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Results for “Forecasting”

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At least 199 records · Page 11Linked to original sources

PubMed2002

The use of a neural network to forecast daily grass pollen concentration in a Mediterranean region: the southern part of the Iberian Peninsula.

BACKGROUND: Pollen allergy is a common disease causing hayfever in 15% of the population in Europe. Medical studies report that a prior knowledge of pollen content in the air can be useful in the management of pollen-related diseases. OBJECTIVES: The aim of this work was to forecast daily Poaceae pollen concentrations in the air by using meteorological data and pollen counts from previous days as independent variables. METHODS: Linear regression models and co-evolutive neural network models were used for this study. Pollen was monitored by a Hirst-type spore trap using standard techniques. The data were obtained from the Spanish Aerobiology Network database, University of Cordoba Monitoring Unit. The set of data includes a series of 20 years, from 1982 to 2001. A classification of the years according to their allergenic potential was made using a K-mean cluster analysis with pollen and meteorological parameters. Statistical analysis was applied to all the years of each class with the exception of the most recent year, which was used for model validation. RESULTS: It was observed that cumulative variables and pollen values from previous days are the most important factors in the models. In general, neural network equations produce better results than linear regression equations. CONCLUSION: Co-evolutive neural network models, which obtain the best forecasts (an almost 90% "good" classification), make it possible to predict daily airborne Poaceae pollen concentrations. This new system based on neural network models is a step toward the automation of the pollen forecast process.

Environmental Pollution↗

PubMed2004

Long-term forecast: key to groundwater protection.

Groundwater resources are at risk from pollution, climate change and land-management practices. Long-term forecast is a tool to demonstrate future development and to support decisions on measures which can be implemented, controlled and eventually corrected. As the basis for preventive action, a forecast can be viewed as a key to groundwater protection. The soil zone plays a vital role in groundwater protection. Research on soil and groundwater trends, as affected by change of climate and/or land-management practices, is on the agenda. Integrated research is necessary to develop numerical soil-water system models reflecting all relevant transport processes at various scales. Even relatively simple problems like forecasting pollutant release from contaminated materials are difficult to resolve. Batch tests may be sufficient in low-risk cases. At higher risk when contaminated substrates are chosen for deposition on a large scale, a thorough characterization of the materials is necessary covering all aspects of stability and possible changes of the environment.

Conservation of Natural Resources↗

PubMed2000

Forecasting air pollution potential: a synoptic climatological approach.

This paper describes the development and application of an air pollution potential (APP) forecast model based on a synoptic climatological approach in a heavily industrialized area in Durban, South Africa. The aim of the forecasting procedure, based on a system of orange, red, and all-clear alerts, was to give industry advance warning of periods of poor atmospheric dispersion so that it could take action to reduce emissions. The key meteorological parameter in accurately identifying the commencement of an APP episode was found to be negative surface pressure tendency. Wind direction was the most useful parameter in estimating the end point of an APP episode. The model was very successful in identifying periods of elevated SO2, but there is a need for further refinement in forecasting the end point of an episode.

Air Movements↗

PubMed2002

Advances in real-time flood forecasting.

This paper discusses the modelling of rainfall-flow (rainfall-run-off) and flow-routeing processes in river systems within the context of real-time flood forecasting. It is argued that deterministic, reductionist (or 'bottom-up') models are inappropriate for real-time forecasting because of the inherent uncertainty that characterizes river-catchment dynamics and the problems of model over-parametrization. The advantages of alternative, efficiently parametrized data-based mechanistic models, identified and estimated using statistical methods, are discussed. It is shown that such models are in an ideal form for incorporation in a real-time, adaptive forecasting system based on recursive state-space estimation (an adaptive version of the stochastic Kalman filter algorithm). An illustrative example, based on the analysis of a limited set of hourly rainfall-flow data from the River Hodder in northwest England, demonstrates the utility of this methodology in difficult circumstances and illustrates the advantages of incorporating real-time state and parameter adaption.

Algorithms↗

PubMedDate not supplied

Development of a parametric simulation model for forecasting goal-oriented treatment outcomes.

