The Impacts of Low Diversity Sequence Data on Phylodynamic Inference during an Emerging Epidemic

Phylodynamic inference is a pivotal tool in understanding transmission dynamics of viral outbreaks. These analyses are strongly guided by the input of an epidemiological model as well as sequence data that must contain sufficient intersequence variability in order to be informative. These criteria,...

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Main Authors: Anthony Lam, Sebastian Duchene
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Viruses
Subjects:
Online Access:https://www.mdpi.com/1999-4915/13/1/79
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author Anthony Lam
Sebastian Duchene
author_facet Anthony Lam
Sebastian Duchene
author_sort Anthony Lam
collection DOAJ
description Phylodynamic inference is a pivotal tool in understanding transmission dynamics of viral outbreaks. These analyses are strongly guided by the input of an epidemiological model as well as sequence data that must contain sufficient intersequence variability in order to be informative. These criteria, however, may not be met during the early stages of an outbreak. Here we investigate the impact of low diversity sequence data on phylodynamic inference using the birth–death and coalescent exponential models. Through our simulation study, estimating the molecular evolutionary rate required enough sequence diversity and is an essential first step for any phylodynamic inference. Following this, the birth–death model outperforms the coalescent exponential model in estimating epidemiological parameters, when faced with low diversity sequence data due to explicitly exploiting the sampling times. In contrast, the coalescent model requires additional samples and therefore variability in sequence data before accurate estimates can be obtained. These findings were also supported through our empirical data analyses of an Australian and a New Zealand cluster outbreaks of SARS-CoV-2. Overall, the birth–death model is more robust when applied to datasets with low sequence diversity given sampling is specified and this should be considered for future viral outbreak investigations.
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spelling doaj.art-13aeda2f0f5f4dc4a0a78e64b6abae082023-12-03T12:30:44ZengMDPI AGViruses1999-49152021-01-011317910.3390/v13010079The Impacts of Low Diversity Sequence Data on Phylodynamic Inference during an Emerging EpidemicAnthony Lam0Sebastian Duchene1Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC 3010, AustraliaDepartment of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC 3010, AustraliaPhylodynamic inference is a pivotal tool in understanding transmission dynamics of viral outbreaks. These analyses are strongly guided by the input of an epidemiological model as well as sequence data that must contain sufficient intersequence variability in order to be informative. These criteria, however, may not be met during the early stages of an outbreak. Here we investigate the impact of low diversity sequence data on phylodynamic inference using the birth–death and coalescent exponential models. Through our simulation study, estimating the molecular evolutionary rate required enough sequence diversity and is an essential first step for any phylodynamic inference. Following this, the birth–death model outperforms the coalescent exponential model in estimating epidemiological parameters, when faced with low diversity sequence data due to explicitly exploiting the sampling times. In contrast, the coalescent model requires additional samples and therefore variability in sequence data before accurate estimates can be obtained. These findings were also supported through our empirical data analyses of an Australian and a New Zealand cluster outbreaks of SARS-CoV-2. Overall, the birth–death model is more robust when applied to datasets with low sequence diversity given sampling is specified and this should be considered for future viral outbreak investigations.https://www.mdpi.com/1999-4915/13/1/79phylodynamicsSARS-CoV-2Bayesian phylogeneticsbirth–deathcoalescent
spellingShingle Anthony Lam
Sebastian Duchene
The Impacts of Low Diversity Sequence Data on Phylodynamic Inference during an Emerging Epidemic
Viruses
phylodynamics
SARS-CoV-2
Bayesian phylogenetics
birth–death
coalescent
title The Impacts of Low Diversity Sequence Data on Phylodynamic Inference during an Emerging Epidemic
title_full The Impacts of Low Diversity Sequence Data on Phylodynamic Inference during an Emerging Epidemic
title_fullStr The Impacts of Low Diversity Sequence Data on Phylodynamic Inference during an Emerging Epidemic
title_full_unstemmed The Impacts of Low Diversity Sequence Data on Phylodynamic Inference during an Emerging Epidemic
title_short The Impacts of Low Diversity Sequence Data on Phylodynamic Inference during an Emerging Epidemic
title_sort impacts of low diversity sequence data on phylodynamic inference during an emerging epidemic
topic phylodynamics
SARS-CoV-2
Bayesian phylogenetics
birth–death
coalescent
url https://www.mdpi.com/1999-4915/13/1/79
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