In South Africa, the country with the highest income inequality in the world and an unemployment rate of 32.9%, survey research forms a crucial enabler for evidence-based policymaking to drive inclusive growth (Francis and Webster, 2019; Statistics South Africa, 2025a). For decades, nationally representative household surveys have provided the country with key information on livelihoods and areas for targeted decision making by capturing measures such as household income, food expenditure and employment status. More recently, notable survey research has been undertaken in early childhood education (ECE)-seen through the Thrive by Five Index, a nationally representative survey of child outcomes using ECE tools developed and validated for the South African context. There are several computational methods developed in the literature offering solutions for optimum boundary determination and sample size allocation in the stratified sampling approach to survey research. The uptake of computational methods in the South African context, however, remains limited. Our study offers the first quantitative evaluation of more common stratification approaches used in South Africa in comparison to five prominent computational methods in the literature-random search, genetic algorithm, biased random key genetic algorithm, grouping genetic algorithm, and variable neighbourhood search. The findings indicate that substantial precision gains can be realised when adopting these novel methods. Additionally, this study is the first application of the methods to South African datasets, contributing to the literature using notable, recent research use cases in the country: the Thrive by Five Index 2021, ECD Census 2021, and General Household Surveys of 2023 and 2024. Through comprehensive evaluation, the work offers insights for performing stratified sampling in these applied contexts using existing methods available in the R programming language.
The analysis of multivariate serological data derived from blood serum samples and tested for the presence of antibodies against multiple pathogens gained attention in recent years. Despite the common use of a so-called threshold approach to classify individuals as seronegative or-positive, limitations of such an approach have been reported in the literature, with the subjective choice of the threshold being the most important. Here, we consider a Bayesian mixture approach to model continuous IgG antibody concentrations directly while accounting for the presence of individual heterogeneity and implied association between antibody titer levels for two infections. We fitted the proposed model to Belgian bivariate serological data on the varicella-zoster virus (VZV) and parvovirus B19 (PVB19). Given the existing body of evidence with respect to possible reinfections with PVB19, we investigated whether models explicitly accounting for waning of humoral immunity improved model fit. Our results showed that although after a steep rise with age, the observed seroprevalence for PVB19 decreases between the ages of 20 and 40, the mean IgG antibody concentration remains constant with age among individuals in the seropositive component. This could provide evidence of a direct impact of reinfections with PVB19 on the observed IgG antibody levels, while individuals with loss of humoral immunity after natural infection imply an increase in susceptibility. For VZV, the mean IgG antibody levels slightly decrease with increasing age among seropositive individuals, indicating only very limited waning of humoral immunity as age-dependent seroprevalence estimates are monotonically increasing with increasing age. In general, based on our analyses, we showed that mixture models provide additional insights concerning the waning of humoral immunity as compared to more traditional frailty approaches, which focus on estimating the seroprevalence solely while the model is sufficiently flexible to capture observed dynamics in IgG antibody decay.
Seasonality impacts various industries and sectors, influencing agricultural cycles, economic planning, and healthcare resource allocation. We propose a novel approach using an attribute based fuzzy lattice data structure to create overlapping catchment areas using the fundamentals of label propagation and graph clustering. This approach considers both the link structure and attribute similarities between nodes in a network, where the nodes are points of interest in a road network. Nodes may be close or far apart based on connectivity and shared attributes, such as common interests or in a geographical application considering topography features. In this study, we incorporate static and seasonal attributes for geographical nodes, allowing us to explore seasonal catchment areas and provide a more realistic view of accessibility throughout the year. This integrated approach offers a comprehensive framework for assessing spatial accessibility and understanding seasonal variations in regions to enhance planning for essential services.
Rhino poaching in South Africa continues to threaten the existence of African rhino species. Since poachers often attack wildlife parks frequently, predictive models are essential for exploiting the availability of data to gain information about the poachers. Although a number of statistical methods have been applied to poaching prediction, they either do not take the spatial variation of observations into account, require additional observational data, depend on known priors, or result in models that are overfitted and challenging to interpret. This paper proposes the use of point process models to predict the spatial distribution of poaching activity within a wildlife park. Descriptive statistics of poaching spatial point patterns have been considered, as well as univariate non-parametric kernel density estimation. However, the focus of this work is on fitting multivariate parametric point process models, using a number of environmental factors. Since real-world poaching data could not be obtained for this work, due to the sensitivity of the data, a simulation study is performed, where numerous point patterns are generated from the same underlying point process. The method can be used when no data is available, and is based on environmental preferences of poachers, which can be obtained through expert knowledge, literature reviews, or by making intelligent assumptions. The results indicate that the point process models are able to predict the initial probabilities well, for most data generating processes. Point process models thus appear to be a promising method for predicting the spatial distribution of poaching activity.
In this paper, Bayesian statistical process control limits are derived for Cronbach's coefficient alpha (a) in the case of the balanced one-way random effects model. Cronbach's alpha is one of the most commonly used measures for assessing a set of items' internal consistency or reliability, thereby assessing the assumption that they measure the same latent construct. By using the available data and the Jeffreys independence prior, the posterior distribution of a and the predictive density of a future (unknown) Cronbach's alpha ((alpha)over cap(f)) can be derived. Given a stable Phase I process, the predictive density function (f ((alpha)over cap(f) | (alpha)over cap)) and the conditional predictive density functions (f ((alpha)over cap(f)|alpha)) are used to calculate central values, variances, control limits, run-lengths and the average run-length. The predictive density of a future run-length is the average of a large number of geometrical distributions, each with its own parameter value. Three applications of interest are included in this paper. From the results, it can be seen that the average and median run-lengths are usually larger than the theoretical values. An advantage of the Bayesian procedure, however, is that the control limits, in other words, beta, can be adjusted in such a way that the average or median run-length has a specific value.
