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PubMed1984

[Population forecasts in 1980, an analysis of mortality by cause of death].

The methods of projecting future mortality in official forecasts of population growth in the Netherlands are described. In particular, it is noted that the projected sex, age, and marital status specific death rates were not split up by causes of death. An analysis of mortality from 1980 to 1990 is presented, and it is shown that "the life expectancies calculated for extrapolated death rates split up by group of causes of death hardly differ from those where such a split up is not made. According to the figures presented...the differences never exceed 0.2 years. From these figures it cannot be concluded that splitting up the death rates by groups of causes of death in the national population forecasts would lead to significantly different results as far as mortality is concerned." (summary in ENG)

Cause of Death↗
PubMed1993

[Population forecast for Hungary, 1993-2020].

"The author discusses the results of [an] age-specific technical population forecast up to 2020, based on the latest available population and vital statistics [for Hungary]. The forecast starts from population figures as at 1 January 1993, that have been statistically updated from the 1990 census data, and it assumes that indicators of demographic trends (fertility, mortality) of 1992 will remain unchanged in the period between 1993 and 2020." (SUMMARY IN ENG AND RUS)

Age Factors↗
PubMed1997

Assumptions of the forecast of Poland's population by voivodships for the years 1995-2020.

"The paper briefly discusses the course of the basic demographic phenomena and processes in Poland, and the factors that determine the changes.... A detailed analysis [is provided] of the size and variability of fertility, mortality and migrations.... The method of calculating particular forecast components is presented, as well as results of calculations of the...number and structure of population and of the forecast of the vital statistics and migrations of population."

Demography↗
PubMed1989

Global surveillance and forecasting of AIDS.

The short-term forecasting of future AIDS cases has been attempted by statistical extrapolations of the observed curve of reported AIDS cases. In areas where such reporting is very incomplete or has only recently started, extrapolation is not possible and an epidemiologically-based forecasting model has been developed to estimate the annual number of AIDS cases which may have occurred and to project the annual number and distribution of AIDS cases for up to ten years. This model, which relies on the current understanding of the epidemiology and natural history of HIV infections and on the available HIV serologic survey data, is used to provide estimates and short-term projections of AIDS cases for the USA, Europe, Africa and the world. Because of the very long (mean of 8-9 years) incubation period between HIV infection and the development of AIDS, new cases over the next five years will be mostly derived from persons who became infected with HIV in or before 1987. WHO has estimated that 5-10 million persons worldwide were infected with HIV in 1987. Based on the lower estimate of 5 million, the cumulative number of AIDS cases which can be projected for the end of 1991 is over one million, and for the mid-to-late 1990s could reach 2 to 3 million. HIV/AIDS will therefore be an increasing public health problem throughout the world. Health care systems everywhere will have to be strengthened to respond to this large toll of disease and death due to AIDS.

Acquired Immunodeficiency Syndrome↗
PubMed1988

On forecasting mortality.

Official forecasts of mortality made by the U.S. Office of the Actuary throughout this century have consistently underestimated observed mortality declines. This is due, in part, to their reliance on the static extrapolation of past trends, an atheoretical statistical method that pays scant attention to the behavioral, medical, and social factors contributing to mortality change. A "multiple cause-delay model" more realistically portrays the effects on mortality of the presence of more favorable risk factors at the population level. Such revised assumptions produce large increases in forecasts of the size of the elderly population, and have a dramatic impact on related estimates of population morbidity, disability, and health care costs.

Actuarial Analysis↗
PubMed2001

Dynamic linear model and SARIMA: a comparison of their forecasting performance in epidemiology.

One goal of a public health surveillance system is to provide a reliable forecast of epidemiological time series. This paper describes a study that used data collected through a national public health surveillance system in the United States to evaluate and compare the performances of a seasonal autoregressive integrated moving average (SARIMA) and a dynamic linear model (DLM) for estimating case occurrence of two notifiable diseases. The comparison uses reported cases of malaria and hepatitis A from January 1980 to June 1995 for the United States. The residuals for both predictor models show that they were adequate tools for use in epidemiological surveillance. Qualitative aspects were considered for both models to improve the comparison of their usefulness in public health. Our comparison found that the two forecasting modelling techniques (SARIMA and DLM) are comparable when long historical data are available (at least 52 reporting periods). However, the DLM approach has some advantages, such as being more easily applied to different types of time series and not requiring a new cycle of identification and modelling when new data become available.

Communicable Disease Control↗
PubMed1991

Non-linear and linear forecasting of the EEG time series.

The method of non-linear forecasting of time series was applied to different simulated signals and EEG in order to check its ability of distinguishing chaotic from noisy time series. The goodness of prediction was estimated, in terms of the correlation coefficient between forecasted and real time series, for non-linear and autoregressive (AR) methods. For the EEG signal both methods gave similar results. It seems that the EEG signal, in spite of its chaotic character, is well described by the AR model.

