Application of multiple regression analysis to forecasting South Africa’s electricity demand

In a developing country such as South Africa, understanding the expected future demand for electricity is very important in various planning contexts. It is specifically important to understand how expected scenarios regarding population or economic growth can be translated into corresponding future electricity usage patterns. This paper discusses a methodology for forecasting long-term electricity demand that was specifically developed for applying to such scenarios. The methodology uses a series of multiple regression models to quantify historical patterns of electricity usage per sector in relation to patterns observed in certain economic and demographic variables, and uses these relationships to derive expected future electricity usage patterns. The methodology has been used successfully to derive forecasts used for strategic planning within a private company as well as to provide forecasts to aid planning in the public sector. This paper discusses the development of the modelling methodology, provides details regarding the extensive data collection and validation processes followed during the model development, and reports on the relevant model fit statistics. The paper also shows that the forecasting methodology has to some extent been able to match the actual patterns, and therefore concludes that the methodology can be used to support planning by translating changes relating to economic and demographic growth, for a range of scenarios, into a corresponding electricity demand. The methodology therefore fills a particular gap within the South African long-term electricity forecasting domain

Application of multiple regression analysis to forecasting South Africa’s electricity demand

In a developing country such as South Africa, understanding the expected future demand for electricity is very important in various planning contexts. It is specifically important to understand how expected scenarios regarding population or economic growth can be translated into corresponding future electricity usage patterns. This paper discusses a methodology for forecasting long-term electricity demand that was specifically developed for applying to such scenarios. The methodology uses a series of multiple regression models to quantify historical patterns of electricity usage per sector in relation to patterns observed in certain economic and demographic variables, and uses these relationships to derive expected future electricity usage patterns. The methodology has been used successfully to derive forecasts used for strategic planning within a private company as well as to provide forecasts to aid planning in the public sector. This paper discusses the development of the modelling methodology, provides details regarding the extensive data collection and validation processes followed during the model development, and reports on the relevant model fit statistics. The paper also shows that the forecasting methodology has to some extent been able to match the actual patterns, and therefore concludes that the methodology can be used to support planning by translating changes relating to economic and demographic growth, for a range of scenarios, into a corresponding electricity demand. The methodology therefore fills a particular gap within the South African long-term electricity forecasting domain

Density forecasting for long-term electricity demand in South Africa using quantile regression

Background: This study involves forecasting electricity demand for long-term planning purposes. Long-term forecasts for hourly electricity demands from 2006 to 2023 are done with in-sample forecasts from 2006 to 2012 and out-of-sample forecasts from 2013 to 2023. Quantile regression (QR) is used to forecast hourly electricity demand at various percentiles. Three contributions of this study are (1) that QR is used to generate long-term forecasts of the full distribution per hour of electricity demand in South Africa; (2) variabilities in the forecasts are evaluated and uncertainties around the forecasts can be assessed as the full demand distribution is forecasted and (3) probabilities of exceedance can be calculated, such as the probability of future peak demand exceeding certain levels of demand. A case study, in which forecasted electricity demands over the long-term horizon were developed using South African electricity demand data, is discussed.   Aim: The aim of the study was: (1) to apply a quantile regression (QR) model to forecast hourly distribution of electricity demand in South Africa; (2) to investigate variabilities in the forecasts and evaluate uncertainties around point forecasts and (3) to determine whether the future peak electricity demands are likely to increase or decrease.   Setting: The study explored the probabilistic forecasting of electricity demand in South Africa.   Methods: The future hourly electricity demands were forecasted at 0.01, 0.02, 0.03, … , 0.99 quantiles of the distribution using QR, hence each hour of the day would have 99 forecasted future hourly demands, instead of forecasting just a single overall hourly demand as in the case of OLS.   Results: The findings are that the future distributions of hourly demands and peak daily demands would be more likely to shift towards lower demands over the years until 2023 and that QR gives accurate long-term point forecasts with the peak demands well forecasted.   Conclusion: QR gives forecasts at all percentiles of the distribution, allowing the potential variabilities in the forecasts to be evaluated by comparing the 50th percentile forecasts with the forecasts at other percentiles. Additional planning information, such as expected pattern shifts and probable peak values, could also be obtained from the forecasts produced by the QR model, while such information would not easily be obtained from other forecasting approaches. The forecasted electricity demand distribution closely matched the actual demand distribution between 2012 and 2015. Therefore, the forecasted demand distribution is expected to continue representing the actual demand distribution until 2023. Using a QR approach to obtain long-term forecasts of hourly load profile patterns is, therefore, recommended

