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Evaluating the performance of malaria genetics for inferring changes in transmission intensity using transmission modelling

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Title: Evaluating the performance of malaria genetics for inferring changes in transmission intensity using transmission modelling
Authors: Watson, O
Okell, L
Hellewell, J
Slater, H
Unwin, H
Omedo, I
Bejon, P
Snow, R
Noor, A
Rockett, K
Hubbart, C
Joaniter, N
Greenhouse, B
Chang, H-H
Ghani, A
Verity, A
Item Type: Journal Article
Abstract: Substantial progress has been made globally to control malaria, however there is a growing need for innovative new tools to ensure continued progress. One approach is to harness genetic sequencing and accompanying methodological approaches as have been used in the control of other infectious diseases. However, to utilise these methodologies for malaria we first need to extend the methods to capture the complex interactions between parasites, human and vector hosts, and environment, which all impact the level of genetic diversity and relatedness of malaria parasites. We develop an individual-based transmission model to simulate malaria parasite genetics parameterised using estimated relationships between complexity of infection and age from 5 regions in Uganda and Kenya. We predict that cotransmission and superinfection contribute equally to within-host parasite genetic diversity at 11.5% PCR prevalence, above which superinfections dominate. Finally, we characterise the predictive power of six metrics of parasite genetics for detecting changes in transmission intensity, before grouping them in an ensemble statistical model. The model predicted malaria prevalence with a mean absolute error of 0.055. Different assumptions about the availability of sample metadata were considered, with the most accurate predictions of malaria prevalence made when the clinical status and age of sampled individuals is known. Parasite genetics may provide a novel surveillance tool for estimating the prevalence of malaria in areas in which prevalence surveys are not feasible. However, the findings presented here reinforce the need for patient metadata to be recorded and made available within all future attempts to use parasite genetics for surveillance.
Issue Date: 8-Sep-2020
Date of Acceptance: 17-Aug-2020
URI: http://hdl.handle.net/10044/1/82367
DOI: 10.1093/molbev/msaa225
ISSN: 0737-4038
Publisher: Oxford University Press (OUP)
Start Page: 274
End Page: 289
Journal / Book Title: Molecular Biology and Evolution
Volume: 38
Issue: 1
Copyright Statement: © The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Sponsor/Funder: Wellcome Trust
The Royal Society
The Royal Society of Medicine
Medical Research Council (MRC)
Funder's Grant Number: 109312/Z/15/Z
DH140134
CH160084
MR/R015600/1
Keywords: genetics
malaria
modeling
surveillance
Evolutionary Biology
0601 Biochemistry and Cell Biology
0603 Evolutionary Biology
0604 Genetics
Publication Status: Published
Online Publication Date: 2020-09-08
Appears in Collections:Faculty of Medicine
Grantham Institute for Climate Change
School of Public Health
Faculty of Natural Sciences



This item is licensed under a Creative Commons License Creative Commons