Modelling the Impact of Drug Resistance on Treatment as Prevention as an HIV Control Strategy

Uganda is using a strategy called treatment as prevention where as many individuals as possible that are infected with HIV receive treatment. As a result, the number of newly infected individuals has decreased significantly. However, there is a discussion about a potential problem regarding transmit...

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Main Authors: Rylander, Andreas, Persson, Liam
Format: Others
Language:English
Published: KTH, Matematisk statistik 2019
Subjects:
HIV
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254237
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spelling ndltd-UPSALLA1-oai-DiVA.org-kth-2542372019-07-03T10:06:27ZModelling the Impact of Drug Resistance on Treatment as Prevention as an HIV Control StrategyengModellering av den inverkan läkemedelsresistans har på framgången för smittorisk-förebyggande behandling av HIVRylander, AndreasPersson, LiamKTH, Matematisk statistikKTH, Matematisk statistik2019Bachelor ThesisApplied MathematicsStochastic ModellingHIVHIV Control StrategyResistance ProfilingTreatment as PreventionKandidatexamensarbeteTillämpad MatematikStokastisk ModelleringHIVSmittorisk-förebyggande Behandling av HIVResistansprofileringProbability Theory and StatisticsSannolikhetsteori och statistikUganda is using a strategy called treatment as prevention where as many individuals as possible that are infected with HIV receive treatment. As a result, the number of newly infected individuals has decreased significantly. However, there is a discussion about a potential problem regarding transmitted drug resistance. This work aims to investigate if this in fact will be a problem in the future, and to estimate the costs for different scenarios. Through developing a population-based mathematical model that describes transmission dynamics of HIV in Uganda, stochastic simulations are made for different conditions. Through analysing our simulations, we can see that Uganda may have to change their approach to HIV treatment. För att minska smittoriskerna av HIV nyttjar Uganda en strategi som syftar till att behandla så många smittade personer som möjligt. Detta har lett till en signifikant minskning av antalet smittade personer. Det har dock uppstått en diskussion angående om läkemedels-resistent smitta kan komma att utgöra ett problem. Detta arbete syftar till att undersöka om detta kan utgöra ett problem i framtiden samt till att uppskatta de kostnader som kan uppstå i olika typer av scenarion. Under olika förutsättningar genomförs stokastiska simuleringar med hjälp av en matematisk populationsmodell framtagen för att beskriva spridningen av HIV i Uganda. Genom att analysera resultaten från olika simuleringar dras slutsatsen att Uganda kan behöva omvärdera sitt tillvägagångssätt gällande behandling av HIV. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254237TRITA-SCI-GRU ; 2019:155application/pdfinfo:eu-repo/semantics/openAccess
collection NDLTD
language English
format Others
sources NDLTD
topic Bachelor Thesis
Applied Mathematics
Stochastic Modelling
HIV
HIV Control Strategy
Resistance Profiling
Treatment as Prevention
Kandidatexamensarbete
Tillämpad Matematik
Stokastisk Modellering
HIV
Smittorisk-förebyggande Behandling av HIV
Resistansprofilering
Probability Theory and Statistics
Sannolikhetsteori och statistik
spellingShingle Bachelor Thesis
Applied Mathematics
Stochastic Modelling
HIV
HIV Control Strategy
Resistance Profiling
Treatment as Prevention
Kandidatexamensarbete
Tillämpad Matematik
Stokastisk Modellering
HIV
Smittorisk-förebyggande Behandling av HIV
Resistansprofilering
Probability Theory and Statistics
Sannolikhetsteori och statistik
Rylander, Andreas
Persson, Liam
Modelling the Impact of Drug Resistance on Treatment as Prevention as an HIV Control Strategy
description Uganda is using a strategy called treatment as prevention where as many individuals as possible that are infected with HIV receive treatment. As a result, the number of newly infected individuals has decreased significantly. However, there is a discussion about a potential problem regarding transmitted drug resistance. This work aims to investigate if this in fact will be a problem in the future, and to estimate the costs for different scenarios. Through developing a population-based mathematical model that describes transmission dynamics of HIV in Uganda, stochastic simulations are made for different conditions. Through analysing our simulations, we can see that Uganda may have to change their approach to HIV treatment. === För att minska smittoriskerna av HIV nyttjar Uganda en strategi som syftar till att behandla så många smittade personer som möjligt. Detta har lett till en signifikant minskning av antalet smittade personer. Det har dock uppstått en diskussion angående om läkemedels-resistent smitta kan komma att utgöra ett problem. Detta arbete syftar till att undersöka om detta kan utgöra ett problem i framtiden samt till att uppskatta de kostnader som kan uppstå i olika typer av scenarion. Under olika förutsättningar genomförs stokastiska simuleringar med hjälp av en matematisk populationsmodell framtagen för att beskriva spridningen av HIV i Uganda. Genom att analysera resultaten från olika simuleringar dras slutsatsen att Uganda kan behöva omvärdera sitt tillvägagångssätt gällande behandling av HIV.
author Rylander, Andreas
Persson, Liam
author_facet Rylander, Andreas
Persson, Liam
author_sort Rylander, Andreas
title Modelling the Impact of Drug Resistance on Treatment as Prevention as an HIV Control Strategy
title_short Modelling the Impact of Drug Resistance on Treatment as Prevention as an HIV Control Strategy
title_full Modelling the Impact of Drug Resistance on Treatment as Prevention as an HIV Control Strategy
title_fullStr Modelling the Impact of Drug Resistance on Treatment as Prevention as an HIV Control Strategy
title_full_unstemmed Modelling the Impact of Drug Resistance on Treatment as Prevention as an HIV Control Strategy
title_sort modelling the impact of drug resistance on treatment as prevention as an hiv control strategy
publisher KTH, Matematisk statistik
publishDate 2019
url http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254237
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