Browsing by Author "Abdelatif, N"

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    Modelling representative population mobility for COVID-19 spatial transmission in South Africa
    (2021-10) Potgieter, A; Fabris-Rotelli, IN; Kimmie, N; Dudeni-Tlhone, Nontembeko; Holloway, Jennifer P; Janse van Rensburg, C; Thiede, R; Debba, Pravesh; Manjoo-Docrat, R; Abdelatif, N; Makhanya, S
    The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices and further compares the results through hierarchical clustering. This provides insight for the user into which data provides what type of information and in what situations a particular source is most useful.
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    Spatial age-stratified epidemiological model with applications to South African COVID-19 pandemic
    (2025) Manjoo-Docrat, R; Abdelatif, N; Holloway, Jennifer P; Dudeni-Tlhone, Nontembeko; Dresselhaus, C; Mbayise, E; Janse van Rensburg, C; Fabris-Rotelli, I; Debba, Pravesh; Makhanya, S
    Objectives: This study aims to address the heterogeneity among different age groups in terms of their susceptibility to and transmissibility of infectious diseases. It also seeks to understand how spatial disparities affect disease spread and local population responses to emerging and re- emerging infectious diseases (EIDs and REIDs). Design/Methods: We developed a spatial age-stratified SEIR model using COVID-19 hospitalisation data from South Africa, focusing on the first two waves of the pandemic. This model incorporates contact matrices and demographic data to capture age-dependent and spatial variations in disease dynamics. Results: The spatial age-stratified model produced more biologically plausible and accurate predictions compared to non-stratified models investigated. It highlighted significant differences in COVID-19 risk and transmission across different age groups and regions, offering insights into targeted intervention strategies. Conclusions: The proposed model demonstrates the importance of considering both age and spatial heterogeneity in mathematical models for infectious disease prediction. It provides a valuable tool for governments and public health officials, particularly in resource-limited settings, to develop more effective and targeted interventions. This model can be adapted for other EIDs and REIDs with similar dynamics, enhancing preparedness and response strategies.
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    A spatial model with vaccinations for COVID-19 in South Africa
    (2023-12) Dresselhaus, C; Fabris-Rotelli, I; Manjoo-Docrat, R; Brettenny, W; Holloway, Jennifer P; Abdelatif, N; Thiede, R; Debba, Pravesh; Dudeni-Tlhone, Nontembeko
    Since the emergence of the novel COVID-19 virus pandemic in December 2019, numerous mathematical models were published to assess the transmission dynamics of the disease, predict its future course, and evaluate the impact of different control measures. The simplest models make the basic assumptions that individuals are perfectly and evenly mixed and have the same social structures. Such assumptions become problematic for large developing countries that aggregate heterogeneous COVID-19 outbreaks in local areas. Thus, this paper proposes a spatial SEIRDV model that includes spatial vaccination coverage, spatial vulnerability, and level of mobility, to take into account the spatial–temporal clustering pattern of COVID-19 cases. The conclusion of this study is that immunity, government interventions, infectiousness and virulence are the main drivers of the spread of COVID-19. These factors should be taken into consideration when scientists, public policy makers and other stakeholders in the health community analyse, create and project future disease prevention scenarios. Such a model has a place for disease outbreaks that may occur in future, allowing for the inclusion of vaccination rates in a spatial manner.