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Approximate Bayesian computation for a spatial susceptible-exposed-infectious-removed model
Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2021.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2022
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| _version_ | 1867613553875222528 |
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| access_status_str | Open Access |
| author2 | Fabris-Rotelli, Inger Nicolette |
| author_browse | Fabris-Rotelli, Inger Nicolette |
| author_facet | Fabris-Rotelli, Inger Nicolette |
| collection | Thesis |
| dc_rights_str_mv | © 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. |
| description | Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2021. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/83903 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:58.997Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/83903 Approximate Bayesian computation for a spatial susceptible-exposed-infectious-removed model Fabris-Rotelli, Inger Nicolette arminnpotgieter@gmail.com Potgieter, Arminn Mathematical statistics UCTD Mini Dissertation (MSc (Advanced Data Analytics))--University of Pretoria, 2021. In this mini-dissertation we utilize population mobility data and COVID-19 case data in a variety of formats, from a variety of sources, in order to formulate a model for the spatial spread of COVID-19. The study region for this mini-dissertation is the Western Cape province of South Africa. Appropriate spatial structures are formulated using both standard and novel approaches, and the effect of these different conceptualisations of spatial association are illustrated, compared and discussed. The spatial spread of COVID-19 is modelled using a susceptible-exposed-infectious-removed (SEIR) model that describes the progression of the disease. The model is stochastic in nature in order to incorporate the inherent uncertainty present in pandemic parameters. The stochastic nature of the model allows for greater inferential capabilities than deterministic models. Pandemic characteristics such as the spatial autocorrelation of COVID-19 cases and the reproductive number of the disease are determined and discussed. Model fitting and inference are achieved through the use of approximate Bayesian computation (ABC) techniques for likelihood-free inference. This computational framework extends naturally to stochastic pandemic models, since the potentially complex disease system results in computationally infeasible likelihood expressions. The use of artificial neural networks for the purpose of improving the computational efficiency of this computational framework is evaluated and discussed. National research fund STATOMET Statistics MSc (Advanced Data Analytics) Unrestricted 2022-02-14T13:53:54Z 2022-02-14T13:53:54Z 2022-04 2021-10 Mini Dissertation Potgieter, A 2021, Approximate Bayesian computation for a spatial susceptible-exposed-infectious-removed model, MSc mini-dissertation, University of Pretoria, Pretoria http://hdl.handle.net/2263/83903 A2022 http://hdl.handle.net/2263/83903 en © 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. application/pdf University of Pretoria |
| spellingShingle | Mathematical statistics UCTD Approximate Bayesian computation for a spatial susceptible-exposed-infectious-removed model |
| title | Approximate Bayesian computation for a spatial susceptible-exposed-infectious-removed model |
| title_full | Approximate Bayesian computation for a spatial susceptible-exposed-infectious-removed model |
| title_fullStr | Approximate Bayesian computation for a spatial susceptible-exposed-infectious-removed model |
| title_full_unstemmed | Approximate Bayesian computation for a spatial susceptible-exposed-infectious-removed model |
| title_short | Approximate Bayesian computation for a spatial susceptible-exposed-infectious-removed model |
| title_sort | approximate bayesian computation for a spatial susceptible exposed infectious removed model |
| topic | Mathematical statistics UCTD |
| url | http://hdl.handle.net/2263/83903 |