria for model selection and inference. An application to assess prevention of gastrointestinal parasitism and respiratory mortality of Guinean goats in Kolda, Senegal.

A field experiment was carried out in Kolda (southern Senegal) from July 1986 to July 1988. Its goals were to: (1) describe the patterns of mortality of female Guinean goats by age, season and year; (2) assess preventive measures against respiratory diseases and gastrointestinal parasitism in reducing mortality; and (3) estimate the overall impact of these measures on survival to 1 year of age. Preventive measures for respiratory disease included vaccination against peste des petits ruminants (PPR) and pneumonic pasteurellosis (Pasteurella multocida types A and D). Control of gastrointestinal parasites was by deworming does with morantel (7.5mg kg(-1), three times during the rainy season). The effects of vaccines and deworming were tested in a randomised factorial field experiment with villages being the experimental units. A total of 19 villages, 113 goat herds and 1,458 goats were included in the study. Generalised linear models of survival for five cohorts of goats (defined by five different birth seasons) used a binomial assumption for the response distribution and a complementary log-log link. Explanatory variables included age, season, year, vaccination, deworming and their interactions. A complex a priori model was built on the basis of previous epidemiological knowledge; a purposely selected set of simpler models was compared to this full model by the Akaike information criterion (AIC) and derived statistics. Inference on 1-year survival and treatment effects accounted for model-selection uncertainty. It was carried out with a bootstrap procedure and used information from the whole set of selected models. Large variations in mortality by year and season were observed but no regular seasonal pattern was apparent. Mortality probabilities of kids in dewormed groups decreased quickly after birth, but remained elevated up to 9 months of age in the non-dewormed groups. Deworming lowered the risk of mortality. Vaccination alone was not protective (except during an observed outbreak of PPR).

Animals↗

PubMedDate not supplied

Strategies for extraction of quantitative data from volumetric dynamic cardiac positron emission tomography data.

The ability of positron emission tomography (PET) to serve as a useful myocardial perfusion indicator is well established. We describe a methodology for obtaining reliable quantitative kinetic parameters from dynamic cardiac PET data. Reconstructed images of the myocardium are subdivided into three-dimensional volumes of interest which are used to obtain quantitative measures of myocardial perfusion over physiologically meaningful anatomical regions. The quantitation technique rigorously models the uncertainty of estimated parameters while compensating for effects such as patient motion and partial volumes to arrive at model parameters with well-established confidence intervals.

Coronary Angiography↗

PubMedDate not supplied

Multivariate Markovian modeling of tuberculosis: forecast for the United States.

We have developed a computer-implemented, multivariate Markov chain model to project tuberculosis (TB) incidence in the United States from 1980 to 2010 in disaggregated demographic groups. Uncertainty in model parameters and in the projections is represented by fuzzy numbers. Projections are made under the assumption that current TB control measures will remain unchanged for the projection period. The projections of the model demonstrate an intermediate increase in national TB incidence (similar to that which actually occurred) followed by continuing decline. The rate of decline depends strongly on geographic, racial, and ethnic characteristics. The model predicts that the rate of decline in the number of cases among Hispanics will be slower than among white non-Hispanics and black non-Hispanics a prediction supported by the most recent data.

Adolescent↗

PubMedDate not supplied

Transboundary impacts on regional ground water modeling in Texas.

Recent legislation required regional grassroots water resources planning across the entire state of Texas. The Texas Water Development Board (TWDB), the state's primary water resource planning agency, divided the state into 16 planning regions. Each planning group developed plans to manage both ground water and surface water sources and to meet future demands of various combinations of domestic, agricultural, municipal, and industrial water consumers. This presentation describes the challenges in developing a ground water model for the Llano Estacado Regional Water Planning Group (LERWPG), whose region includes 21 counties in the Southern High Plains of Texas. While surface water is supplied to several cities in this region, the vast majority of the regional water use comes from the High Plains aquifer system, often locally referred to as the Ogallala Aquifer. Over 95% of the ground water demand is for irrigated agriculture. The LERWPG had to predict the impact of future TWDB-projected water demands, as provided by the TWDB, on the aquifer for the period 2000 to 2050. If detrimental impacts were noted, alternative management strategies must be proposed. While much effort was spent on evaluating the current status of the ground water reserves, an appropriate numerical model of the aquifer system was necessary to demonstrate future impacts of the predicted withdrawals as well as the effects of the alternative strategies. The modeling effort was completed in the summer of 2000. This presentation concentrates on the political, scientific, and nontechnical issues in this planning process that complicated the modeling effort. Uncertainties in data, most significantly in distribution and intensity of recharge and withdrawals, significantly impacted the calibration and predictive modeling efforts. Four predictive scenarios, including baseline projections, recurrence of the drought of record, precipitation enhancement, and reduced irrigation demand, were simulated to identify counties at risk of low final ground water storage volume or low levels of satisfied demand by 2050.

