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Planning and development decisions in both the government and business sectors often require small area population forecasts. Unfortunately, current methods often produce forecasts that are inaccurate, particularly for remote areas and those with smaller populations. Such inaccuracy necessitates the development and evaluation of methods to forecast and communicate forecast uncertainty, however, little research has been conducted in this domain for small area populations. In this paper, we evaluate a set of probabilistic forecasting methods which include Autoregressive integrated moving average, Exponential Smoothing, THETA, LightGBM and XGBOOST, to produce point forecasts and 80% prediction intervals for Australian SA2 small area populations. We also investigate methods to combine the intervals to produce ensemble forecasts. Our results show that individual probabilistic methods generally produce prediction intervals which underestimate forecast uncertainty. Combining forecasts improves the overall accuracy of point forecasts and the coverage of their intervals, however, coverage still tends to be less than the expected 80% for all but the most conservative combination method.

Generating accurate small area population forecasts is vital for governments and businesses as it provides better grounds for decision making and strategic planning of future demand for services and infrastructure. Small area population forecasting faces numerous challenges, including complex underlying demographic processes, data sparsity, and short time series due to changing geographic boundaries. In this paper, we propose a novel framework for small area forecasting which combines proven demographic forecasting methods, an exponential smoothing based algorithm, and a machine learning based forecasting technique. The proposed forecasting combination contains four base models commonly used in demographic forecasting, a univariate forecasting model specifically suitable for forecasting yearly data, and a globally trained Light Gradient Boosting Model (LGBM) that exploits the similarities between a collection of population time series. In this study, three forecast combination techniques are investigated to weight the forecasts generated by these base models. We empirically evaluate our method, by preparing small area population forecasts for Australia and New Zealand. The proposed framework is able to achieve competitive results in terms of forecasting accuracy. Moreover, we show that the inclusion of the LGBM model always improves the accuracy of combination models on both datasets, relative to combination models which only include the demographic models. In particular, the results indicate that the proposed combination framework decreases the prevalence of relatively poor forecasts, while improving the reliability of small area population forecasts.

A new dataset of population projections for local areas of Australia is described. The areas comprise SA3 areas of the Australian Statistical Geography Standard, which mostly range in population between 30,000 and 130,000. The projections are launched from the 2020 Estimated Resident Populations published by the Australian Bureau of Statistics and extend out to 2035. They are available by sex and five year age group up to 80-84 with 85+ as the final age group and in five year projection intervals. The projections were prepared using the synthetic migration cohort-component model, a new model for population projections which requires much less input data than conventional projection models, and therefore involves much lower costs and production time. Despite this, a recent evaluation demonstrated respectable forecast accuracy, and greater accuracy than equivalent simple projection models producing populations by age and sex. The age-sex projections are constrained to independent age-sex national projections and local area projections of total populations. The dataset consists of local area projections for the whole of the country which is consistent in methods, input data, and projection outputs due to the use of one model. This is rare in Australia because local area projections are most commonly prepared by individual State/Territory Governments using different methods, data sources, projection assumptions (which can be influenced by State/Territory population policies), and time periods. These nationally consistent projection data should be useful for a wide range of local area planning, policy, and research purposes, such as childcare demand, school enrolments, power and water usage, aged care provision, store and business site selection, living arrangements and household projections, labour force projections, and transport modelling.

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Small area population forecasts are widely used by government and business for a variety of planning, research and policy purposes, and often influence major investment decisions. Yet the toolbox of small area population forecasting methods and techniques is modest relative to that for national and large subnational regional forecasting. In this paper we assess the current state of small area population forecasting, and suggest areas for further research. The paper provides a review of the literature on small area population forecasting methods published over the period 2001-2020. The key themes covered by the review are: extrapolative and comparative methods, simplified cohort-component methods, model averaging and combining, incorporating socio-economic variables and spatial relationships, ‘downscaling’ and disaggregation approaches, linking population with housing, estimating and projecting small area component input data, microsimulation, machine learning, and forecast uncertainty. Several avenues for further research are then suggested, including more work on model averaging and combining, developing new forecasting methods for situations which current models cannot handle, quantifying uncertainty, exploring methodologies such as machine learning and spatial statistics, creating user-friendly tools for practitioners, and understanding more about how forecasts are used.

The ‘M4’ forecasting competition results were featured recently in a special issue of the International Journal of Forecasting and included projections for demographic time series. We sought to investigate whether the best M4 methods could improve the accuracy of small area population forecasts, which generally suffer from much higher forecast errors than regions with larger populations. The aim of this study was to apply the top ten M4 forecasting methods to produce 5- and 10-year forecasts of small area total populations using historical datasets from Australia and New Zealand. Forecasts were compared against the actual population numbers and forecasts from two simple benchmark models. The M4 methods were found to perform relatively well compared to our benchmarks. In the light of these findings, we discuss possible future directions for small area population forecasting research.

There has been a decline in the number of new drugs registered over the past decade and regulatory concerns for safety as well as payer concerns for efficacy have focused attention on stratified medicine. Integration of pharmacogenetics into the drug development pipeline will contribute to the development of new stratified drugs. We describe here the concept of pipeline pharmacogenetics and its application throughout the phases of drug discovery. Pipeline pharmacogenetics enables the evaluation of the genetic contribution to safety potentially lowering barriers to registration as well as providing rationale for efficacy and enabling co-development of genetic in vitro diagnostics. © 2009 Bentham Science Publishers Ltd.