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PubMed1985

Uncertain population forecasting.

"Errors in population forecasts arise from errors in the jump-off population and errors in the predictions of future vital rates. The propagation of these errors through the linear (Leslie) growth model is studied, and prediction intervals for future population are developed. For U.S. national forecasts, the prediction intervals are compared with the U.S. Census Bureau's high-low intervals." In order to assess the accuracy of the predictions of vital rates, the authors "derive the predictions from a parametric statistical model and estimate the extent of model misspecification and errors in parameter estimates. Subjective, expert opinion, so important in real forecasting, is incorporated with the technique of mixed estimation. A robust regression model is used to assess the effects of model misspecification."

Americas↗

PubMed1987

Tests of forecast accuracy and bias for county population projections.

"This article deals with the forecast accuracy and bias of population projections for 2,971 counties in the United States. It uses three different projection techniques and data from 1950, 1960, 1970, and 1980 to make two sets of 10-year projections and one set of 20-year projections. These projections are compared with census counts to determine forecast errors. The size, direction, and distribution of forecast errors are analyzed by size of place, rate of growth, and length of projection horizon. A number of consistent patterns are noted, and an extension of the empirical results to the production of confidence intervals for population projections is considered." A comment by Paul M. Beaumont and Andrew M. Isserman is included (pp. 1,004-9) together with a rejoinder by the author (pp. 1,009-12). This is a revised version of a paper presented at the 1986 Annual Meeting of the Population Association of America (see Population Index, Vol. 52, No. 3, Fall 1986, p. 456).

Americas↗

PubMed1995

Population forecasting: do simple models outperform complex models?

"This paper reviews the growing literature on population forecasting to examine a curious paradox: despite continuing refinements in the specification of models used to represent population dynamics, simple exponential growth models, it is claimed, continue to outperform such more complex models in forecasting exercises. Shrinking a large complex model in order to simplify it typically involves two processes: aggregation and decomposition. Both processes are known to introduce biases into the resulting representations of population dynamics. Thus it is difficult to accept the conclusion that simple models outperform complex models. Moreover, assessments of forecasting performance are notoriously difficult to carry out, because they inevitably depend not only on the models used but also on the particular historical periods selected for examination.... This paper reviews some of the recent debate on the simple versus complex modeling issue and links it to the questions of model bias and distributional momentum impacts." (SUMMARY IN FRE)

Demography↗

PubMed1991

[Population forecasts for the Netherlands during the 1980s: how far were they wrong?].

An evaluation of the accuracy of the seven national population forecasts produced by the Netherlands Central Bureau of Statistics during the 1980s is presented. "Compared with the eight national forecasts made in the preceding three decades those of the 1980s were more successful. The number of live births in particular was forecasted better in the last decade. Regarding fertility of subsequent generations of women it was rightly assumed that: the numbers of children would decrease, [and] the ages at which women have their children would increase. Other assumptions made during the 1980s that turned out to be right were: increasing life expectancy at birth with a stable difference of about 6.5 years between the expectations for men and those for women, [and] continuing net immigration into the Netherlands." (SUMMARY IN ENG)

Age Factors↗

PubMed1987

Measuring the accuracy of population forecasts.

The accuracy of population projections is discussed, with specific reference to population forecasts for development planning purposes. The author notes that errors in making population forecasts depend on the parameter values used in the calculations, and that such errors can be avoided if the demographic models used are seen not as stochastic but as models with random parameters. "For such models the present article derives estimations of the covariance matrices of forecasts of the social and age composition of the population."

Age Distribution↗

PubMed1991

[Uncertainty variants in population forecasts for the Netherlands].

"Because of the uncertainty of population forecasts the Netherlands Central Bureau of Statistics publishes a Low and a High variant next to the Medium variant.... Variants are obtained by using a deterministic model. Hence the probability that the interval between the variants [covers] the true future values is unknown. Under reasonable assumptions a statistical confidence interval for total population size can be derived. The basic assumption is that the forecast errors of population growth are serially correlated. If a first-order autoregressive model is estimated on the basis of all population forecasts published...since 1950, it turns out that the interval between the Low and High variants corresponds reasonably close to a two-thirds confidence interval in the next two decades." (SUMMARY IN ENG)

Developed Countries↗

PubMedDate not supplied

A forecasting approach to accelerate drug development.

