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PubMed2002

Bioclimatic indices as a tool in pollen forecasting.

The use of bioclimatic indices could be a major step forward in the methodology of pollen forecasting. The basis for this proposal is that simple meteorological parameters do not reflect the global status of the atmosphere, but merely some static measurements. However, pollen dispersal is, above all, a dynamic phenomenon, and this fact should be reflected in the variables we used to explain it. Here, we test the two methodologies for routine pollen forecasting by comparing correlation coefficients using the same daily Poaceae airborne pollen data base from León (6 years, from 1994 to 1999) as the dependent variable and either simple daily meteorological variables or compound daily bioclimatic indices as independent variables. Both simple and compound indices reproduced the same profile of evolution of plant eco-physiological requirements, as the length of the study period during the pollen season increased. However, for time frames larger than the main pollen period, bioclimatic indices gave superior coefficients, which seems to indicate that these could be more valuable for pre-season pollen forecasting. The continentality index produced the highest mean coefficient, higher than those generated by any meteorological variable. Furthermore, at least for a Mediterranean climate, site location and evapotranspiration in relation to precipitation seem to be the most promising factors for increasing success when forecasting Poaceae airborne pollen concentration.

Allergens↗

PubMed2003

Two statistical approaches to forecasting the start and duration of the pollen season of Ambrosia in the area of Lyon (France).

The aim of the present study was to forecast the start and duration of the pollen season of Ambrosia from meteorological data, in order to provide early information to allergists and allergic people. We used the airborne pollen data from Lyon (France), sampled using a Hirst trap from 1987 to 1999, and the meteorological data for the same period: air temperature (minimal, maximal, and average), rainfall, relative humidity, sunshine duration and soil temperature. Two forecasting models were used, one summing the temperatures and the other making use of a multiple regression on 10-day or monthly meteorological parameters. The start of the pollen season was predicted with both methods, results being more accurate with the regression (the errors between the predicted and the observed SDP ranging from 0 to 3 days). The duration of the pollen season was predicted by a regression model, errors ranging from 0 to 7 days. The models were later tested with satisfactory results from 2 additional years (2000 and 2001). Such forecasting models are helpful for allergic people, who have to begin their anti-allergic treatment before the start of the pollen season and not when the symptoms have appeared, since a preventive treatment is more efficient than a curative one. The regression allows predictions to be made 3-5 weeks in advance and so it is of particular interest. The forecasts will be broadcast on the Internet.

Ambrosia↗

PubMed2006

A long-term forecast analysis on worldwide land uses.

More and more lands worldwide are being cultivated for food production while forests are disappearing at an unprecedented rate. This paper aims to make a long-term forecast on land uses worldwide and provide the public, researchers, and government officials with a clear profile for land uses in the future. Data of land uses since 1961 were used to fit historical trajectories and make the forecast. The results show that trajectories of land areas can be well fitted with univariate linear regressions. The forecasts of land uses during the coming 25 years were given in detail. Areas of agricultural land, arable land, and permanent pasture land worldwide would increase by 6.6%, 7.2%, and 6.3% respectively in the year 2030 as compared to the current areas. Permanent crops land area all over the world is forecasted to increase 0.64% by 2030. By the year 2030 the areas of forests and woodland, nonarable and nonpermanent land worldwide would decrease by 2.4% and 0.9% against the current areas. All other land area in the world would dramatically decline by 6.4% by the year 2030. Overall the land area related to agriculture would tend to decrease in developed countries, industrialized countries, Europe, and North and Central America. The agriculture related land area would considerably increase in developing countries, least developed countries, low-income countries, Asia, Africa, South America, etc. Developing countries hold larger total land area than developed countries. Dramatic and continuous growth in agricultural land area of developing countries would largely contribute to the expected growth of world agricultural land area in the coming years. Population explosion, food shortage and poverty in the world, especially in developing countries, together caused the excessive cultivation of land for agricultural uses in the past years. Increasing agricultural land area exacerbates the climate changes and degradation of environment. How to limit the growth of human population is a key problem for reducing agricultural land expansion. Development and use of high-yielding and high-quality crop and animal varieties, diversification of human food sources, and technical and financial assistance to developing countries from developed countries, should also be implemented and strengthened in the future in order to slow down or even reverse the increase trend of agricultural land area. Sustainable agriculture is the effective way to stabilize the agricultural land area without food shortage. Through various techniques and measures, sustainable agriculture may meet the food production goals with minimum environmental risk. Public awareness and interest in sustainable agriculture will help realize and ease the increasing stress from agricultural land expansion.