Algorithms↗
PubMed1994

Forecasting the demand for inpatient services for specific chronic conditions.

While the proposed forecasting methodology has a well-established record in evaluating economic time-series, there is minimal, if any, use of this technique in projecting hospitalizations for specific chronic conditions. Using an established taxonomy of disease codes for alcoholism and alcohol abuse in a national inpatient database, a monthly time-series of hospitalizations was modeled. The model derived in both statistically adequate and accurate in forecasting future monthly demand for inpatient hospitalizations. This type of model specifications could be used by hospital planners and policy makers in evaluating monthly resources for specific chronic conditions.

Alcoholism↗
PubMed2005

The use of discriminant analysis and neural networks to forecast the severity of the Poaceae pollen season in a region with a typical Mediterranean climate.

Biological particles in the air such as pollen grains can cause environmental problems in the allergic population. Medical studies report that a prior knowledge of pollen season severity can be useful in the management of pollen-related diseases. The aim of this work was to forecast the severity of the Poaceae pollen season by using weather parameters prior to the pollen season. To carry out the study a historical database of 21 years of pollen and meteorological data was used. First, the years were grouped into classes by using cluster analysis. As a result of the grouping, the 21 years were divided into 3 classes according to their potential allergenic load. Pre-season meteorological variables were used, as well as a series of characteristics related to the pollen season. When considering pre-season meteorological variables, winter variables were separated from early spring variables due to the nature of the Mediterranean climate. Second, a neural network model as well as a discriminant linear analysis were built to forecast Poaceae pollen season severity, according to the three classes previously defined. The neural network yielded better results than linear models. In conclusion, neural network models could have a high applicability in the area of prevention, as the allergenic potential of a year can be determined with a high degree of reliability, based on a series of meteorological values accumulated prior to the pollen season.

Climate↗
PubMed2007

Overview and forecast on forestry productions worldwide.

Our world is largely dependent upon the forestry productions. Through the exploitation of forest reserves, we manufacture various industrial products, furniture, and obtain fuel and energy. Forestry productions should be conducted without large-scale deforestation and environmental degradation. In present study we perform a review and forecast analysis on forestry productions worldwide, with the objectives of providing an insight into the trend for several types of forestry productions in the future, and providing referential data for sustainable forestry productions and environmental management. Polynomial functions are used to fit trajectories of forestry productions since 1961 and forecasts during the coming 20 years are given in detail. If the past pattern continues, world fibreboard production would dramatically grow and reach 224,300,000 +/- 44,400,000 m(3) by the year 2020, an increase up to 240.7 to 408.9% as compared to the present level. Roundwood production of the world would change by -55.5 to 70.4% and reach 3,526,600,000 +/- 2,066,800,000 m(3) by 2020. In 2020 world production of sawlogs and veneer logs would change by -100 to 164.6% and reach 1,212,900,000 +/- 1,242,600,000 m(3). Global wood fuel production would change by -68.9 to 1.4% and reach 1,130,900,000 +/- 600,800,000 m(3) by 2020. Forestry productions in developed countries would largely surpass productions in developing countries in the near future. World forestry production grew since 1961 excluding wood fuel. Roundwood and wood fuel account for the critical proportions in the forestry productions. Wood fuel production has being declined and rapid growing of roundwood production has slowed in recent years. Widespread use of regenerative wood substitutes and worldwide afforestation against deforestation will be among the most effective ways to reduce deforestation and environment degradation associated with forestry productions.

Conservation of Natural Resources↗
PubMed1995

What is happening to the number of fatalities in road accidents? A model for forecasts and continuous monitoring of development up to the year 2000.

A model for successively forecasting and monitoring the development in the number of fatalities in traffic is presented. The model has been created through time series analysis covering the years 1977-1991. The model is simple, with the number of fatalities as the dependent variable and with time and traffic as the only predictors. The time factor describes the cumulative effect of changes such as better roads, vehicles, drivers, etc. The model is multiplicative and permits a nonproportional relationship with traffic volume. Taking into account the purely random fluctuations in the number of fatalities, the historical fit for the period 1977-1991 is very good. Also the forecasts for 1992 and 1993 have proved very accurate. The model will be revised as new annual data are received. At present, the model points to a favorable development in the reduction of the number of fatalities up to the year 2000, assuming a moderate increase in traffic.

Accidents, Traffic↗
PubMed1982

Future forecasting with LEAP.

A rapidly changing business environment has caused numerous firms to adopt some form of environmental assessment as part of their strategic planning process. Extrapolative techniques and trend analysis are useful when forecasting for the short-term and in comparatively stable environments. Futuristic methodologies are appropriate in turbulent environments with long-term planning requirements. The Likelihood of Events Assessment Process (LEAP), a new method of forecasting developed by the author, is explained in detail using examples from a recent study which used top level life insurance executives to predict the relative likelihood of occurrence of planning dates for a set of events in the socio-political environment of business.

