Non-parametric quantile regression-based modelling of additive effects to solar irradiation in Southern Africa.

Modelling of solar irradiation is paramount to renewable energy management. This warrants the inclusion of additive effects to predict solar irradiation. Modelling of additive effects to solar irradiation can improve the forecasting accuracy of prediction frameworks. To help develop the frameworks, this current study modelled the additive effects using non-parametric quantile regression (QR). The approach applies quantile splines to approximate non-parametric components when finding the best relationships between covariates and the response variable. However, some additive effects are perceived as linear. Thus, the study included the partial linearly additive quantile regression model (PLAQR) in the quest to find how best the additive effects can be modelled. As a result, a comparative investigation on the forecasting performances of the PLAQR, an additive quantile regression (AQR) model and the new quantile generalised additive model (QGAM) using out-of-sample and probabilistic forecasting metric evaluations was done. Forecasted density plots, Murphy diagrams and results from the Diebold-Mariano (DM) hypothesis test were also analysed. The density plot, the curves on the Murphy diagram and most metric scores computed for the QGAM were slightly better than for the PLAQR and AQR models. That is, even though the DM test indicates that the PLAQR and AQR models are less accurate than the QGAM, we could not conclude an outright greater forecasting performance of the QGAM than the PLAQR or AQR models. However, in situations of probabilistic forecasting metric preferences, each model can be prioritised to be applied to the metric where it performed slightly the best. The three models performed differently in different locations, but the location was not a significant factor in their performances. In contrast, forecasting horizon and sample size influenced model performance differently in the three additive models. The performance variations also depended on the metric being evaluated. Therefore, the study has established the best forecasting horizons and sample sizes for the different metrics. It was finally concluded that a 20% forecasting horizon and a minimum sample size of 10000 data points are ideal when modelling additive effects of solar irradiation using non-parametric QR.

Retrospective Post-Hospitalisation COVID-19 Mortality Risk Assessment of Patients in South Africa.

Background: This study explores the determinants impacting the mortality risk of COVID-19 patients following hospitalisation within South Africa's Limpopo province. Methods: Utilising a dataset comprising 388 patients, the investigation employs a frailty regression model to evaluate the influence of diverse characteristics on mortality outcomes, contrasting its performance against other parametric models based on loglikelihood measures. Results: The findings underscore diabetes and hypertension as notable contributors to heightened mortality rates, underscoring the urgency of effectively managing these comorbidities to optimise patient well-being. Additionally, regional discrepancies come to the fore, with the Capricorn district demonstrating elevated mortality risks, thereby accentuating the necessity for precisely targeted interventions. Medical interventions, particularly ventilation, emerge as pivotal factors in mitigating mortality risk. Gender-based distinctions in mortality patterns also underscore the need for bespoke patient care strategies. Conclusions: Collectively, these outcomes supply practical insights with implications for healthcare interventions, policy formulation, and clinical strategies aimed at ameliorating COVID-19 mortality risk among individuals discharged from hospitals within South Africa's Limpopo province.

An Investigation of Risk Factors Associated with Tuberculosis Transmission in South Africa Using Logistic Regression Model.

Background: South Africa has a high burden of tuberculosis (TB) disease and is currently not meeting the national and international reduction outcome targets. The TB prevalence rate of South Africa in 2015 was estimated at approximately 690 per 100,000 population per year, with an incidence rate of about 834 per 100,000 population. This study examines risk factors associated with development of TB in South Africa. Materials and Methods: This study utilised readily available open access secondary data of 2019 South African Health and Demographic Survey from Statistics South Africa (StatsSA) website, which was collected from self-reported information relating to TB in the household questionnaire. The factors analysed were of demographic, socio-economic and health nature. Bivariate and binary logistics analyses were carried out from which appropriate inferences were drawn on the association of TB with demographic, socio-economic and health factors. Results: In multivariate analysis the study revealed that age, personal weight, smoke, alcohol, asthma, province of residence, race and usually coughing were significantly associated with an increased risk of having TB. Conclusions and Recommendations: The results strongly suggest that young and older people coming from black and coloured ethic groups, who are asthmatic and cough frequently, and/or smoking and consuming alcohol are at high risk of developing TB. In addition, those who are overweight appear to have an increased risk of TB transmission, with the Western Cape, Eastern Cape, Northern Cape, Free State, North West and Gauteng being the hardest hit provinces. Hence, the study recommends that these factors must be taken into account in the planning and development of TB policies in order to work successfully towards the achievement of sustainable development goal of reducing TB by 80% before 2030.

Modelling temperature extremes in the Limpopo province: bivariate time-varying threshold excess approach.