Africa↗

PubMed1992

International health spending forecasts: concepts and evaluation.

Health care depends on the organizational and financial decisions which constituted each national system. Since those decisions were made at various times over the preceding years under different macroeconomic conditions, current expenditures are a distributed lag function of GDP growth and inflation rates. The accuracy of forecasts from such causal econometric models are compared to exponential smoothing, moving average, and ARIMA methods. Data fro 19 OECD countries 1965-79 are used for calibration, and then ex ante forecasts are generated for 1980-87 so that actual forecast accuracy can be tested. The greatest reduction in mean absolute error was obtained with the econometric model estimated in aggregate across all 19 countries, although single-country models, exponential smoothing and international averaging were also effective. A combination of all four forecasts was more accurate than any one alone, reducing MAE by 25% relative to a constant growth projection.

Delivery of Health Care↗

PubMed2005

Forecasting models to quantify three anthropogenic stresses on coral reefs from marine recreation: anchor damage, diver contact and copper emission from antifouling paint.

This research focuses on damage to coral reefs from three anthropogenic stresses: the dropping of anchors and their chains, human contact, and emission of copper from antifouling paints. Forecasting models are described that quantify degradation in terms of percentage of coral cover damaged/year or increasing levels of water toxicity/year. The models utilize a Monte Carlo simulation that applies a range of values or a probability distribution to each of the numerous uncertain variables. This model has the flexibility to adapt, and become more accurate, when users input assumptions specific to their diving sites. Given our specific assumptions for a frequently visited site, anchors and their chains forecast a distribution of coral reef cover damage with a mean of 7.11%+/-4.77%, diver contact forecast a distribution of coral reef cover damage with a mean of 0.67%+/-0.38%, and antifouling paint forecast a distribution of copper level increase in the water with a mean of 0.037+/-0.014ppb. The results support recommendations for the implementation and sustained use of several specific marine recreation practices.

Animals↗

PubMed2006

Ensemble forecasting of species distributions.

Concern over implications of climate change for biodiversity has led to the use of bioclimatic models to forecast the range shifts of species under future climate-change scenarios. Recent studies have demonstrated that projections by alternative models can be so variable as to compromise their usefulness for guiding policy decisions. Here, we advocate the use of multiple models within an ensemble forecasting framework and describe alternative approaches to the analysis of bioclimatic ensembles, including bounding box, consensus and probabilistic techniques. We argue that, although improved accuracy can be delivered through the traditional tasks of trying to build better models with improved data, more robust forecasts can also be achieved if ensemble forecasts are produced and analysed appropriately.

Ecosystem↗

PubMed2002

Statistical analysis of environmental data as the basis of forecasting: an air quality application.

A statistical analysis technique is used for the development of an environmental forecasting tool. More specifically, a stochastic autoregressive integrated moving average (ARIMA) model is developed for maximum ozone concentration forecasts in Athens, Greece. For this purpose, the Box-Jenkins approach is applied for the analysis of a 9-year air quality observation record. The model developed is checked against real data for 1 year. Results show a good index of agreement, accompanied by a weakness in forecasting alarms. Finally, suggestions are made regarding the enrichment of the approach used in order to improve the forecasting performance.

Air Pollutants↗

PubMed2000

Forecasting disease risk for increased epidemic preparedness in public health.

Emerging infectious diseases pose a growing threat to human populations. Many of the world's epidemic diseases (particularly those transmitted by intermediate hosts) are known to be highly sensitive to long-term changes in climate and short-term fluctuations in the weather. The application of environmental data to the study of disease offers the capability to demonstrate vector-environment relationships and potentially forecast the risk of disease outbreaks or epidemics. Accurate disease forecasting models would markedly improve epidemic prevention and control capabilities. This chapter examines the potential for epidemic forecasting and discusses the issues associated with the development of global networks for surveillance and prediction. Existing global systems for epidemic preparedness focus on disease surveillance using either expert knowledge or statistical modelling of disease activity and thresholds to identify times and areas of risk. Predictive health information systems would use monitored environmental variables, linked to a disease system, to be observed and provide prior information of outbreaks. The components and varieties of forecasting systems are discussed with selected examples, along with issues relating to further development.