BACKGROUND: Treatment-to-goal (TTG) analyses are frequently used to predict guideline-directed population control rates for drug therapies based on mean efficacy data. Nevertheless, estimates are commonly inaccurate because variability in efficacy is not considered. A new methodology was developed to improve TTG forecasting. METHODS: Patient-level blood pressure (BP) lowering data sets, designed to simulate clinical trial results, were generated for testing from three underlying distributions: normal, lognormal, and beta. To emulate real-world conditions where patient-level data are unavailable, two approaches were considered: parametric--simulated BP lowering data were generated using the mean and standard deviation of the test data sets; and point-estimate--BP lowering was uniformly assigned as the mean lowering. BP control (systolic BP < 140 and diastolic BP < 90 mmHg) was forecasted by subtracting values generated by these two methods from baseline BP values in untreated hypertensive patients (n = 2483) from the Third National Health and Nutrition Examination Survey. Estimated control rates were compared to analyses where the patient-level data sets were bootstrapped. RESULTS: We assumed mean (+/- SD) BP lowering of 20 (12) mmHg systolic and 14 (7) mmHg diastolic. Parametric method predicted a BP control rate of 66.9% [95% confidence interval (CI) 65.7-67.9], similar to the bootstrapping approach (67.3%, 95% CI 65.9-68.8). The control rate projected based on the point-estimate method was 75.5%. The point-estimate method frequently led to substantially different results under a wide range of model assumptions. CONCLUSIONS: A new parametric-based forecasting method, which addresses underlying variability, improves on estimates based on mean efficacy only. In the absence of patient-level data, this method is generalizable to different therapeutic areas.

Adolescent↗

PubMed2001

Product liability forecasting for asbestos-related personal injury claims: a multidisciplinary approach.

This paper focuses on three aspects of forecasting models for asbestos-related disease/injuries relating to the Manville asbestos case: (1) The structure of forecasting models for asbestos-related personal injuries. (2) The epidemiologic evidence supporting the selected model structure and the constraints on the modeling assumptions imposed by that evidence. (3) The range of uncertainty associated with projections based on these forecasting models and issues relating to decision making under uncertainty.

Asbestos↗

PubMed1992

A review of methods to forecast restorative treatment needs.

Decision makers in the areas of health policy, resource allocation, and manpower requirements rely implicitly on estimations of treatment needs on which to base their forecasts. The less specific the treatment-need estimate, the less precise the forecast. In previous decades, high caries rates were so prevalent that the dental profession could risk having inexact projections because overwhelming need and demand existed. However, rampant decay is no longer a common occurrence. Decay levels are declining in our nation's children and adults have fewer missing teeth. Therefore, restorative treatment needs and patterns in adult populations are transforming at a time when health care costs are spiraling and budget analysts at all levels of government are questioning the priority of continued support of dental care, dental education, and dental research at current levels. The purpose of this review is to present the existing methods of forecasting restorative treatment needs and to postulate the development of a new method based on the collective experiences of practicing dentists nationwide, an empirical method, to convert surface-specific oral health status data to restorative treatment need information. Need estimations based on empirical data would more accurately reflect the actual distribution of services that practicing dentists provide.

Adult↗

PubMed2005

Forecasting one's future based on fleeting subjective experiences.

When we forecast our futures, to what extent do we rely on explicit and concrete facts versus implicit and fleeting subjective experiences? Results from two studies reveal that forecasting judgments hinge on at least two fleeting experiences: the specific incidental emotions one happens to feel at the time of forming a judgment and the subjective ease-of-thought-generation. Results also reveal that imposing accountability for the accuracy of one's forecast provides no simple remedy. Incidental emotions, the ease-of-thought-generation, and accountability combine multiplicatively in a three-way interaction. Although accountability attenuates the respective effects of incidental fear and incidental anger, doing so has the undesirable effect of amplifying the ease-of-thought-generation effects that fear otherwise suppresses. In no instance does accountability completely eliminate the unintended effects of these fleeting subjective experiences. Discussion addresses implications for theories of affect and social cognition as well as for applications to risk perception.

Adolescent↗

PubMed1998

Forecasting American health care: how we got here and where we might be going.

This article is a form of thinking about the future properly regarded as conditional forecasting. It begins by reminding readers of the enormous changes in American medicine since World War II. The second part revisits critically an earlier effort at conditional forecasting for 1995 that Paul Starr and I published in the early 1980s. Besides reviewing the prescience of our forecasts, the second part outlines the earlier trends in progress we identified and the four combinations of political and economic settings we explored. On that basis, the final part takes up the challenge of anticipating sensibly some possible medical futures in the America of the early twenty-first century, a task which excludes simple extrapolation.

Delivery of Health Care↗

PubMed2001

Evaluating the performance of the Lee-Carter method for forecasting mortality.

Lee and Carter (LC) published a new statistical method for forecasting mortality in 1992. This paper examines its actual and hypothetical forecast errors, and compares them with Social Security forecast errors. Hypothetical historical projections suggest that LC tended to underproject gains, but by less than did Social Security. True e0 was within the ex ante 95% probability interval 97% of the time overall, but intervals were too broad up to 40 years and too narrow after 50 years. Projections to 1998 made after 1945 always contain errors of less than two years. Hypothetical projections for France, Sweden, Japan, and Canada would have done well. Changing age patterns of mortality decline over the century pose problems for the method.