The clinical phase of drug development should be concluded sooner and at a lower cost if primarily only the pivotal and supportive studies were to be conducted. Such improved efficiency requires development of a decision support system that delivers five new capabilities: (i) it enables one to predict a result of a clinical study and to identify those studies that are expected to have an acceptable probability of success; (ii) it will allow one to optimally utilize available pharmacokinetic and pharmacodynamic (PK/PD) data and improve its predictive capability as more data become available; (iii) it will enable one to project useful population results, not just mean results; (iv) predictions will be accompanied by a measure of reliability; and (v) expected initial clinical results will be predictable from animal and related drug class data. With such a tool population targets could be specified very early in the drug development programme, challenged, and then rationally revised at each step during the development process. This report describes progress in developing and testing a clinical trials Forecaster, a prototype for such a system. The Forecaster generates estimates of the joint density for a population of combined PK/PD parameters. That population then serves as a surrogate for the population of individuals. When the resulting joint density is sampled, the obtained sets of parameters may be used to generate data that is statistically indistinguishable from the original experimental data. Such simulated data can be used to validate assumptions, and make inferences on specified population targets that are accompanied by a measure of prediction reliability. We demonstrate use of the forecaster by employing N = 22 PK/PD parameter sets for an orally administered analgesic.

Bias↗

PubMed1992

Forecast accuracy of Australian subnational population projections.

The authors assess demographic projections made over the last 20 years for Australia. "This paper first considers the role of accuracy amongst other objectives of projection activity. Accepting accuracy as a legitimate goal, we then assess the performance of 48 sets of population projections and forecasts for states and territories of Australia prepared since 1970. Projection accuracy is assessed by reference to length of forecast horizon, population size and rate of growth. We also examine the main sources of forecast error in selected projections for each state and compare the performance of past projections with alternatives based on simple extrapolation of contemporary population trends."

Australia↗

PubMed1996

Forecasting the AIDS epidemic in Puerto Rico.

The purpose of this study is to model and predict the diffusion of the AIDS epidemic in Puerto Rico. Specifically we aim at identifying primary influences in the geographical distribution of the population affected with AIDS to produce a reasonable projection of the time and space paths that will be followed by the epidemic. The study is based on AIDS incidence data from 1982 through 1992. The epidemic is analyzed statistically through multivariate regression and the potential model to produce a probability surface in which risk behavior and intra-municipality mobility are significant risk factors. The growth forecast is produced using the logistic function fitted to the past growth using an iterative non-linear optimization method. A simulation technique is then employed to forecast the spatial development of the epidemic from 1993 through 1997. The results indicate a clear hierarchical tendency at the beginning of the epidemic, later a wave-like diffusion pattern is also observed. While the absolute number of new cases is expected to remain higher in the more urbanized areas, the relative growth of AIDS cases is likely to become much higher in the rural municipalities. The forecasting procedure employed here is applicable to populations with diverse epidemiological profiles.

Acquired Immunodeficiency Syndrome↗

PubMed2004

Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong.

The forecasting of air pollutant trends has received much attention in recent years. It is an important and popular topic in environmental science, as concerns have been raised about the health impacts caused by unacceptable ambient air pollutant levels. Of greatest concern are metropolitan cities like Hong Kong. In Hong Kong, respirable suspended particulates (RSP), nitrogen oxides (NOx), and nitrogen dioxide (NO2) are major air pollutants due to the dominant usage of diesel fuel by commercial vehicles and buses. Hence, the study of the influence and the trends relating to these pollutants is extremely significant to the public health and the image of the city. The use of neural network techniques to predict trends relating to air pollutants is regarded as a reliable and cost-effective method for the task of prediction. The works reported here involve developing an improved neural network model that combines both the principal component analysis technique and the radial basis function network and forecasts pollutant tendencies based on a recorded database. Compared with general neural network models, the proposed model features a more simple network architecture, a faster training speed, and a more satisfactory prediction performance. The improved model was evaluated with hourly time series of RSP, NOx and NO2 concentrations monitored at the Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000 and proved to be effective. The model developed is a potential tool for forecasting air quality parameters and is superior to traditional neural network methods.

Air Pollutants↗

PubMed2006

Validation of a 20-year forecast of US childhood lead poisoning: Updated prospects for 2010.

We forecast childhood lead poisoning and residential lead paint hazard prevalence for 1990-2010, based on a previously unvalidated model that combines national blood lead data with three different housing data sets. The housing data sets, which describe trends in housing demolition, rehabilitation, window replacement, and lead paint, are the American Housing Survey, the Residential Energy Consumption Survey, and the National Lead Paint Survey. Blood lead data are principally from the National Health and Nutrition Examination Survey. New data now make it possible to validate the midpoint of the forecast time period. For the year 2000, the model predicted 23.3 million pre-1960 housing units with lead paint hazards, compared to an empirical HUD estimate of 20.6 million units. Further, the model predicted 498,000 children with elevated blood lead levels (EBL) in 2000, compared to a CDC empirical estimate of 434,000. The model predictions were well within 95% confidence intervals of empirical estimates for both residential lead paint hazard and blood lead outcome measures. The model shows that window replacement explains a large part of the dramatic reduction in lead poisoning that occurred from 1990 to 2000. Here, the construction of the model is described and updated through 2010 using new data. Further declines in childhood lead poisoning are achievable, but the goal of eliminating children's blood lead levels > or =10 microg/dL by 2010 is unlikely to be achieved without additional action. A window replacement policy will yield multiple benefits of lead poisoning prevention, increased home energy efficiency, decreased power plant emissions, improved housing affordability, and other previously unrecognized benefits. Finally, combining housing and health data could be applied to forecasting other housing-related diseases and injuries.