Conservation of Natural Resources↗

PubMedDate not supplied

Using objective and subjective information to develop distributions for probabilistic exposure assessment.

The propagation of variance through complex exposure models has been simplified by simulation software running on desktop computers; however, the appropriate representation of variability in, and uncertainty about, model inputs remains a challenge. The U.S. Environmental Protection Agency (EPA) has indicated that the Monte Carlo simulation approach to exposure assessment is acceptable as long as input distributions are credible, i.e., rooted in fact (EPA, 1992). The use of distributions based on empirical evidence is certainly desirable; however, it is not always possible to obtain measurements for all exposure model inputs under the conditions of interest. Where objective evidence is scarce or unavailable, subjective judgments are necessary. In this work, the application of both statistical and subjective approaches to the development of distributions for exposure model inputs is explored. The consequences of one's approach to distribution development, on the interpretation and applicability of the output, are also considered.

Data Interpretation, Statistical↗

PubMed1995

Models of G-protein coupled receptors revised for family-wide compliance with experimental data. A new sequence accommodation suggested for helix G.

The G-protein coupled receptors form a vast superfamily. The hydrophobic sequences of the transmembrane regions can be consistently and unambiguously aligned for nearly all, even distantly related, members owing to striking conserved patterns. As the fold is highly conserved in evolution, a model of the structure of the transmembrane helix bundle built for any individual receptor will thus be of family-wide relevance. Consequently the model must comply with key results experimentally obtained for other individual receptors. Meeting this demand can greatly reduce the uncertainties in modelling G-protein coupled receptors. This present communication shows how our recent template model based on the backbone structure of bacteriorhodopsin is revised according to that demand. Although it turns out to be in accordance with the experimental data in most of its parts, this revision suggests a sequence accommodation to helix G that is different from all other published models.

Amino Acid Sequence↗

PubMed2000

Bayesian synthesis for quantifying uncertainty in predictions from process models.

The Bayesian synthesis method is reviewed and judged to be useful for determining posterior distributions and interval estimates for inputs and outputs of process-based forest models. The method furnishes posterior distributions of the values of a model's parameters and response variables. The method also provides estimates of correlation among the parameters and output variables. Bayesian synthesis is the only type of uncertainty analysis that affords incorporation of all the information available to the investigator, in addition to the information contained in the model itself.

Journal Article↗

PubMed2001

Tibia lead levels and methodological uncertainty in 12-year-old children.

In vivo bone lead measurements with 109Cd-based K-shell X-ray fluorescence (XRF) have been used to assess long-term lead exposure in adults. Tibia lead levels were measured in 210 children (106 boys, 104 girls) of 11-12(1/2) years of age in a lead smelter town and in a control (nonexposed) town. Tibia lead levels, methodological uncertainties, and models of some of the factors influencing them are presented. 109Cd-based K-shell XRF tibia lead methodological uncertainty in children is comparable to that in adults.

Child↗

PubMed1999

Sources of uncertainty in dose-response modeling of epidemiological data for cancer risk assessment.

Epidemiologic data is increasingly being used for dose-response analysis in risk assessment. The Environmental Protection Agency (EPA) and other U.S. agencies have expressed a preference for using epidemiologic data rather than toxicologic data when possible. However, there are a number of important sources of uncertainty in using epidemiologic data for this purpose that need to be clearly recognized and, when possible, quantified. This paper presents a critical review of the major sources of uncertainty in the use of epidemiologic data for cancer risk assessment. These may include: (1) study design issues such as potential confounding and other biases, inadequate sample size, and followup, (2) the choice of the data set, (3) specification of the dose-response model, (4) estimation of exposure and dose, and (5) unrecognized variability in susceptibility. Examples from risk assessments for cadmium, asbestos, and diesel exhaust are used to illustrate the potential magnitude of some of these sources of uncertainty. It is shown that the overall uncertainty from these various sources combined may often result in highly uncertain risk estimates from dose-response modeling of epidemiologic data. For this reason, we believe it is best to present a range of possible risk estimates, which, to the extent possible, reflects the variability and uncertainty inherent in the dose-response evaluation of epidemiologic data.

Asbestos↗