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There are several reasons why expenditure managers need to forecast the financial progress of programmes accurately. The paper discusses the use of distributed lag models to assist in this and the practical decisions required in the process of selecting, setting up and calibrating the models.

Background The leading emerging markets of Brazil, Russia, India, China and South Africa (BRICS) are increasingly shaping the landscape of the global health sector demand and supply for medical goods and services. BRICS’ share of global health spending and future projections will play a prominent role during the 2020s. The purpose of the current research was to examine the decades-long underlying historical trends in BRICS countries’ health spending and explore these data as the grounds for reliable forecasting of their health expenditures up to 2030. Methods BRICS’ health spending data spanning 1995–2017 were extracted from the Institute for Health Metrics and Evaluation (IHME) Financing Global Health 2019 database. Total health expenditure, government, prepaid private and out-of-pocket spending per capita and gross domestic product (GDP) share of total health spending were forecasted for 2018–2030. Autoregressive integrated moving average (ARIMA) models were used to obtain future projections based on time series analysis. Results Per capita health spending in 2030 is projected to be as follows: Brazil, $1767 (95% prediction interval [PI] 1615, 1977); Russia, $1933 (95% PI 1549, 2317); India, $468 (95% PI 400.4, 535); China, $1707 (95% PI 1079, 2334); South Africa, $1379 (95% PI 755, 2004). Health spending as a percentage of GDP in 2030 is projected as follows: Brazil, 8.4% (95% PI 7.5, 9.4); Russia, 5.2% (95% PI 4.5, 5.9); India, 3.5% (95% PI 2.9%, 4.1%); China, 5.9% (95% PI 4.9, 7.0); South Africa, 10.4% (95% PI 5.5, 15.3). Conclusions All BRICS countries show a long-term trend towards increasing their per capita spending in terms of purchasing power parity (PPP). India and Russia are highly likely to maintain stable total health spending as a percentage of GDP until 2030. China, as a major driver of global economic growth, will be able to significantly expand its investment in the health sector across an array of indicators. Brazil is the only large nation whose health expenditure as a percentage of GDP is about to contract substantially during the third decade of the twenty-first century. The steepest curve of increased per capita spending until 2030 seems to be attributable to India, while Russia should achieve the highest values in absolute terms. Health policy implications of long-term trends in health spending indicate the need for health technology assessment dissemination among the BRICS ministries of health and national health insurance funds. Matters of cost-effective allocation of limited resources will remain a core challenge in 2030 as well.

The coronavirus disease (COVID-19) is a severe, ongoing, novel pandemic that emerged in Wuhan, China, in December 2019. As of January 21, 2021, the virus had infected approximately 100 million people, causing over 2 million deaths. This article analyzed several time series forecasting methods to predict the spread of COVID-19 during the pandemic’s second wave in Italy (the period after October 13, 2020). The autoregressive moving average (ARIMA) model, innovations state space models for exponential smoothing (ETS), the neural network autoregression (NNAR) model, the trigonometric exponential smoothing state space model with Box–Cox transformation, ARMA errors, and trend and seasonal components (TBATS), and all of their feasible hybrid combinations were employed to forecast the number of patients hospitalized with mild symptoms and the number of patients hospitalized in the intensive care units (ICU). The data for the period February 21, 2020–October 13, 2020 were extracted from the website of the Italian Ministry of Health (www.salute.gov.it). The results showed that (i) hybrid models were better at capturing the linear, nonlinear, and seasonal pandemic patterns, significantly outperforming the respective single models for both time series, and (ii) the numbers of COVID-19-related hospitalizations of patients with mild symptoms and in the ICU were projected to increase rapidly from October 2020 to mid-November 2020. According to the estimations, the necessary ordinary and intensive care beds were expected to double in 10 days and to triple in approximately 20 days. These predictions were consistent with the observed trend, demonstrating that hybrid models may facilitate public health authorities’ decision-making, especially in the short-term.

Background This study forecasts total and cause-specific health expenditures in Norway to 2050 and quantifies the contribution of four key drivers—total population growth, population aging, changes in disease prevalence, and cost per case—on future health care spending. Methods We forecast spending for 116 health conditions in Norway from 2022 to 2050, using historical and forecasted data of population growth, disease prevalence, gross domestic product (GDP), health spending, and residual factors. Our analysis included a reference scenario that forecasted disease-specific health spending; two alternative scenarios examining the effects of alternative unit cost developments; and a scenario examining the consequences of improved behavioral and metabolic risk factors. Results Health spending increased from 10.6% (95% uncertainty interval, 10.2–11.1) of GDP in 2022 to 14.3% (13.0–15.7) in 2050 in the reference scenario. Among the top aggregate causes of Norwegian health spending in 2022, the spending for neurological disorders rose the most, from 1.7% (1.6–1.8) to 2.7% (2.3–3.1) of GDP, surpassing mental and substance use disorders which rose from 2.2% (2.1–2.3) to 2.4% (2.2–2.6) of GDP. Of the 116 single conditions analyzed, dementias accounted for the highest spending in 2022. This expenditure was forecasted to increase considerably from 1.1% (1.09–1.2) to 1.9% (1.6–2.2) of GDP by 2050, largely due to population aging. Spending on other old-age-related conditions like falls, stroke, and diabetes, was also forecasted to increase. Increased population, aging, and spending per case contributed to increased future spending. Reduced behavioral and metabolic risks were forecasted to increase the number of elderly persons and reduce age-specific disease prevalence but had little impact on forecasted health spending. Conclusions Health spending growth was forecasted regardless of the scenario, and Norway needs to plan for this. However, policymakers can curb total spending growth, while maintaining health care quality and output, by ensuring more efficient allocation and effective use of resources. While the overall impact of behavioral and metabolic risk reductions on total healthcare spending was modest, reducing risk factors is needed if countries aim to achieve a healthier, longer-living population.