Age Factors↗

PubMed2006

Forecasting extinction risk with nonstationary matrix models.

Matrix population growth models are standard tools for forecasting population change and for managing rare species, but they are less useful for predicting extinction risk in the face of changing environmental conditions. Deterministic models provide point estimates of lambda, the finite rate of increase, as well as measures of matrix sensitivity and elasticity. Stationary matrix models can be used to estimate extinction risk in a variable environment, but they assume that the matrix elements are randomly sampled from a stationary (i.e., non-changing) distribution. Here we outline a method for using nonstationary matrix models to construct realistic forecasts of population fluctuation in changing environments. Our method requires three pieces of data: (1) field estimates of transition matrix elements, (2) experimental data on the demographic responses of populations to altered environmental conditions, and (3) forecasting data on environmental drivers. These three pieces of data are combined to generate a series of sequential transition matrices that emulate a pattern of long-term change in environmental drivers. Realistic estimates of population persistence and extinction risk can be derived from stochastic permutations of such a model. We illustrate the steps of this analysis with data from two populations of Sarracenia purpurea growing in northern New England. Sarracenia purpurea is a perennial carnivorous plant that is potentially at risk of local extinction because of increased nitrogen deposition. Long-term monitoring records or models of environmental change can be used to generate time series of driver variables under different scenarios of changing environments. Both manipulative and natural experiments can be used to construct a linking function that describes how matrix parameters change as a function of the environmental driver. This synthetic modeling approach provides quantitative estimates of extinction probability that have an explicit mechanistic basis.

Adaptation, Physiological↗

PubMed1993

Forecasting the number of diabetic patients in The Netherlands in 2005.

OBJECTIVES: There is evidence from past decades that the number of diabetic patients has increased independently of changes in demography. A static model that takes into account only demographic changes is therefore unable to forecast the expected number of diabetic patients correctly. METHODS: We developed a dynamic model in which actual incidence, prevalence, and life expectancy data are used and alternative assumptions about future trends in these parameters can be incorporated. RESULTS: This dynamic model forecasts higher numbers of diabetic patients than the less sophisticated static model. According to the dynamic model, a 46% increase in the number of diabetic patients in The Netherlands can be expected, from 244,000 in 1990 to 355,000 in 2005 (about 2.5% annually). The static model forecasts a 22% increase. CONCLUSIONS: Diabetes mellitus will become a more serious public health problem than can be expected from demographic changes only. In planning future health care, monitoring of trends in incidence, prevalence, remission, and mortality or life expectancy is a necessary prerequisite.

Diabetes Mellitus↗

PubMedDate not supplied

Forecasting the number of soil samples required to reduce remediation cost uncertainty.

Sampling scheme design is an important step in the management of polluted sites. It largely controls the accuracy of remediation cost estimates. In practice, however, sampling is seldom designed to comply with a given level of remediation cost uncertainty. In this paper, we present a new technique that allows one to estimate of the number of samples that should be taken at a given stage of investigation to reach a forecasted level of accuracy. The uncertainty is expressed both in terms of volume of polluted soil and overall cost of remediation. This technique provides a flexible tool for decision makers to define the amount of investigation worth conducting from an environmental and financial perspective. The technique is based on nonlinear geostatistics (conditional simulations) to estimate the volume of soil that requires remediation and excavation and on a function allowing estimation of the total cost of remediation (including investigations). The geostatistical estimation accounts for support effect, information effect, and sampling errors. The cost calculation includes mainly investigation, excavation, remediation, and transportation. The application of the technique on a former smelting work site (lead pollution) demonstrates how the tool can be used. In this example, the forecasted volumetric uncertainty decreases rapidly for a relatively small number of samples (20-50) and then reaches a plateau (after 100 samples). The uncertainty related to the total remediation cost decreases while the expected total cost increases. Based on these forecasts, we show how a risk-prone decision maker would probably decide to take 50 additional samples while a risk-averse decision maker would take 100 samples.

Forecasting↗

PubMed2004

Disability forecasts and future Medicare costs.