A common problem that arises in extreme value theory when dealing with several variables (such as weather or meteorological) is to find an appropriate method to assess their joint or conditional multivariate extremal dependence behaviour. The method for choosing an appropriate threshold in peaks-over threshold approach is also another problem of endless debate. In this era of climate change and global warming, extreme temperatures accompanied by heat waves and cold waves pose serious economic and health challenges particularly in small economies or developing countries like South Africa. The present study attempts to address these problems, in particular, to deal with and capture dependencies in extreme values of two variables, by applying bivariate conditional extremes modelling with a time-varying threshold to Limpopo province's monthly maximum temperature series. Limpopo and North West provinces are the two hottest provinces in South Africa characterised by heat waves and the present study is carried out in the Limpopo province at Mara, Messina, Polokwane and Thabazimbi meteorological stations for the period 1994-2009. With the aim to model extremal dependence of maximum temperature at these four meteorological stations, two modelling approaches are applied: bivariate conditional extremes model and time-varying threshold. The latter approach was used to capture the climate change effects in the data. The main contribution of this paper is in combining these two approaches in bivariate extremal dependence modelling of maximum temperature extremes in the Limpopo province of South Africa. The findings of the study revealed both significant positive and negative extremal dependence in some pairs of meteorological stations. Among the major findings were the significant strong positive extremal dependence of Thabazimbi on high-temperature values at Mara and the strong negative extremal dependence of Polokwane on high-temperature values at Messina. The findings of this study play an important role in revealing information useful to meteorologists, climatologists, agriculturalists, and planners in the energy sector among others.

Modelling Malaria Incidence in the Limpopo Province, South Africa: Comparison of Classical and Bayesian Methods of Estimation.

Malaria infects and kills millions of people in Africa, predominantly in hot regions where temperatures during the day and night are typically high. In South Africa, Limpopo Province is the hottest province in the country and therefore prone to malaria incidence. The districts of Vhembe, Mopani and Sekhukhune are the hottest districts in the province. Malaria cases in these districts are common and malaria is among the leading causes of illness and deaths in these districts. Factors contributing to malaria incidence in Limpopo Province have not been deeply investigated, aside from the general knowledge that the province is the hottest in South Africa. Bayesian and classical methods of estimation have been applied and compared on the effect of climatic factors on malaria incidence. Credible and confidence intervals from a negative binomial model estimated via Bayesian estimation and maximum likelihood estimation, respectively, were utilized in the comparison process. Overall assumptions underpinning each method were given. The Bayesian method appeared more robust than the classical method in analysing malaria incidence in Limpopo Province. The classical method identified rainfall and temperature during the night to be significant predictors of malaria incidence in Mopani, Vhembe and Waterberg districts. However, the Bayesian method found rainfall, normalised difference vegetation index, elevation, temperatures during the day and night to be the significant predictors of malaria incidence in Mopani, Sekhukhune and Vhembe districts of Limpopo Province. Both methods affirmed that Vhembe district is more susceptible to malaria incidence, followed by Mopani district. We recommend that the Department of Health and Malaria Control Programme of South Africa allocate more resources for malaria control, prevention and elimination to Vhembe and Mopani districts of Limpopo Province.

Modelling non-stationary annual maximum flood heights in the lower Limpopo River basin of Mozambique.

In this article we fit a time-dependent generalised extreme value (GEV) distribution to annual maximum flood heights at three sites: Chokwe, Sicacate and Combomune in the lower Limpopo River basin of Mozambique. A GEV distribution is fitted to six annual maximum time series models at each site, namely: annual daily maximum (AM1), annual 2-day maximum (AM2), annual 5-day maximum (AM5), annual 7-day maximum (AM7), annual 10-day maximum (AM10) and annual 30-day maximum (AM30). Non-stationary time-dependent GEV models with a linear trend in location and scale parameters are considered in this study. The results show lack of sufficient evidence to indicate a linear trend in the location parameter at all three sites. On the other hand, the findings in this study reveal strong evidence of the existence of a linear trend in the scale parameter at Combomune and Sicacate, whilst the scale parameter had no significant linear trend at Chokwe. Further investigation in this study also reveals that the location parameter at Sicacate can be modelled by a nonlinear quadratic trend; however, the complexity of the overall model is not worthwhile in fit over a time-homogeneous model. This study shows the importance of extending the time-homogeneous GEV model to incorporate climate change factors such as trend in the lower Limpopo River basin, particularly in this era of global warming and a changing climate.

Prevalence and predictors of illicit drug use among school-going adolescents in Harare, Zimbabwe.

OBJECTIVE: To estimate the prevalence and predictors of illicit drug use among school-going adolescents in Harare, Zimbabwe. METHODS: We used data from the Global School-based Health Survey (GSHS) conducted in 2003 in Harare to obtain frequencies of a selected list of characteristics. We also carried out logistic regression to assess the association between illicit drug use and explanatory variables. For the purpose of this study, illicit drug use was defined as marijuana or glue use. RESULTS: A total of 1984 adolescents participated in the study. Most of the sample were females (50.7%), 15-year- olds (30.3%), nonsmokers and non-alcohol drinkers. Nine percent of the subjects (13.4% males and 4.9% females) reported having ever used marijuana or glue. Males were more likely to have used marijuana or glue than females (OR=2.70; 95% CI [1.47, 4.96]). Marijuana or glue use was positively associated with cigarette smoking (OR=11.17; 95% CI [4.29, 29.08]), alcohol drinking (OR=7.00; 95% CI [3.39, 14.47]) and sexual intercourse (OR=5.17; 95% CI [2.59, 10.29]). Parental supervision was a protective factor for marijuana or glue use (OR=0.31; 95% CI [0.16, 0.61]). CONCLUSIONS: Public health intervention aimed to prevent marijuana or glue use among adolescents should be designed with the understanding that illicit drug use may be associated with other behaviors such as teenage sexual activity, cigarette smoking and alcohol use.