Communicable Diseases↗

PubMed1994

Forecasting the demand for maternity services.

Faced with demands on maternity services exceeding design capacity, one British Columbia hospital commissioned forecasting studies to determine trends in demand and if accurate forecasts could be obtained. In addition to describing the forecasting method employed, the data used and the results, the authors look at what literature is available on obstetrics forecasting.

Birth Rate↗

PubMed2003

From self-prediction to self-defeat: behavioral forecasting, self-fulfilling prophecies, and the effect of competitive expectations.

Four studies explored behavioral forecasting and the effect of competitive expectations in the context of negotiations. Study 1 examined negotiators' forecasts of how they would behave when faced with a very competitive versus a less competitive opponent and found that negotiators believed they would become more competitive. Studies 2 and 3 examined actual behaviors during a negotiation and found that negotiators who expected a very competitive opponent actually became less competitive, as evidenced by setting lower, less aggressive reservation prices, making less demanding counteroffers, and ultimately agreeing to lower negotiated outcomes. Finally, Study 4 provided a direct test of the disconnection between negotiators' forecasts for their behavior and their actual behaviors within the same sample and found systematic errors in behavioral forecasting as well as evidence for the self-fulfilling effects of possessing a competitive expectation.

Adult↗

PubMed2002

Evaluation of artificial neural networks for fine particulate pollution (PM10 and PM2.5) forecasting.

Multi-layer perceptron (MLP) artificial neural network (ANN) models are compared with traditional multiple regression (MLR) models for daily maximum and average O3 and particulate matter (PM10 and PM2.5) forecasting. MLP particulate forecasting models show little if any improvement over MLR models and exhibit less skill than do O3 forecasting models. Meteorological variables (precipitation, wind, and temperature), persistence, and co-pollutant data are shown to be useful PM predictors. If MLP approaches are adopted for PM forecasting, training methods that improve extreme value prediction are recommended.

Air Pollutants↗

PubMed1988

An assessment of the use of Bayes' Theorem for forecasting in public health: the case of epidemic meningitis in China.

A mathematical model based upon Bayes' Theorem (BT) was used to forecast the occurrence of epidemic cerebrospinal meningitis (ECM) in ten communities in North China. Reports of ECM from each ten-day period during the meningitis season and records of special population movement during 1960-82 were analysed to establish forecast models. Calibration, split-sample, random-sample selection, as well as actual forecast tests, were used to check the efficiency of the models. For all the tests, the theoretical occurrence of ECM forecast by the BT method was compared with the observational data. Since the BT method offers efficiency and convenience, it is recommended for use in planning for the prevention and control of ECM in China.

Bayes Theorem↗

PubMed1981

An evaluation of influenza mortality surveillance, 1962-1979. I. Time series forecasts of expected pneumonia and influenza deaths.

Proposed in this paper is a new method of forecasting the expected number of pneumonia and influenza deaths based on a time series analysis of the historical mortality data. Currently, the method for forecasting the expected pneumonia and influenza deaths used by the Center for Disease Control is based on regression analysis. These forecasts are used to estimate the excess deaths attributable to pneumonia and influenza. Careful comparative analysis demonstrates that the proposed method provides a more accurate forecast of pneumonia and influenza mortality than the existing method.

Adolescent↗

PubMed2002

Social networks and forecasting the spread of HIV infection.

This study is an initial effort to use network data to forecast the spread of HIV in a large U.S. city. Data were collected from a sample of drug users and sociodemographically matched nonusers in low-income areas of Houston, Texas. Two sample-based HIV prevalence models and two sociological models were combined with three published biological models to yield forecasts of the growth of HIV seroprevalence. The forecasts predict a compounded annual growth in HIV of between 2.4% and 16.5% among low-income residents of Houston's inner city. These results suggest that forecasts are most sensitive to the nature of the sociological model used. A random mixing model showed about a threefold overestimate of 20-year projected seroprevalence compared with the empiric network data. Thus, the collection of additional social network data is probably the most important requirement for more accurate projections.

