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PubMed2006

Land-use forecasting and hydrologic model integration for improved land-use decision support.

This paper develops a methodology for integrating a land-use forecasting model with an event scale, rainfall-runoff model in support of improving land-use policy formulation at the watershed scale. The models selected for integration are loosely coupled, structured upon a common GIS platform that facilitates data exchange. The hydrologic model HEC-HMS is calibrated for a specific storm event that occurred within central Washington State. The land-use forecasting model, What If? is implemented to forecast future spatial distributions of low-density residential land-uses under low and high population growth estimates. Forecasted land-use distribution patterns for the years 2015, 2025, and 2050 are then used as land-use data input for the calibrated hydrologic model, keeping all other parameters constant. Impacts to the stream discharge hydrograph are predicted as the study area becomes increasingly developed as forecasted by What If?. The initial results of this integration process demonstrate the synergy that can be generated through the linkage of the selected models. The ability to quantifiably forecast the potential hydrologic implications of proposed land-use policies before their implementation offers land-use decision-makers a valuable tool for discerning which proposed land-use alternatives will be effective at minimizing storm water runoff.

Conservation of Natural Resources↗

PubMed1999

Problems in the forecasting of solar particle events for manned missions.

Manned spacecraft will require a much improved ability to forecast solar particle events. The lead time required will depend on the use to which the forecast is put. Here we discuss problems of forecasting with the lead times of hours to weeks. Such forecasts are needed for scheduling and carrying out activities. Our present capabilities with these lead times is extremely limited. To improve our capability we must develop an ability to predict fast coronal mass ejections (CMEs). It is not sufficient to observe that a CME has already taken place since by that time it is already too late to make predictions with these lead times. Both to learn how to predict CMEs and to carry out forecasts on time scales of several days to weeks, observations of the other side of the Sun are required. We describe a low-cost space mission of this type that would further the development of an hours-to-weeks forecast capability.

Forecasting↗

PubMed2002

Forecasting malaria incidence from historical morbidity patterns in epidemic-prone areas of Ethiopia: a simple seasonal adjustment method performs best.

The aim of this study was to assess the accuracy of different methods of forecasting malaria incidence from historical morbidity patterns in areas with unstable transmission. We tested five methods using incidence data reported from health facilities in 20 areas in central and north-western Ethiopia. The accuracy of each method was determined by calculating errors resulting from the difference between observed incidence and corresponding forecasts obtained for prediction intervals of up to 12 months. Simple seasonal adjustment methods outperformed a statistically more advanced autoregressive integrated moving average method. In particular, a seasonal adjustment method that uses mean deviation of the last three observations from expected seasonal values consistently produced the best forecasts. Using 3 years' observation to generate forecasts with this method gave lower errors than shorter or longer periods. Incidence during the rainy months of June-August was the most predictable with this method. Forecasts for the normally dry months, particularly December-February, were less accurate. The study shows the limitations of forecasting incidence from historical morbidity patterns alone, and indicates the need for improved epidemic early warning by incorporating external predictors such as meteorological factors.

Disease Outbreaks↗

PubMed2000

Application of the Urban Airshed Model to forecasting next-day peak ozone concentrations in Atlanta, Georgia.

Twenty-four to forty-eight-hour ozone air quality forecasts are increasingly being used in metropolitan areas to inform the public about potentially harmful air quality conditions. The forecasts are also behind "ozone action day" programs in which the public and private sectors are encouraged or mandated to alter activities that contribute to the formation of ground-level ozone. Presented here is a low-cost application of the Urban Airshed Model (UAM), an Eulerian 3-dimensional photochemical-transport grid model for generating next-day peak ozone concentration forecasts. During the summer of 1997, next-day peak ozone concentrations in Atlanta, GA, were predicted both by a team of eight forecasters and by the Urban Airshed Model in Forecast Mode (UAM-FM). Results are presented that compare the accuracy of the team and the UAM-FM. The results for the summer of 1997 indicate that the UAM-FM may be a better predictor of peak ozone concentrations when concentrations are high (> 0.095 ppmv), and the team may be a better predictor of ozone concentrations when concentrations are low (< or = 0.095 ppmv). The UAM-FM is also discussed in the context of other forecasting tools, primarily linear regression models and a no-skill, persistence-based technique.

Forecasting↗

PubMed2000

A comparison of nonlinear regression and neural network models for ground-level ozone forecasting.

