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Resource constraints in an epidemic: a goal programming and mathematical modelling framework for optimal resource shifting in South Africa
The COVID-19 pandemic has had devastating consequences across the globe, and has led many governments into completely new decision making territory. Developing models which are capable of producing realistic projections of disease spread under extreme uncertainty has been paramount for supporting de...
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| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2022
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| _version_ | 1867613326543945728 |
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| access_status_str | Open Access |
| author | Mayet, Saadiyah |
| author2 | Silal, Sheetal |
| author_browse | Mayet, Saadiyah Silal, Sheetal |
| author_facet | Silal, Sheetal Mayet, Saadiyah |
| author_sort | Mayet, Saadiyah |
| collection | Thesis |
| description | The COVID-19 pandemic has had devastating consequences across the globe, and has led many governments into completely new decision making territory. Developing models which are capable of producing realistic projections of disease spread under extreme uncertainty has been paramount for supporting decision making by many levels of government. In South Africa, this role has been fulfilled by the South African COVID-19 Modelling Consortium's generalised Susceptible-ExposedInfectious-Removed compartmental model, known as the National COVID-19 Epi Model. This thesis adapted and contributed to the Model; its primary contribution has been to incorporate the feature that resources available to the health system are limited. Building capacity constraints into the Model allowed it to be used in the resource-scarce context of a pandemic. This thesis further designed and implemented a goal programming framework to shift ICU beds between districts intra-provincially in a way that aimed to minimise deaths caused by the non-availability of ICU beds. The results showed a 15% to 99% decrease in lives lost when ICU beds were shifted, depending on the scenario considered. Although there are limitations to the scope and assumptions of this thesis, it demonstrates that it is possible to combine mathematical modelling with optimisation in a way that may save lives through optimal resource allocation. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/35867 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:34:20.437Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/35867 Resource constraints in an epidemic: a goal programming and mathematical modelling framework for optimal resource shifting in South Africa Mayet, Saadiyah Silal, Sheetal Durbach, Ian statistical science The COVID-19 pandemic has had devastating consequences across the globe, and has led many governments into completely new decision making territory. Developing models which are capable of producing realistic projections of disease spread under extreme uncertainty has been paramount for supporting decision making by many levels of government. In South Africa, this role has been fulfilled by the South African COVID-19 Modelling Consortium's generalised Susceptible-ExposedInfectious-Removed compartmental model, known as the National COVID-19 Epi Model. This thesis adapted and contributed to the Model; its primary contribution has been to incorporate the feature that resources available to the health system are limited. Building capacity constraints into the Model allowed it to be used in the resource-scarce context of a pandemic. This thesis further designed and implemented a goal programming framework to shift ICU beds between districts intra-provincially in a way that aimed to minimise deaths caused by the non-availability of ICU beds. The results showed a 15% to 99% decrease in lives lost when ICU beds were shifted, depending on the scenario considered. Although there are limitations to the scope and assumptions of this thesis, it demonstrates that it is possible to combine mathematical modelling with optimisation in a way that may save lives through optimal resource allocation. 2022-03-01T16:11:18Z 2022-03-01T16:11:18Z 2021 2022-03-01T16:09:04Z Master Thesis Masters MSc http://hdl.handle.net/11427/35867 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | statistical science Mayet, Saadiyah Resource constraints in an epidemic: a goal programming and mathematical modelling framework for optimal resource shifting in South Africa |
| thesis_degree_str | Master's |
| title | Resource constraints in an epidemic: a goal programming and mathematical modelling framework for optimal resource shifting in South Africa |
| title_full | Resource constraints in an epidemic: a goal programming and mathematical modelling framework for optimal resource shifting in South Africa |
| title_fullStr | Resource constraints in an epidemic: a goal programming and mathematical modelling framework for optimal resource shifting in South Africa |
| title_full_unstemmed | Resource constraints in an epidemic: a goal programming and mathematical modelling framework for optimal resource shifting in South Africa |
| title_short | Resource constraints in an epidemic: a goal programming and mathematical modelling framework for optimal resource shifting in South Africa |
| title_sort | resource constraints in an epidemic a goal programming and mathematical modelling framework for optimal resource shifting in south africa |
| topic | statistical science |
| url | http://hdl.handle.net/11427/35867 |
| work_keys_str_mv | AT mayetsaadiyah resourceconstraintsinanepidemicagoalprogrammingandmathematicalmodellingframeworkforoptimalresourceshiftinginsouthafrica |