Research Publications/Outputs

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Browsing Research Publications/Outputs 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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    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.