A hybrid nonlinear regression (NLR) model and a neural network (NN) model, each designed to forecast next-day maximum 1-hr average ground-level O3 concentrations in Louisville, KY, were compared for two O3 seasons--1998 and 1999. The model predictions were compared for the forecast mode, using forecasted meteorological data as input, and for the hindcast mode, using observed meteorological data as input. The two models performed nearly the same in the forecast mode. For the two seasons combined, the mean absolute forecast error was 12.5 ppb for the NLR model and 12.3 ppb for the NN model. The detection rate of 120 ppb threshold exceedances was 42% for each model in the forecast mode. In the hindcast mode, the NLR model performed marginally better than the NN model. The mean absolute hindcast error was 11.1 ppb for the NLR model and 12.9 ppb for the NN model. The hindcast detection rate was 92% for the NLR model and 75% for the NN model.

Forecasting↗

PubMed1999

The sensitivity and specificity of forecasting high-cost users of medical care.

OBJECTIVES: This study compares the ability of 3 risk-assessment models to distinguish high and low expense-risk status within a managed care population. Models are the Global Risk-Assessment Model (GRAM) developed at the Kaiser Permanente Center for Health Research; a logistic version of GRAM; and a prior-expense model. GRAM was originally developed for use in adjusting Medicare payments to health plans. METHODS: Our sample of 98,985 cases was drawn from random samples of memberships of 3 staff/group health plans. Risk factor data were from 1992 and expenses were measured for 1993. Models produced distributions of individual-level annual expense forecasts (or predicted probabilities of high expense-risk status for logistic) for comparison to actual values. Prespecified "high-cost" thresholds were set within each distribution to analyze the models' ability to distinguish high and low expense-risk status. Forecast stability was analyzed through bootstrapping. RESULTS: GRAM discriminates better overall than its comparators (although the models are similar for policy-relevant thresholds). All models forecast the highest-cost cases relatively well. GRAM forecasts high expense-risk status better than its comparators within chronic and serious disease categories that are amenable to early intervention but also generates relatively more false positives within these categories. CONCLUSIONS: This study demonstrates the potential of risk-assessment models to inform care management decisions by efficiently screening managed care populations for high expense-risk. Such models can act as preliminary screens for plans that can refine model forecasts with detailed surveys. Future research should involve multiple-year data sets to explore the temporal stability of forecasts.

Adolescent↗

PubMed2001

Forecast of the number of patients with end-stage renal disease in the United States to the year 2010.

As the United States end-stage renal disease (ESRD) program enters the new millennium, the continued growth of the ESRD population poses a challenge for policy makers, health care providers, and financial planners. To assist in future planning for the ESRD program, the growth of patient numbers and Medicare costs was forecasted to the year 2010 by modeling of historical data from 1982 through 1997. A stepwise autoregressive method and exponential smoothing models were used. The forecasting models for ESRD patient numbers demonstrated mean errors of -0.03 to 1.03%, relative to the observed values. The model for Medicare payments demonstrated -0.12% mean error. The R(2) values for the forecasting models ranged from 99.09 to 99.98%. On the basis of trends in patient numbers, this forecast projects average annual growth of the ESRD populations of approximately 4.1% for new patients, 6.4% for long-term ESRD patients, 7.1% for dialysis patients, 6.1% for patients with functioning transplants, and 8.2% for patients on waiting lists for transplants, as well as 7.7% for Medicare expenditures. The numbers of patients with ESRD in 2010 are forecasted to be 129,200 +/- 7742 (95% confidence limits) new patients, 651,330 +/- 15,874 long-term ESRD patients, 520,240 +/- 25,609 dialysis patients, 178,806 +/- 4349 patients with functioning transplants, and 95,550 +/- 5478 patients on waiting lists. The forecasted Medicare expenditures are projected to increase to $28.3 +/- 1.7 billion by 2010. These projections are subject to many factors that may alter the actual growth, compared with the historical patterns. They do, however, provide a basis for discussing the future growth of the ESRD program and how the ESRD community can meet the challenges ahead.

Forecasting↗

PubMedDate not supplied

U.S. physician workforce forecasting: a tale of two states.

Physician workforce forecasting in the U.S. has returned to preeminence in the recent past. With the publication of the federal Council on Graduate Medical Education's Sixteenth Report: Physician Workforce Policy Guidelines for the United States, 2000-2020, efforts have begun to examine specialty- and geography-specific forecasts to determine the extent to which the projected national shortage of physicians will affect particular specialties and populations in particular areas. This article begins with a brief history of physician workforce forecasting in the U.S. over the past 25 years and summarizes the findings of two state-specific attempts to forecast physician supply and demand in the coming years. Discussions of the findings from the two studies as well as a brief commentary on how particular model assumptions obfuscate interpretation of the forecasts are provided. Finally, a brief discussion of how the forecasts were used by stakeholders in each state is presented.

California↗

PubMed1992

Building a strategic forecasting system for hospital foodservice operations.