ENIGMA-anxiety working group : Rationale for and organization of large-scale neuroimaging studies of anxiety disorders

Altres ajuts: Anxiety Disorders Research Network European College of Neuropsychopharmacology; Claude Leon Postdoctoral Fellowship; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, 44541416-TRR58); EU7th Frame Work Marie Curie Actions International Staff Exchange Scheme grant 'European and South African Research Network in Anxiety Disorders' (EUSARNAD); Geestkracht programme of the Netherlands Organization for Health Research and Development (ZonMw, 10-000-1002); Intramural Research Training Award (IRTA) program within the National Institute of Mental Health under the Intramural Research Program (NIMH-IRP, MH002781); National Institute of Mental Health under the Intramural Research Program (NIMH-IRP, ZIA-MH-002782); SA Medical Research Council; U.S. National Institutes of Health grants (P01 AG026572, P01 AG055367, P41 EB015922, R01 AG060610, R56 AG058854, RF1 AG051710, U54 EB020403).Anxiety disorders are highly prevalent and disabling but seem particularly tractable to investigation with translational neuroscience methodologies. Neuroimaging has informed our understanding of the neurobiology of anxiety disorders, but research has been limited by small sample sizes and low statistical power, as well as heterogenous imaging methodology. The ENIGMA-Anxiety Working Group has brought together researchers from around the world, in a harmonized and coordinated effort to address these challenges and generate more robust and reproducible findings. This paper elaborates on the concepts and methods informing the work of the working group to date, and describes the initial approach of the four subgroups studying generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobia. At present, the ENIGMA-Anxiety database contains information about more than 100 unique samples, from 16 countries and 59 institutes. Future directions include examining additional imaging modalities, integrating imaging and genetic data, and collaborating with other ENIGMA working groups. The ENIGMA consortium creates synergy at the intersection of global mental health and clinical neuroscience, and the ENIGMA-Anxiety Working Group extends the promise of this approach to neuroimaging research on anxiety disorders

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Food and nutrition labelling : the past, present and the way forward

CITATION: Koen, N., Blaauw, R. & Wentzel-Viljoen, Edelweiss. 2016. Food and nutrition labelling : the past, present and the way forward. South African Journal of Clinical Nutrition, 29(1):13-21.The original publication is available at http://www.sajcn.co.zaCurrent global mortality from noncommunicable diseases (NCDs) remains unacceptably high and is increasing. A major reduction in the burden of NCDs should come from population-wide interventions, including the promotion of a healthy diet through the provision of adequate nutrition information on food labels. However, in order for this type of intervention to be successful, it is important to have a better understanding of the consumer. This review focuses on the need for food and nutrition labelling (the section of information on a food label that specifically declares nutrient content) within the context of NCDs, as well as consumer nutrition label use, and understanding and the impact of nutrition labelling on purchasing behaviour. It provides a summary of the latest global nutrition labelling trends, the current situation in South Africa and the way forward. Consumer knowledge, use and understanding of nutrition labelling has been investigated extensively in the international literature. However, the majority of these investigations were conducted in developed countries. Therefore, additional research on the impact of nutrition labelling in developing countries is necessary, and should be a priority. There have been many developments in South Africa in terms of food and nutrition labelling in the last decade. Although the food industry, health professionals and consumers face many changes, challenges and opportunities with regard to food, and specifically to nutrition labelling, this is also the ideal time to promote the use and understanding of nutrition information on labels by health professionals to consumers.http://www.sajcn.co.za/index.php/SAJCN/article/view/1076Publisher's versio

