Use of Available Data To Inform The COVID-19 Outbreak in South Africa: A Case Study

The coronavirus disease (COVID-19), caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organization (WHO) in February 2020. Currently, there are no vaccines or treatments that have been approved after clinical trials. Social distancing measures, including travel bans, school closure, and quarantine applied to countries or regions are being used to limit the spread of the disease and the demand on the healthcare infrastructure. The seclusion of groups and individuals has led to limited access to accurate information. To update the public, especially in South Africa, announcements are made by the minister of health daily. These announcements narrate the confirmed COVID-19 cases and include the age, gender, and travel history of people who have tested positive for the disease. Additionally, the South African National Institute for Communicable Diseases updates a daily infographic summarising the number of tests performed, confirmed cases, mortality rate, and the regions affected. However, the age of the patient and other nuanced data regarding the transmission is only shared in the daily announcements and not on the updated infographic. To disseminate this information, the Data Science for Social Impact research group at the University of Pretoria, South Africa, has worked on curating and applying publicly available data in a way that is computer-readable so that information can be shared to the public - using both a data repository and a dashboard. Through collaborative practices, a variety of challenges related to publicly available data in South Africa came to the fore. These include shortcomings in the accessibility, integrity, and data management practices between governmental departments and the South African public. In this paper, solutions to these problems will be shared by using a publicly available data repository and dashboard as a case study.Comment: Accepted for publication in the Data Science Journa

Improving short text classification through global augmentation methods

We study the effect of different approaches to text augmentation. To do this we use 3 datasets that include social media and formal text in the form of news articles. Our goal is to provide insights for practitioners and researchers on making choices for augmentation for classification use cases. We observe that Word2vec-based augmentation is a viable option when one does not have access to a formal synonym model (like WordNet-based augmentation). The use of \emph{mixup} further improves performance of all text based augmentations and reduces the effects of overfitting on a tested deep learning model. Round-trip translation with a translation service proves to be harder to use due to cost and as such is less accessible for both normal and low resource use-cases.Comment: Final version published in CD-MAKE 2020: Machine Learning and Knowledge Extraction pp 385-39

Cost containment strategies and their relationship to quality of care within the South African private healthcare industry

The purpose of this research was to understand cost containment strategies used by private hospitals under managed care plans and their relationship to quality of care within the South African environment. The data was collected using a questionnaire consisting of closed questions requesting respondents to rate statements about costs and quality of care, as well as open questions for additional information about costs and quality of care. The study found that managed care has the ability to control costs and that hospitals monitor LOS and prescribe generic medication in order to control costs. The study also found that cost control strategies have a negative impact on quality of care and that hospitals place more emphasis on cost control than quality of care. In addition, large hospitals that enjoy high occupancy rates experienced an increase in patient complaints since the introduction of managed care, compared to small and medium hospitals. The study found that managed care has had a better than average impact on controlling costs and a better than average impact in quality reduction, however the correlation between cost control and quality reduction was negative. Finally, the study found that technology has an impact on rising healthcare costs and that any constraints placed on rising costs associated with technology will have a negative impact on quality of care. CopyrightDissertation (MBA)--University of Pretoria, 2010.Gordon Institute of Business Science (GIBS)unrestricte

Multimodal Misinformation Detection in a South African Social Media Environment

With the constant spread of misinformation on social media networks, a need has arisen to continuously assess the veracity of digital content. This need has inspired numerous research efforts on the development of misinformation detection (MD) models. However, many models do not use all information available to them and existing research contains a lack of relevant datasets to train the models, specifically within the South African social media environment. The aim of this paper is to investigate the transferability of knowledge of a MD model between different contextual environments. This research contributes a multimodal MD model capable of functioning in the South African social media environment, as well as introduces a South African misinformation dataset. The model makes use of multiple sources of information for misinformation detection, namely: textual and visual elements. It uses bidirectional encoder representations from transformers (BERT) as the textual encoder and a residual network (ResNet) as the visual encoder. The model is trained and evaluated on the Fakeddit dataset and a South African misinformation dataset. Results show that using South African samples in the training of the model increases model performance, in a South African contextual environment, and that a multimodal model retains significantly more knowledge than both the textual and visual unimodal models. Our study suggests that the performance of a misinformation detection model is influenced by the cultural nuances of its operating environment and multimodal models assist in the transferability of knowledge between different contextual environments. Therefore, local data should be incorporated into the training process of a misinformation detection model in order to optimize model performance.Comment: Artificial Intelligence Research. SACAIR 202

An Intelligent Multi-Agent Recommender System for Human Capacity Building

This paper presents a Multi-Agent approach to the problem of recommending training courses to engineering professionals. The recommendation system is built as a proof of concept and limited to the electrical and mechanical engineering disciplines. Through user modelling and data collection from a survey, collaborative filtering recommendation is implemented using intelligent agents. The agents work together in recommending meaningful training courses and updating the course information. The system uses a users profile and keywords from courses to rank courses. A ranking accuracy for courses of 90% is achieved while flexibility is achieved using an agent that retrieves information autonomously using data mining techniques from websites. This manner of recommendation is scalable and adaptable. Further improvements can be made using clustering and recording user feedback.Comment: Proceedings of the 14th IEEE Mediterranean Electrotechnical Conference, 2008, pages 909 to 91