@article{Watson:2020:molbev/msaa225,
author = {Watson, O and Okell, L and Hellewell, J and Slater, H and Unwin, H and Omedo, I and Bejon, P and Snow, R and Noor, A and Rockett, K and Hubbart, C and Joaniter, N and Greenhouse, B and Chang, H-H and Ghani, A and Verity, A},
doi = {molbev/msaa225},
journal = {Molecular Biology and Evolution},
pages = {274--289},
title = {Evaluating the performance of malaria genetics for inferring changes in transmission intensity using transmission modelling},
url = {http://dx.doi.org/10.1093/molbev/msaa225},
volume = {38},
year = {2020}
}

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TY  - JOUR
AB  - 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.
AU  - Watson,O
AU  - Okell,L
AU  - Hellewell,J
AU  - Slater,H
AU  - Unwin,H
AU  - Omedo,I
AU  - Bejon,P
AU  - Snow,R
AU  - Noor,A
AU  - Rockett,K
AU  - Hubbart,C
AU  - Joaniter,N
AU  - Greenhouse,B
AU  - Chang,H-H
AU  - Ghani,A
AU  - Verity,A
DO  - molbev/msaa225
EP  - 289
PY  - 2020///
SN  - 0737-4038
SP  - 274
TI  - Evaluating the performance of malaria genetics for inferring changes in transmission intensity using transmission modelling
T2  - Molecular Biology and Evolution
UR  - http://dx.doi.org/10.1093/molbev/msaa225
UR  - https://academic.oup.com/mbe/article/38/1/274/5902837
UR  - http://hdl.handle.net/10044/1/82367
VL  - 38
ER  - 

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