Child, Preschool↗

PubMed2006

Cellular automata-based forecasting of the impact of accidental fire and toxic dispersion in process industries.

The strategies to prevent accidents from occurring in a process industry, or to minimize the harm if an accident does take place, always revolve around forecasting the likely accidents and their impacts. Based on the likely frequency and severity of the accidents, resources are committed towards preventing the accidents. Nearly all techniques of ranking hazardous units, be it the hazard and operability studies, fault tree analysis, hazard indice, etc.--qualitative as well as quantitative--depend essentially on the assessment of the likely frequency and the likely harm accidents in different units may cause. This fact makes it exceedingly important that the forecasting the accidents and their likely impact is done as accurately as possible. In the present study we introduce a new approach to accident forecasting based on the discrete modeling paradigm of cellular automata. In this treatment an accident is modeled as a self-evolving phenomena, the impact of which is strongly influenced by the size, nature, and position of the environmental components which lie in the vicinity of the accident site. The outward propagation of the mass, energy and momentum from the accident epicenter is modeled as a fast diffusion process occurring in discrete space-time coordinates. The quantum of energy and material that would flow into each discrete space element (cell) due to the accidental release is evaluated and the degree of vulnerability posed to the receptors if present in the cell is measured at the end of each time element. This approach is able to effectively take into account the modifications in the flux of energy and material which occur as a result of the heterogeneous environment prevailing between the accident epicenter and the receptor. Consequently, more realistic accident scenarios are generated than possible with the prevailing techniques. The efficacy of the approach has been illustrated with case studies.

Accident Prevention↗

PubMed2005

A research model--forecasting incident rates from optimized safety program intervention strategies.

UNLABELLED: INTRODUCTION/PROBLEM: Property damage incidents, workplace injuries, and safety programs designed to prevent them, are expensive aspects of doing business in contemporary industry. The National Safety Council (2002) estimated that workplace injuries cost $146.6 billion per year. Because companies are resource limited, optimizing intervention strategies to decrease incidents with less costly programs can contribute to improved productivity. METHOD: Systematic data collection methods were employed and the forecasting ability of a time-lag relationship between interventions and incident rates was studied using various statistical methods (an intervention is not expected to have an immediate nor infinitely lasting effect on the incident rate). RESULTS/SUMMARY: As a follow up to the initial work, researchers developed two models designed to forecast incident rates. One is based on past incident rate performance and the other on the configuration and level of effort applied to the safety and health program. Researchers compared actual incident performance to the prediction capability of each model over 18 months in the forestry operations at an electricity distribution company and found the models to allow accurate prediction of incident rates. IMPACT ON INDUSTRY: These models potentially have powerful implications as a business-planning tool for human resource allocation and for designing an optimized safety and health intervention program to minimize incidents. Depending on the mathematical relationship, one can determine what interventions, where and how much to apply them, and when to increase or reduce human resource input as determined by the forecasted performance.

Accidents, Occupational↗

PubMed2004

Waiting time information services: an evaluation of how well clearance time statistics can forecast a patient's wait.

Governments in some countries have created web-based information services so that patients requiring elective surgery can compare the waiting times of surgical units. This study investigated how accurately the waiting times of patients about to join a waiting list can be forecast by various clearance time statistics. It used 3 years of elective surgical activity data that covered 46 surgeons in 10 specialties within a public hospital. Six clearance time functions were tested, and the best function was compared with average waiting time statistics derived from census and throughput data. The forecast accuracy of the clearance time functions was found to be greatly affected by the characteristics and behaviour of a surgeon's waiting list. Although there was less difference in performance among the six functions, systematic differences between them were also found. The best of these performed better than the statistics derived from waiting time data, especially where waiting times exceeded 6 months. Yet, its accuracy was still poor. For each surgeon with an average waiting time of more than 6 months, at least 20% of patients waited more than 90 days beyond the clearance time forecast. Consequently, while waiting time information services should consider adopting the clearance time approach, they need to be explicit about its statistical limitations.

Elective Surgical Procedures↗

PubMed2004

Forecasting aphid outbreaks and epidemics of Cucumber mosaic virus in lupin crops in a Mediterranean-type environment.

