On the complexities of modeling HIV/AIDS in Southern Africa

In this paper problems associated with the modeling of HIV/AIDS in Southern Africa are presented. A mathematical model is presented to highlight the three major challenges of modeling HIV/AIDS, i.e condom use, vertical transmission and treatment. The model analysis for the case, where the treatment parameter ñ = 0, is presented in terms of the model reproduction number R and threshold parameters RT and RA that show the contribution of vertical transmission. It is shown that if R, RT, RA < 1, then the disease free equilibrium point is both locally asymptotically and globally stable. Numerical simulations for the model are presented to determine the role of some key epidemiological parameters of the model. First Published Online: 14 Oct 201

A systems dynamic model for drug abuse and drug-related crime in the Western Cape Province of South Africa

CITATION: Nyabadza, F. & Coetzee, L. 2017. A systems dynamic model for drug abuse and drug-related crime in the Western Cape Province of South Africa. Computational and Mathematical Methods in Medicine, 2017:1-14 (Article ID 4074197), doi:10.1155/2017/4074197.The original publication is available at http://www.hindawi.com/journals/cmmmPublication of this article was funded by the Stellenbosch University Open Access Fund.The complex problem of drug abuse and drug-related crimes in communities in the Western Cape province cannot be studied in isolation but through the system they are embedded in. In this paper, a theoretical model to evaluate the syndemic of substance abuse and drug-related crimes within the Western Cape province of South Africa is constructed and explored. The dynamics of drug abuse and drug-related crimes within the Western Cape are simulated using STELLA software. The simulation results are consistent with the data from SACENDU and CrimeStats SA, highlighting the usefulness of such a model in designing and planning interventions to combat substance abuse and its related problems.https://www.hindawi.com/journals/cmmm/2017/4074197/abs/Publisher's versio

AI-enabled case detection model for infectious disease outbreaks in resource-limited settings

IntroductionThe utility of non-contact technologies for screening infectious diseases such as COVID-19 can be enhanced by improving the underlying Artificial Intelligence (AI) models and integrating them into data visualization frameworks. AI models that are a fusion of different Machine Learning (ML) models where one has leveraged the different positive attributes of these models have the potential to perform better in detecting infectious diseases such as COVID-19. Furthermore, integrating other patient data such as clinical, socio-demographic, economic and environmental variables with the image data (e.g., chest X-rays) can enhance the detection capacity of these models.MethodsIn this study, we explore the use of chest X-ray data in training an optimized hybrid AI model based on a real-world dataset with limited sample size to screen patients with COVID-19. We develop a hybrid Convolutional Neural Network (CNN) and Random Forest (RF) model based on image features extracted through a CNN and EfficientNet B0 Transfer Learning Model and applied to an RF classifier. Our approach includes an intermediate step of using the RF's wrapper function, the Boruta Algorithm, to select important variable features and further reduce the number of features prior to using the RF model.Results and discussionThe new model obtained an accuracy and recall of 96% for both and outperformed the base CNN model and four other experimental models that combined transfer learning and alternative options for dimensionality reduction. The performance of the model fares closely to relatively similar models previously developed, which were trained on large datasets drawn from different country contexts. The performance of the model is very close to that of the “gold standard” PCR tests, which demonstrates the potential for use of this approach to efficiently scale-up surveillance and screening capacities in resource limited settings

Quantifying early COVID-19 outbreak transmission in South Africa and exploring vaccine efficacy scenarios

The emergence and fast global spread of COVID-19 has presented one of the greatest public health challenges in modern times with no proven cure or vaccine. Africa is still early in this epidemic, therefore the extent of disease severity is not yet clear. We used a mathematical model to fit to the observed cases of COVID-19 in South Africa to estimate the basic reproductive number and critical vaccination coverage to control the disease for different hypothetical vaccine efficacy scenarios. We also estimated the percentage reduction in effective contacts due to the social distancing measures implemented. Early model estimates show that COVID-19 outbreak in South Africa had a basic reproductive number of 2.95 (95% credible interval [CrI] 2.83-3.33). A vaccine with 70% efficacy had the capacity to contain COVID-19 outbreak but at very higher vaccination coverage 94.44% (95% Crl 92.44-99.92%) with a vaccine of 100% efficacy requiring 66.10% (95% Crl 64.72-69.95%) coverage. Social distancing measures put in place have so far reduced the number of social contacts by 80.31% (95% Crl 79.76-80.85%). These findings suggest that a highly efficacious vaccine would have been required to contain COVID-19 in South Africa. Therefore, the current social distancing measures to reduce contacts will remain key in controlling the infection in the absence of vaccines and other therapeutics

Mathematical Modelling of Bacterial Meningitis Transmission Dynamics with Control Measures

Vaccination and treatment are the most effective ways of controlling the transmission of most infectious diseases. While vaccination helps susceptible individuals to build either a long-term immunity or short-term immunity, treatment reduces the number of disease-induced deaths and the number of infectious individuals in a community/nation. In this paper, a nonlinear deterministic model with time-dependent controls has been proposed to describe the dynamics of bacterial meningitis in a population. The model is shown to exhibit a unique globally asymptotically stable disease-free equilibrium E0, when the effective reproduction number RVT≤1, and a globally asymptotically stable endemic equilibrium E1, when RVT>1; and it exhibits a transcritical bifurcation at RVT=1. Carriers have been shown (by Tornado plot) to have a higher chance of spreading the infection than those with clinical symptoms who will sometimes be bound to bed during the acute phase of the infection. In order to find the best strategy for minimizing the number of carriers and ill individuals and the cost of control implementation, an optimal control problem is set up by defining a Lagrangian function L to be minimized subject to the proposed model. Numerical simulation of the optimal problem demonstrates that the best strategy to control bacterial meningitis is to combine vaccination with other interventions (such as treatment and public health education). Additionally, this research suggests that stakeholders should press hard for the production of existing/new vaccines and antibiotics and their disbursement to areas that are most affected by bacterial meningitis, especially Sub-Saharan Africa; furthermore, individuals who live in communities where the environment is relatively warm (hot/moisture) are advised to go for vaccination against bacterial meningitis