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Algorithms for efficiently and effectively matching agents in microsimulations of sexually transmitted infections
Mathematical models of the HIV epidemic have been used to estimate incidence, prevalence and life-expectancy, as well the benefits and costs of public health interventions, such as the provision of antiretroviral treatment. Models of sexually transmitted infection epidemics attempt to account for va...
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| Format: | Thesis |
| Language: | English |
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Department of Computer Science
2018
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| _version_ | 1867613174508814336 |
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| access_status_str | Open Access |
| author | Geffen, Nathan |
| author2 | Kuttel, Michelle |
| author_browse | Geffen, Nathan Kuttel, Michelle |
| author_facet | Kuttel, Michelle Geffen, Nathan |
| author_sort | Geffen, Nathan |
| collection | Thesis |
| description | Mathematical models of the HIV epidemic have been used to estimate incidence, prevalence and life-expectancy, as well the benefits and costs of public health interventions, such as the provision of antiretroviral treatment. Models of sexually transmitted infection epidemics attempt to account for varying levels of risk across a population based on diverse / or heterogeneous / sexual behaviour. Microsimulations are a type of model that can account for fine-grained heterogeneous sexual behaviour. This requires pairing individuals, or agents, into sexual partnerships whose distribution matches that of the population being studied, to the extent this is known. But pair-matching is computationally expensive. There is a need for computer algorithms that pair-match quickly. In this work we describe the role of modelling in responses to the South African HIV epidemic. We also chronicle a three-decade debate, greatly influenced since 2008 by a mathematical model, on the optimal time for people with HIV to start antiretroviral treatment. We then present and analyse several pair-matching algorithms, and compare them in a microsimulation of a fictitious STI. We find that there are algorithms, such as Cluster Shuffle Pair-Matching, that offer a good compromise between speed and approximating the distribution of sexual relationships of the study-population. An interesting further finding is that infection incidence decreases as population increases, all other things being equal. Whether this is an artefact of our methodology or a natural world phenomenon is unclear and a topic for further research. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/27855 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:31:56.645Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | Department of Computer Science |
| publisherStr | Department of Computer Science |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/27855 Algorithms for efficiently and effectively matching agents in microsimulations of sexually transmitted infections Geffen, Nathan Kuttel, Michelle Computer Science Mathematical models of the HIV epidemic have been used to estimate incidence, prevalence and life-expectancy, as well the benefits and costs of public health interventions, such as the provision of antiretroviral treatment. Models of sexually transmitted infection epidemics attempt to account for varying levels of risk across a population based on diverse / or heterogeneous / sexual behaviour. Microsimulations are a type of model that can account for fine-grained heterogeneous sexual behaviour. This requires pairing individuals, or agents, into sexual partnerships whose distribution matches that of the population being studied, to the extent this is known. But pair-matching is computationally expensive. There is a need for computer algorithms that pair-match quickly. In this work we describe the role of modelling in responses to the South African HIV epidemic. We also chronicle a three-decade debate, greatly influenced since 2008 by a mathematical model, on the optimal time for people with HIV to start antiretroviral treatment. We then present and analyse several pair-matching algorithms, and compare them in a microsimulation of a fictitious STI. We find that there are algorithms, such as Cluster Shuffle Pair-Matching, that offer a good compromise between speed and approximating the distribution of sexual relationships of the study-population. An interesting further finding is that infection incidence decreases as population increases, all other things being equal. Whether this is an artefact of our methodology or a natural world phenomenon is unclear and a topic for further research. 2018-04-24T14:01:56Z 2018-04-24T14:01:56Z 2018 Doctoral Thesis Doctoral PhD http://hdl.handle.net/11427/27855 eng application/pdf Department of Computer Science Faculty of Science University of Cape Town |
| spellingShingle | Computer Science Geffen, Nathan Algorithms for efficiently and effectively matching agents in microsimulations of sexually transmitted infections |
| thesis_degree_str | Doctoral |
| title | Algorithms for efficiently and effectively matching agents in microsimulations of sexually transmitted infections |
| title_full | Algorithms for efficiently and effectively matching agents in microsimulations of sexually transmitted infections |
| title_fullStr | Algorithms for efficiently and effectively matching agents in microsimulations of sexually transmitted infections |
| title_full_unstemmed | Algorithms for efficiently and effectively matching agents in microsimulations of sexually transmitted infections |
| title_short | Algorithms for efficiently and effectively matching agents in microsimulations of sexually transmitted infections |
| title_sort | algorithms for efficiently and effectively matching agents in microsimulations of sexually transmitted infections |
| topic | Computer Science |
| url | http://hdl.handle.net/11427/27855 |
| work_keys_str_mv | AT geffennathan algorithmsforefficientlyandeffectivelymatchingagentsinmicrosimulationsofsexuallytransmittedinfections |