Cucumber mosaic virus (CMV) causes a serious disease of narrow-leafed lupin (Lupinus angustifolius). It is seed-borne in lupin and seed-infected plants act as the primary virus source for secondary spread by aphid vectors within crops. Infection with CMV causes yield losses of up to 60% in epidemic years, but has little impact on yield in years when spread is limited. Aphids also cause sporadic yield losses due to direct feeding damage. A simulation model was developed to forecast aphid outbreaks and epidemics of CMV in lupin crops growing in the 'grainbelt' of south-west Australia, which has a Mediterranean-type climate. The model uses rainfall during summer and early autumn to calculate an index of aphid build-up on weeds, crop volunteers and self-regenerating annual pastures in each 'grainbelt' locality before the growing season commences in late autumn. The index is used to forecast the timing of aphid immigration into crops. The subsequent aphid build-up and movement within the crop, spread of CMV from virus-infected source plants within the crop, yield losses and percentage of harvested seed-infected are then calculated. The model evaluates the effects of different sowing dates, percentages of CMV infection in seed sown and plant population densities on virus spread. The model simulations were validated with 14 years' field data from six different sites in the 'grainbelt', representing a wide range of pre-growing season rainfall scenarios, sowing dates, percentages of infection in seed sown and plant population densities. The model was incorporated into a decision support system (DSS) for use by lupin farmers and agricultural consultants in planning CMV management and targeting sprays against aphids to prevent direct feeding damage. The inputs required from the user are lupin cultivar, anticipated emergence date, percentage CMV infection in seed sown, plant density and location. The output consists of a personalised risk forecast for the user and includes predictions for date of first aphid arrival, aphid numbers, CMV spread, final virus incidence, yield loss due to infection and percentage infection in harvested seed. Predictions from the DSS are accessible via an Internet site and from other information sources. The model can serve as a template for modelling similar virus/aphid vector pathosystems in other regions of the world, especially those with Mediterranean-type climates.

Animals↗

PubMed1998

Modeling and forecasting monthly patient volume at a primary health care clinic using univariate time-series analysis.

Two univariate time-series analysis methods have been used to model and forecast the monthly patient volume at the family and community medicine primary health care clinic of King Faisal University, Al-Khobar, Saudi Arabia. Models were based on nine years of data and forecasts made for 2 years. The optimum ARIMA model selected is an autoregressive model of the fourth order operating on the data after differencing twice at the nonseasonal level and once at the seasonal level. It gives mean and maximum absolute percentage errors of 1.86 and 4.23%, respectively, over the forecasting interval. A much simpler method based on extrapolating the growth curve of the annual means of the patient volume using a polynomial fit gives the better figures of 0.55 and 1.17%, respectively. This is due to the fairly regular nature of the data and the lack of strong random components that require ARIMA processes for modeling.

Ambulatory Care↗

PubMed1998

Time series forecasts of ambulance run volume.

To test the hypothesis that time series analysis can provide accurate predictions of future ambulance service run volume, a prospective stochastic time series modeling study was conducted at a community-based regional ambulance service. For all requests for ambulance transport during two sequential years, the time and date, total run time, and acuity code of the run were recorded in a computer database. Time series variables were formed for ambulance service runs per hour, total run time, and acuity. Prediction models were developed from one complete year's data (1994) and included four model 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 24 weeks of the subsequent year (1995). Each model's adequacy was tested on residuals by autocorrelation functions, integrated periodograms, linear regression, and differences among the variances. A total of 68,433 patients were seen in 1994 and 32,783 in the first 24 weeks of 1995. Large periodic variations in run volume with time of day were found (P < .001). A model based on arithmetic means of each hour of the week with 3-point moving average smoothing yielded the most accurate forecasts and explained 54.3% of the variation observed in the 1995 test series (P < .001). Time series analysis can provide powerful, accurate short-range forecasts of future ambulance service run volume. Simpler, less expensive models performed best in this study.

Ambulances↗

PubMed2002

An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight elimination.

Sales forecasting plays a very prominent role in business strategy. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average (ARMA). However, sales forecasting is very complicated owing to influence by internal and external environments. Recently, artificial neural networks (ANNs) have also been applied in sales forecasting since their promising performances in the areas of control and pattern recognition. However, further improvement is still necessary since unique circumstances, e.g. promotion, cause a sudden change in the sales pattern. Thus, this study utilizes a proposed fuzzy neural network (FNN), which is able to eliminate the unimportant weights, for the sake of learning fuzzy IF-THEN rules obtained from the marketing experts with respect to promotion. The result from FNN is further integrated with the time series data through an ANN. Both the simulated and real-world problem results show that FNN with weight elimination can have lower training error compared with the regular FNN. Besides, real-world problem results also indicate that the proposed estimation system outperforms the conventional statistical method and single ANN in accuracy.

Algorithms↗