The traditional focus of disability research has been on the elderly, with good reason. Chronic disability is much more prevalent among the elderly, and it has a more direct impact on the demand for medical care. It is also important to understand trends in disability among the young, however, particularly if these trends diverge from those among the elderly. These trends could have serious implications for future health care spending because more disability at younger ages almost certainly translates into more disability among tomorrow's elderly, and disability is a key predictor of health care spending. Using data from the Medicare Current Beneficiary Survey (MCBS) and the National Health Interview Study (NHIS), we forecast that per-capita Medicare costs will decline for the next fifteen to twenty years, in accordance with recent projections of declining disability among the elderly. By 2020, however, the trend reverses. Per-capita costs begin to rise due to growth in disability among the younger elderly. Total costs may well remain relatively flat until 2010 and then begin to rise because per-capita costs will cease to decline rapidly enough to offset the influx of new elderly people. Overall, cost forecasts for the elderly that incorporate information about disability among today's younger generations yield more pessimistic scenarios than those based solely on elderly data sets, and this information should be incorporated into official Medicare forecasts.

Adult↗

PubMed1985

Alcoholism and drug abuse service forecasting models: a comparative discussion.

Several demand-based models for forecasting alcoholism/drug abuse bed needs have been developed over the past few years. Demand models have inherent problems for predicting future needs because of the unknown relationship between the demand group and the nondemand group. Nonetheless, these models hold promise for the alcoholism/drug abuse service system because of some of the unique characteristics of that system. Three models for forecasting alcoholism bed need and one for forecasting drug abuse bed need are discussed; the strengths and weaknesses of each are presented. Projections for the same geographic area are compared. Criteria for selecting one model over another are also recommended.

Alcoholism↗

PubMed1995

Time series forecasts of poison center call volume.

We tested the hypothesis that time series analysis can provide accurate predictions of future poison center telephone call volume by a prospective stochastic time series modeling of calls to a university-based regional poison center. All callers evaluated and managed during two sequential years had the time and date of the call recorded in a computer database. Time series variables were formed for poison center calls per hour. Prediction models were developed from the 1992 data and included four types: raw observations, moving average, means with moving average smoothing, and autoregressive integrated moving average. Forecasts from each model were tested against observations from the first 26 weeks of 1993. Each model's adequacy was tested on residuals by autocorrelation functions, integrated periodograms, linear regression, and differences among the variances. A total of 44,584 calls were received in 1992 and 24,781 in the first half of 1993. Large periodic variations in call volume with time of day were found (p < 0.001). The model based on arithmetic means of each hour of the week with three-point moving average smoothing yielded the most accurate forecasts and explained 58.5% of the variation observed in the 1993 test series (p < 0.001). Time series analysis can provide powerful, accurate short range forecasts of future poison center telephone call volume. Simpler, less expensive models performed best in this study.

Computers↗

PubMed2006

Forecasting veterinary school admission probabilities for undergraduate student profiles.

Increased competition for veterinary school admission has created a need to determine whether individual students are likely to be successful candidates for veterinary school admission early in their undergraduate careers. Students invest considerable time and money in pre-veterinary courses of study, hoping for acceptance into professional veterinary school. A forecasting model was developed to predict the likelihood of students with particular characteristics gaining acceptance. Characteristics such as gender, age, size of high school, and ACT, are known upon entrance into college and can be used to determine the likelihood of an individual's being accepted. Data were gathered from the Louisiana State University College of Veterinary Medicine (LSU-CVM) admissions for all students applying to veterinary school for the classes of 2006 through 2008 from the top two agricultural programs in the state in terms of quantity of applicants to veterinary school: Louisiana State University and Louisiana Tech University. A one-way ANOVA was used to examine whether there were any statistical differences between known demographic and performance variables and acceptance into veterinary school. A logit forecasting model was then estimated to predict the likelihood of gaining acceptance into veterinary school based only on variables known early in the student's undergraduate career. Age, gender, and ACT scores were determined to be important variables in determining the likelihood of gaining admission. Overall, the forecasting model is of use in assigning probabilities of acceptance into veterinary school for specific student profiles, which can assist in one-on-one assistance from advisor to student.

Analysis of Variance↗

PubMed2005

Intergroup relations in soccer finals: people's forecasts of the duration of emotional reactions of in-group and out-group soccer fans.

The authors examined the hypothesis that people forecast a longer duration of uniquely human secondary emotions for their in-group than for an out-group. The authors conducted a field experiment in the setting of the European soccer championship. They asked Belgian participants to forecast the intensity with which their in-group Belgian fans or the out-group Turkish fans would experience various primary and secondary emotions in response to their team's victory or loss immediately after the Turkey-Belgium match and three days later. The results support the hypothesis. Moreover, and as the authors expected, they found no differences in the participants' forecasts of primary emotions. The authors discussed the implications of these findings for intergroup relations in general and for soccer fans' behavior in particular.

Adult↗