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...

Full description

Bibliographic Details
Main Author: Geffen, Nathan
Other Authors: Kuttel, Michelle
Format: Doctoral Thesis
Language:English
Published: University of Cape Town 2018
Subjects:
Online Access:http://hdl.handle.net/11427/27855
id ndltd-netd.ac.za-oai-union.ndltd.org-uct-oai-localhost-11427-27855
record_format oai_dc
spelling ndltd-netd.ac.za-oai-union.ndltd.org-uct-oai-localhost-11427-278552020-07-22T05:07:30Z 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 University of Cape Town Faculty of Science Department of Computer Science
collection NDLTD
language English
format Doctoral Thesis
sources NDLTD
topic Computer Science
spellingShingle Computer Science
Geffen, Nathan
Algorithms for efficiently and effectively matching agents in microsimulations of sexually transmitted infections
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.
author2 Kuttel, Michelle
author_facet Kuttel, Michelle
Geffen, Nathan
author Geffen, Nathan
author_sort Geffen, Nathan
title 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_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_sort algorithms for efficiently and effectively matching agents in microsimulations of sexually transmitted infections
publisher University of Cape Town
publishDate 2018
url http://hdl.handle.net/11427/27855
work_keys_str_mv AT geffennathan algorithmsforefficientlyandeffectivelymatchingagentsinmicrosimulationsofsexuallytransmittedinfections
_version_ 1719330225310924800