dc.contributor.advisor |
Fabris-Rotelli, Inger Nicolette |
|
dc.contributor.postgraduate |
Dresselhaus, Claudia Josephina |
|
dc.date.accessioned |
2023-02-14T10:25:58Z |
|
dc.date.available |
2023-02-14T10:25:58Z |
|
dc.date.created |
2023-05-05 |
|
dc.date.issued |
2023 |
|
dc.description |
Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2023. |
en_US |
dc.description.abstract |
Rarely has the world undertaken a public health effort equal in scale or scope to the one it faced in response to the COVID-19 pandemic. Countries around the world implemented government interventions such as lockdowns and national vaccination campaigns to gain control of the COVID-19 pandemic that tore across the globe in 2020. Despite best effort and determination, thousands within the global population continued to suffer and die from COVID-19 day after day. Nevertheless, much was learned about designing mass vaccination plans and implementing mass vaccination roll-outs throughout the world. When analysing cause and effect of the pandemic and when proposing intervention and prevention mechanisms to counter the pandemic, analysts in the health sector often apply mathematical models. Within the context described above, the main objective is to improve on the previously published spatial SEIR model for South Africa by including a compartment for spatial vaccination. The study further aims to assess validity, reliability and accuracy of the new model, given a socially heterogeneous and mobile population. The conclusion of this study is that the proposed model shows promising results in predicting the number of cases as well as the peak point and longevity of the wave. The study further concludes that factors such as immunity, lockdown levels, infectiousness and virulence are the
main drivers of the spread of COVID-19. |
en_US |
dc.description.availability |
Unrestricted |
en_US |
dc.description.degree |
MSc (Advanced Data Analytics) |
en_US |
dc.description.department |
Statistics |
en_US |
dc.identifier.citation |
* |
en_US |
dc.identifier.other |
A2023 |
|
dc.identifier.uri |
https://repository.up.ac.za/handle/2263/89486 |
|
dc.language.iso |
en |
en_US |
dc.publisher |
University of Pretoria |
|
dc.rights |
© 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
|
dc.subject |
Spatial Epidemiology |
en_US |
dc.subject |
Vaccinations |
en_US |
dc.subject |
COVID-19 |
en_US |
dc.subject |
South Africa |
en_US |
dc.subject |
Disease Modelling |
en_US |
dc.subject |
UCTD |
|
dc.title |
A spatial model with vaccinations for COVID-19 in South Africa |
en_US |
dc.type |
Mini Dissertation |
en_US |