The development of a single health-endorsement logo for South Africa

CITATION: Koen, N., Wentzel-Viljoen, E. & Blaauw, R. 2018. The development of a single health-endorsement logo for South Africa. Public Health Nutrition, 21(8): 1444-1454, doi:10.1017/S1368980018000034.The original publication is available at https://www.cambridge.org/core/journals/public-health-nutritionObjective: To develop health-endorsement logos (HEL) for food products indicating healthy choices based on the South African nutrient profile model and to pilot test these logos with consumers. Design: Multistage mixed-methods design. Setting: Cape Town, South Africa. Subjects: Nine focus group discussions (FGD) were conducted with adult consumers to explore what types of HEL are preferred and why. Based on the findings, ten HEL were designed by a graphic design team. A modified Delphi technique, conducted with experts in the fields of nutrition and food science, was employed to eliminate lowest-scoring HEL and to improve the design of the remaining logos. Participants from the initial FGD participated in pilot testing the improved logos. Results: Participants from FGD (n 67) were positive about a single HEL, stating it would make food labelling less confusing as they did not understand the various HEL used. Participants indicated the logo should include wording related to ‘healthy choice’ or ‘better choice’ and pictures/symbols related to health and/or food. During two rounds of scoring and comments by experts (n 19), five logos were eliminated and the design of the remaining five improved. Three of five remaining logos received overall rankings of 3·08/5, 3·28/5 and 3·39/5, respectively, during FGD (n 36) in the pilot-testing phase. Conclusion: HEL were designed and consumer tested. Three designs were submitted to the national Department of Health to consider for implementation, after further testing, as a tool to assist in addressing the high incidence of non-communicable diseases in South Africa.Publisher's versio

Estimating carbon in savanna ecosystems: rational distribution of effort

Clean development mechanism, Economic analysis, Land C sink, Optimal sampling, Savanna, Sequestration, Transaction costs,

Assessing the utilization of a child health monitoring tool

CITATION: Blaauw, R., et al. 2017. Assessing the utilization of a child health monitoring tool. South African Journal of Child Health, 11(4):174-179, doi:10.7196/SAJCH.2017.v11i4.1326.The original publication is available at http://www.sajch.org.zaObjective: The study assessed the implementation of growth monitoring and promotion, immunisation, vitamin A supplementation, and deworming sections of the Road-to-Health Booklet. Caregivers and health care workers knowledge, attitudes and practices were investigated as well as health care workers perceptions of barriers undermining implementation. Methods: A cross-sectional descriptive study was conducted on a proportional sample of randomly selected Primary Health Care facilities across six health districts (35%; n=143) in the Western Cape Province. Health care workers involved in the implementation of the Road-to-Health Booklet, children (0-36 months) and CGs were included. Information was obtained through scrutiny of the Road-to-Health Booklet, observation of consultations and structured questionnaires. Results: A total of 2442 children, 2481 caregivers and 270 health care workers were recruited. Weight (94.7%) measurements were performed routinely. Less than half (40.2%) of caregivers reported that their child’s growth was explained. Sixty-eight percent of health care workers correctly identified criteria for underweight, whereas only 55% and 39% could do so for stunting and wasting respectively. Road-to-Health Booklet sections were completed adequately for immunization (89.3%), vitamin A supplementation (94.6%) but not for deworming (48.8%). Most health care workers (94%) knew the correct regimes for vitamin A supplementation and deworming, but few caregivers knew when treatment was due for vitamin A supplementation (16.4%) and deworming (26.2%). Potential barriers identified related to inadequate training, staff shortages and limited time. Conclusion: Focussed effort and resources should be channelled towards health care workers training and monitoring regarding growth monitoring and promotion to optimize utilization of the Road-to-Health Booklet. Mobilisation of community health workers is needed to strengthen community awareness of preventative health interventions.http://www.sajch.org.za/index.php/SAJCH/article/view/1396Publisher's versio