Analysis of Variance↗
PubMed1985

Methodological issues and policy implications of physician manpower forecasting.

Future physician supply is a matter of concern in most industrialized countries due to its impact on cost and quality of health care. Consequently, forecasting the number of physicians becomes an important research issue from both methodological and policy perspectives. This article presents a simple forecast model developed for the State of Wisconsin (U.S.A.), using the complex federal model of the U.S. Graduate Medical Education National Advisory Committee as a backdrop. In this context, the article discusses the main determinants of physician supply, such as mobility, retention, ageing and productivity. The methodological issues in the simple modelling of such phenomena are examined using the Wisconsin case as an empirical example. Finally, policy implications of planning physician supply with respect to projected demand for services, changes in reimbursement patterns and productivity are discussed along with some of the recommendations actually made to the Wisconsin Legislature.

Forecasting↗
PubMed1985

Supply projections as planning: a critical review of forecasting net physician requirements in Canada.

This study involves a critical review of the current methods used to establish net future physician manpower requirements in Canada. The paper explores specific examples of physician manpower forecasting in Canada, and contrasts them with an extensive U.S. exercise completed recently. The conceptual and measurement difficulties inherent in the Canadian methodology are outlined. Particular attention is paid to the various factors that can potentially influence physician requirements but which are omitted from consideration in traditional forecasting. The paper concludes with a discussion of the definition of 'need' in the context of physician requirements.

Canada↗
PubMed2005

Forecasting of spinal cord injury annual case numbers in Australia.

OBJECTIVES: To forecast annual numbers of cases of spinal cord injury (SCI) and to assess the effect on case mix. DESIGN: Cohort of incident cases from 1986 to 1997, with forecasting to 2021. SETTING: Australian cases registered by treatment centers for the acute care and rehabilitation of SCI patients. PARTICIPANTS: A total of 2959 SCI patients, aged 15 years and over, identified through the Australian Spinal Cord Register. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Incidence and case mix. RESULTS: If the recent trends in SCI continue (ie, a 6.21% per annum rate increase in elderly men and a 2.8% per annum rate decrease in young men), the annual number of cases will increase from 253 in 1997 to 464 in 2021. In addition, the case mix would change substantially and the number and proportion of elderly persons with SCI would increase from 32 cases per annum in 1997 (13% of cases) to 233 per annum in 2021 (47% of cases). In addition, there would be a 143% increase in the number of cases of incomplete tetraplegia, from 88 cases per annum in 1997 to 214 cases per annum in 2021. Substantial increases would occur even if the age-specific rates of SCI were stable over the period. CONCLUSIONS: Population growth, and aging, plus increasing rates of SCI in the elderly will have profound effects on the expected number of SCI patients and their case mix. Treatment centers in Australia will need to plan for these changes. At the same time, there should be increased efforts to prevent SCI in order to ameliorate the problem.

Adolescent↗
PubMed2004

Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends.

Monitoring and forecasting of air quality parameters are popular and important topics of atmospheric and environmental research today due to the health impact caused by exposing to air pollutants existing in urban air. The accurate models for air pollutant prediction are needed because such models would allow forecasting and diagnosing potential compliance or non-compliance in both short- and long-term aspects. Artificial neural networks (ANN) are regarded as reliable and cost-effective method to achieve such tasks and have produced some promising results to date. Although ANN has addressed more attentions to environmental researchers, its inherent drawbacks, e.g., local minima, over-fitting training, poor generalization performance, determination of the appropriate network architecture, etc., impede the practical application of ANN. Support vector machine (SVM), a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented in this paper aims to examine the feasibility of applying SVM to predict air pollutant levels in advancing time series based on the monitored air pollutant database in Hong Kong downtown area. At the same time, the functional characteristics of SVM are investigated in the study. The experimental comparisons between the SVM model and the classical radial basis function (RBF) network demonstrate that the SVM is superior to the conventional RBF network in predicting air quality parameters with different time series and of better generalization performance than the RBF model.

Air Pollutants↗
PubMed2006

Modular learning models in forecasting natural phenomena.

Modular model is a particular type of committee machine and is comprised of a set of specialized (local) models each of which is responsible for a particular region of the input space, and may be trained on a subset of training set. Many algorithms for allocating such regions to local models typically do this in automatic fashion. In forecasting natural processes, however, domain experts want to bring in more knowledge into such allocation, and to have certain control over the choice of models. This paper presents a number of approaches to building modular models based on various types of splits of training set and combining the models' outputs (hard splits, statistically and deterministically driven soft combinations of models, 'fuzzy committees', etc.). An issue of including a domain expert into the modeling process is also discussed, and new algorithms in the class of model trees (piece-wise linear modular regression models) are presented. Comparison of the algorithms based on modular local modeling to the more traditional 'global' learning models on a number of benchmark tests and river flow forecasting problems shows their higher accuracy and transparency of the resulting models.

Artificial Intelligence↗