This study projects the performance of maternal and child mortalities in relation to the SDGs target (70 maternal deaths and 25 child deaths) by year 2030, based on three simulation scenarios of public health expenditures (PHEs). In essence, this study investigates the predictability of PHE in explaining maternal and child mortalities in a bid to confirm the possibility of meeting the SDGs target. The SSA is known to be facing critical health challenges; this study contributes to the problem underlying the health sector by forecasting PHEs in relation to goal 3 because the knowledge of correlation and threshold relationship between PHE and health outcomes, as seen in previous studies, may not be adequate to prepare the SSA countries towards achieving the SDGs target. This study uses Feasible Quasi-Generalised Least Squares as a baseline forecasting approach for 25 selected SSA countries. An increase in the PHE by 30 percent from the current level shows that only Botswana, Namibia, and South Africa will achieve the SDGs target of 70 maternal deaths, while Burundi, Cameroon, Central African Republic, Cote d’Ivoire, Eswatini, Lesotho, Mauritania, Niger, Nigeria, Tanzania, and Togo may have to bear more than 200 maternal deaths by 2030. In contrast, about 60 percent of the countries will achieve the SDGs target for child mortality. PHEs must meet the 30% increase forecasted for a reduction in mortality, being the benchmark that will enable the SSA region to achieve the SDGs target by year 2030.

Background Accelerated globalization especially in the late 1980s has provided opportunities for economic progress in the world of emerging economies. The BRICS nations’ economies are distinguishable from other emerging economies due to their rate of expansion and sheer size. As a result of their economic prosperity, health spending in the BRICS countries has been increasing. However, health security is still a distant dream in these countries due to low public health spending, lack of pre-paid health coverage, and heavy out-of-pocket spending. There is a need for changing the health expenditure composition to address the challenge of regressive health spending and ensure equitable access to comprehensive healthcare services. Objective Present study examined the health expenditure trend among the BRICS from 2000 to 2019 and made predictions with an emphasis on public, pre-paid, and out-of-pocket expenditures for 2035. Methods Health expenditure data for 2000–2019 were taken from the OECD iLibrary database. The exponential smoothing model in R software ( ets () ) was used for forecasting. Results Except for India and Brazil, all of the BRICS countries show a long-term increase in per capita PPP health expenditure. Only India’s health expenditure is expected to decrease as a share of GDP after the completion of the SDG years. China accounts for the steepest rise in per capita expenditure until 2035, while Russia is expected to achieve the highest absolute values. Conclusion The BRICS countries have the potential to be important leaders in a variety of social policies such as health. Each BRICS country has set a national pledge to the right to health and is working on health system reforms to achieve universal health coverage (UHC). The estimations of future health expenditures by these emerging market powers should help policymakers decide how to allocate resources to achieve this goal.

This paper proposes an innovative methodological framework that combines machine-learning and deep learning algorithms with established econometric methods for the critical problem of expenditure forecasting in the budget process. The paper aims to develop, test, and validate an artificial intelligence model capable of improving the accuracy of expenditure forecasting in the budget process and supporting financial accounting decisions in public institutions. Using historical and statistical data from a group of public institutions, the research applies both univariate and multivariate forecasting strategies, evaluated with performance metrics. The research focuses on the development of an innovative forecasting model based on AI, using historical and statistical data from public sources and case studies of local public institutions to transform them into smart cities. The selected AI algorithms include artificial neural networks, support vector machines, and deep learning models, implemented and evaluated using Python v3.14. The research results show that AI can significantly improve the accuracy of budget forecasts compared to traditional methods, such as linear regression and econometric models. The use of AI contributes to increasing transparency and accountability in the management of public funds, providing more detailed and well-founded forecasts.

The article first describes characteristics of major infrastructure projects. Second, it documents a much neglected topic in economics: that ex ante estimates of costs and benefits are often very different from actual ex post costs and benefits. For large infrastructure projects the consequences are cost overruns, benefit shortfalls, and the systematic underestimation of risks. Third, implications for cost–benefit analysis are described, including that such analysis is not to be trusted for major infrastructure projects. Fourth, the article uncovers the causes of this state of affairs in terms of perverse incentives that encourage promoters to underestimate costs and overestimate benefits in the business cases for their projects. But the projects that are made to look best on paper are the projects that amass the highest cost overruns and benefit shortfalls in reality. The article depicts this situation as ‘survival of the unfittest’. Fifth, the article sets out to explain how the problem may be solved, with a view to arriving at more efficient and more democratic projects, and avoiding the scandals that often accompany major infrastructure investments. Finally, the article identifies current trends in major infrastructure development. It is argued that a rapid increase in stimulus spending, combined with more investments in emerging economies, combined with more spending on information technology is catapulting infrastructure investment from the frying pan into the fire.