Forecasting, which involves the manipulation of operational data, is a critical task in foodservice management. We propose a strategic forecasting system for foodservice management, which may be expedited by using decision support systems. The most notable potential for improved forecast performance and decision flexibility may rest with increasing forecasting information. This article provides a framework for building a strategic forecasting system that can help managers select and use appropriate forecasting models for foodservice operations.

Costs and Cost Analysis↗

PubMed1990

Forecasting the number of AIDS cases: an analysis of two techniques.

Two methods used to forecast AIDS cases--trend analysis and back calculation--are examined in this paper. I forecast AIDS cases using trend analysis with data on the number of cases reported between January 1984 and March 1990. Forecasts using back calculation methods are based on the assumption that the progression rates from HIV to AIDS slowed in 1988 due to the use of AZT and aerosol pentamidine. With both methods, reasonable models yield widely different forecasts. Analysis also is hampered by a lack of information about the basic determinants of AIDS incidence, changes in the definition of AIDS, underreporting, and uncertainty about the effect of the use of prophylactic drugs. It is not possible to establish confidence limits around forecasts that take into account these sources of biases, and it is important that those who use AIDS forecasts be aware of these uncertainties.

Acquired Immunodeficiency Syndrome↗

PubMed1988

Use of spreadsheet software for short-range forecasting of pharmacy management data.

The use of a personal-computer spreadsheet program (Lotus 1-2-3) for forecasting pharmacy management data is described. Exponential smoothing is used in the forecasting model; historical data are used to predict subsequent values. Future performance is assumed to be more closely related to recent performance than to older performance data, so the model gives greater weight to more recent data. The smoothing weight controls the magnitude of error correction; a mean squared error table in the spreadsheet is used to determine the weighting. Two versions of the forecasting model are described. Version A, for a service or product for which data are expected to remain fairly stable, is used to predict values for the next time period. Version B, for data in which a substantial upward or downward trend exists, can be used to predict values for several time periods in the future. Version B differs from version A in that the forecast in version B is an estimate of the overall average of the data (weighted toward the most recent data) plus the estimated change per time period (weighted toward the most recent changes). Data from a university hospital for 1986-87 are used to illustrate the spreadsheet's tabular and graphic output; version A is used to predict the number of outpatient prescriptions for the next month, and data for the hospital's semiannual expenditures on i.v. solutions and sets are used to illustrate version B's forecast. Pharmacy managers can use these spreadsheet forecasts to quantify drug-use and personnel information for presentation to hospital administration.

Computer Simulation↗

PubMed1996

On the utility of population forecasts.

Many customers demand population forecasts, particularly for small areas. Although the forecast evaluation literature is extensive, it is dominated by a focus on accuracy. We go beyond accuracy by examining the concept of forecast utility in an evaluation of a sample of 2,709 counties and census tracts. We find that forecasters provide "value-added" knowledge for areas experiencing rapid change or areas with relatively large populations. For other areas, reduced value is more common than added value. Our results suggest that new forecasting strategies and methods such as composite modeling may substantially improve forecast utility.

Bias↗

PubMed2004

Forecasting spatially structured populations: the role of dispersal and scale.

We forecasted spatially structured population models with complex dynamics, focusing on the effect of dispersal and spatial scale on the predictive capability of nonlinear forecasting (NLF). Dispersal influences NLF ability by its influence on population dynamics. For simple 2-cell models, when dispersal is small, our ability to predict abundance in subpopulations decreased and then increased with increasing dispersal. Spatial heterogeneity, dispersal manner, and environmental noise did not qualitatively change this result. But results are not clear for complex spatial configurations because of complicated dispersal interactions across subpopulations. Populations undergoing periodic fluctuations could be forecasted perfectly for all deterministic cases that we studied, but less reliably when environmental noise was incorporated. More importantly, for all models that we have examined, NLF was much worse at larger spatial scales as a consequence of the asynchronous dynamics of subpopulations when the dispersal rate was below some critical value. The only difference among models was the critical value of dispersal rate, which varied with growth rate, carrying capacity, mode of dispersal, and spatial configuration. These results were robust even when environmental noise was incorporated. Intermittency, common in the dynamics of spatially structured populations, lowered the predictive capability of NLF. Forecasting population behaviour is of obvious value in resource exploitation and conservation. We suggest that forecasting at local scales holds promise, whereas forecasting abundance at regional scales may yield poor results. Improved understanding of dispersal can enhance the management and conservation of natural resources, and may help us to understand resource-exploitation strategies employed by local indigenous humans.

Animals↗

PubMedDate not supplied

Global forecast model to predict the daily dose of the solar erythemally effective UV radiation.

