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Integration of parasite genetic information in malaria transmission modelling

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Title: Integration of parasite genetic information in malaria transmission modelling
Authors: Watson, Oliver
Item Type: Thesis or dissertation
Abstract: Mathematical models of malaria transmission are increasingly used to quantify the impact of malaria control efforts and to assist in the development and costing of future initiatives such as the WHO Global Technical Strategy for Malaria 2016-2030. These models have highlighted both the progress made so far, but also how continued investment is needed to reach the milestones required. However, the increase in global malaria cases reported in 2018 suggests that new tools may be required to continue the gains made and to address the growing risk of antimalarial resistance threatening to reverse the recent declines in malaria burden. The proliferation of genetic sequencing and the publication of the Plasmodium falciparum reference genome in 2002 has facilitated a greater understanding of the genetic determinants of resistance and molecular tools are subsequently poised to become a routine tool for malaria control. Consequently, integrating parasite genetic information into established models of malaria transmission models can contribute to both our understanding of the drivers and optimum policies for addressing resistance and detailing the potential of molecular tools within malaria control. Plasmodium falciparum is known to have evolved several times in response to first line antimalarials. However, recent evidence has shown evolution to rapid diagnostic tests. The WHO has consequently issued guidance advising national malaria control programmes to conduct surveillance for pfhrp2/3 deletions. The timing of this policy recommendation and my previous work modelling pfhrp2 deletions necessitated a timely extension of our previous model to evaluate the implications of seasonality in malaria transmission on estimates of the prevalence of pfhrp2/3 deletions. Recent studies have suggested that malaria genotyping could be a useful tool for epidemiological surveillance. By developing an extended version of an established model of malaria transmission, which now models individual mosquitoes affording the full parasite life cycle to be represented, I characterise the potential utility of malaria genomics for inferring changes in transmission intensity. I conclude that although molecular tools could enable accurate estimation of malaria prevalence, greater attention needs to be placed on the chosen sampling scheme, recording patient metadata and developing the statistical toolkit for analysing polyclonal infected individuals. In 2015, health ministers in the Greater Mekong Subregion (GMS) adopted the WHO strategy for malaria elimination in the GMS 2016-2030. The strategy was developed to accelerate elimination in South-East Asia, which is currently the best approach to address the growing threat of artemisinin resistance and the emergence of multidrug resistant parasite lineages. In response, I demonstrate how the therapeutic lifespan of the five currently recommended artemisinin combination therapies can be prolonged by reducing antimalarial overprescription by ensuring that all suspected malaria fevers are tested before administering antimalarials. I conclude by comparing different cycling and mixing strategies before reviewing how each strategy can be improved to slow the spread of antimalarial resistance. Elimination in the GMS is undoubtedly an effective mechanism for preventing the spread of artemisinin resistance to Africa. However, if efforts to eliminate by 2030 have failed it will be imperative to understand the mechanisms with which resistance may continue to spread. To this extent, the capability of resistant strains to invade susceptible populations is evaluated using data from standard membrane feeding assays. Findings are incorporated in the transmission model to quantify the transmission advantage of artemisinin resistance at the population level.
Content Version: Open Access
Issue Date: Sep-2019
Date Awarded: Mar-2020
URI: http://hdl.handle.net/10044/1/97671
DOI: https://doi.org/10.25560/97671
Copyright Statement: Creative Commons Attribution ShareAlike Licence
Supervisor: Ghani, Azra
Okell, Lucy
Verity, Robert
Sponsor/Funder: Wellcome Trust (London, England)
Funder's Grant Number: 109312/Z/15/Z
Department: School of Public Health
Publisher: Imperial College London
Qualification Level: Doctoral
Qualification Name: Doctor of Philosophy (PhD)
Appears in Collections:Life Sciences PhD theses



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