Adult↗

PubMed2006

Ratio-based lengths of intervals to improve fuzzy time series forecasting.

The objective of this study is to explore ways of determining the useful lengths of intervals in fuzzy time series. It is suggested that ratios, instead of equal lengths of intervals, can more properly represent the intervals among observations. Ratio-based lengths of intervals are, therefore, proposed to improve fuzzy time series forecasting. Algebraic growth data, such as enrollments and the stock index, and exponential growth data, such as inventory demand, are chosen as the forecasting targets, before forecasting based on the various lengths of intervals is performed. Furthermore, sensitivity analyses are also carried out for various percentiles. The ratio-based lengths of intervals are found to outperform the effective lengths of intervals, as well as the arbitrary ones in regard to the different statistical measures. The empirical analysis suggests that the ratio-based lengths of intervals can also be used to improve fuzzy time series forecasting.

Biometry↗

PubMed2002

An approach to forecasting health expenditures, with application to the U.S. Medicare system.

OBJECTIVE: To quantify uncertainty in forecasts of health expenditures. STUDY DESIGN: Stochastic time series models are estimated for historical variations in fertility, mortality, and health spending per capita in the United States, and used to generate stochastic simulations of the growth of Medicare expenditures. Individual health spending is modeled to depend on the number of years until death. DATA SOURCES/STUDY SETTING: A simple accounting model is developed for forecasting health expenditures, using the U.S. Medicare system as an example. PRINCIPAL FINDINGS: Medicare expenditures are projected to rise from 2.2 percent of GDP (gross domestic product) to about 8 percent of GDP by 2075. This increase is due in equal measure to increasing health spending per beneficiary and to population aging. The traditional projection method constructs high, medium, and low scenarios to assess uncertainty, an approach that has many problems. Using stochastic forecasting, we find a 95 percent probability that Medicare spending in 2075 will fall between 4 percent and 18 percent of GDP, indicating a wide band of uncertainty. Although there is substantial uncertainty about future mortality decline, it contributed little to uncertainty about future Medicare spending, since lower mortality both raises the number of elderly, tending to raise spending, and is associated with improved health of the elderly, tending to reduce spending. Uncertainty about fertility, by contrast, leads to great uncertainty about the future size of the labor force, and therefore adds importantly to uncertainty about the health-share of GDP. In the shorter term, the major source of uncertainty is health spending per capita. CONCLUSIONS: History is a valuable guide for quantifying our uncertainty about future health expenditures. The probabilistic model we present has several advantages over the high-low scenario approach to forecasting. It indicates great uncertainty about future Medicare expenditures relative to GDP.

Accounting↗

PubMed2004

Forecasting the number of inpatients with schizophrenia.

There has been much discussion in Japan regarding the reduction of psychiatric beds. For effective healthcare planning, reliable forecasting is important. The purpose of this study was to predict the number of future schizophrenic inpatients using quantitative methodology. Data was obtained from a survey of schizophrenic inpatients conducted annually at the end of March by the Niigata Prefecture from 1974 to 2003. The numbers of schizophrenic inpatients in different age groups over a long period of time were used in a precise time-series analysis to establish trends. Then these past trends were used to forecast inpatient numbers for future years. The pattern of ascents and declines of each inpatient group stratified by age appeared to be duplicated by the next older age group 10 years later. The numbers of inpatients with schizophrenia in 2013 and 2023 are projected to be 78.5% and 56.7% of the number of patients in 2003, respectively. By 2033, the number is forecast to decline to 41.0% of the number in 2003. This study forecasts that inpatients with schizophrenia will decrease substantially over the next several decades. Policy should be designed to reflect this trend.

Adolescent↗

PubMed2001

Ecological forecasts: an emerging imperative.

Planning and decision-making can be improved by access to reliable forecasts of ecosystem state, ecosystem services, and natural capital. Availability of new data sets, together with progress in computation and statistics, will increase our ability to forecast ecosystem change. An agenda that would lead toward a capacity to produce, evaluate, and communicate forecasts of critical ecosystem services requires a process that engages scientists and decision-makers. Interdisciplinary linkages are necessary because of the climate and societal controls on ecosystems, the feedbacks involving social change, and the decision-making relevance of forecasts.

Agriculture↗