A worldwide forecast of the erythemally effective ultraviolet (UV) radiation is presented. The forecast was established to inform the public about the expected amount of erythemally effective UV radiation for the next day. Besides the irradiance, the daily dose is forecasted to enable people to choose the appropriate sun protection tools. Following the UV Index as the measure of global erythemally effective irradiance, the daily dose is expressed in units of UV Index hours. In this study, we have validated the model and the forecast against measurements from broadband UV radiometers of the Robertson-Berger type. The measurements were made at four continents ranging from the northern polar circle (67.4 degrees N) to the Antarctic coast (61.1 degrees S). As additional quality criteria the frequency of underestimation was taken into account because the forecast is a tool of radiation protection and made to avoid overexposure. A value closer than one minimal erythemal dose for the most sensitive skin type 1 to the observed value was counted as hit and greater deviations as underestimation or overestimation. The Austrian forecast model underestimates the daily dose in 3.7% of all cases, whereas 1.7% results from the model and 2.0% from the assumed total ozone content. The hit rate could be found in the order of 40%.

Humans↗

PubMed2006

Forecasting fluid milk and cheese demands for the next decade.

Predictions of future market demands and farm prices for dairy products are important determinants in developing marketing strategies and farm-production planning decisions. The objective of this report was to use current aggregate forecast data, combined with existing econometric models of demand and supply, to forecast retail demands for fluid milk and cheese and the supply and price of farm milk over the next decade. In doing so, we can investigate whether projections of population and consumer food-spending patterns will extend or alter current consumption trends and examine the implications of future generic advertising strategies for dairy products. To conduct the forecast simulations and appropriately allocate the farm milk supply to various uses, we used a partial equilibrium model of the US domestic dairy sector that segmented the industry into retail, wholesale, and farm markets. Model simulation results indicated that declines in retail per capita demand would persist but at a reduced rate from years past and that retail per capita demand for cheese would continue to grow and strengthen over the next decade. These predictions rely on expected changes in the size of populations of various ages, races, and ethnicities and on existing patterns of spending on food at home and away from home. The combined effect of these forecasted changes in demand levels was reflected in annualized growth in the total farm-milk supply that was similar to growth realized during the past few years. Although we expect nominal farm milk prices to increase over the next decade, we expect real prices (relative to assumed growth in feed costs) to remain relatively stable and show no increase until the end of the forecast period. Supplemental industry model simulations also suggested that net losses in producer revenues would result if only nominal levels of generic advertising spending were maintained in forthcoming years. In fact, if real generic advertising expenditures are increased relative to 2005 levels, returns to the investment in generic advertising can be improved. Specifically, each additional real dollar invested in generic advertising for fluid milk and cheese products over the forecast period would result in an additional 5.61 dollars in producer revenues.

Animals↗

PubMed1978

A multi-echelon menu item forecasting system for hospitals.

A multi-echelon system was designed to generate statistical forecasts of menu-item demand in hospitals from one- through twenty-eight-day intervals prior to patient meal service. The three interdependent echelons were: (1) Forecasting patient census, (2) estimating diet category census, and (3) calculating menu-item demand. Eighteen weeks of supper data were utilized to analyze diet category distribution patterns and menu-item preferences, to test forecasting models, and to evaluate the performance of the forecasting system. A cost function was used to evaluate the efficiency of the mathematical forecasting system and manual technique over a nine-week period. The cost of menu-item forecast errors resulting from the use of adaptive exponential smoothing and Box-Jenkins formulations was approximately 40 per cent less than costs associated with the manual system.

Computers↗

PubMed1990

Stochastic methods in population forecasting.

"This paper presents a stochastic version of the demographic cohort-component method of forecasting future population. In this model the sizes of future age-sex groups are non-linear functions of random future vital rates. An approximation to their joint distribution can be obtained using linear approximations or simulation. A stochastic formulation points to the need for new empirical work on both the autocorrelations and the cross-correlations of the vital rates. Problems of forecasting declining mortality and fluctuating fertility are contrasted. A volatility measure for fertility is presented. The model can be used to calculate approximate prediction intervals for births using data from deterministic cohort-component forecasts. The paper compares the use of expert opinion in mortality forecasting with simple extrapolation techniques to see how useful each approach has been in the past. Data from the United States suggest that expert opinion may have caused systematic bias in the forecasts."

Age Distribution↗

PubMed1992

Stochastic demographic forecasting.

"This paper describes a particular approach to stochastic population forecasting, which is implemented for the U.S.A. through 2065. Statistical time series methods are combined with demographic models to produce plausible long run forecasts of vital rates, with probability distributions. The resulting mortality forecasts imply gains in future life expectancy that are roughly twice as large as those forecast by the Office of the Social Security Actuary.... Resulting stochastic forecasts of the elderly population, elderly dependency ratios, and payroll tax rates for health, education and pensions are presented."

Adult↗