Infectious disease phylodynamics with occurrence data

Featherstone, Leo; Giallonardo, Francesca Di; Holmes, Edward C.; Vaughan, Timothy G.

doi:10.1101/596700

Cited by 5 publications

(5 citation statements)

References 39 publications

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“…Here, we explore the possibility of for compensating the effects of sampling bias in BMP by adding "sequence-free" samples to the analyses. This is representative of the case, for example, that we know that an outbreak has spread into a location, and we know the time and place of some of the cases in that location, but we cannot collect or sequence samples from those cases; so, some of the samples will be "proper", that is, will encompass genetic sequences, while the other "sequence-free" samples will have sampling location and time, but no genetic sequence (see also (37,38)).…”

Section: Resultsmentioning

confidence: 99%

Sampling bias and model choice in continuous phylogeography: getting lost on a random walk

Kalkauskas

Perron

Sun

et al. 2020

Preprint

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‡ These authors contributed equally to this work Phylogeographic inference allows reconstruction of past geographical spread of pathogens or living organisms by integrating genetic and geographic data. A popular model in continuous phylogeography -with location data provided in the form of latitude and longitude coordinates -describes spread as a Brownian motion (Brownian Motion Phylogeography, BMP) in continuous space and time, akin to similar models of continuous trait evolution. Here, we show that reconstructions using this model can be strongly affected by sampling biases, such as the lack of sampling from certain areas. As an attempt to reduce the effects of sampling bias on BMP, we consider the addition of sequence-free samples from under-sampled areas. While this approach alleviates the effects of sampling bias, in most scenarios this will not be a viable option due to the need for prior knowledge of an outbreak's spatial distribution. We therefore consider an alternative model, the spatial Λ-Fleming-Viot process (ΛFV), which has recently gained popularity in population genetics. Despite the ΛFV's robustness to sampling biases, we find that the different assumptions of the ΛFV and BMP models result in different applicabilities, with the ΛFV being more appropriate for scenarios of endemic spread, and BMP being more appropriate for recent outbreaks or colonizations. Phylogeography | Phylogenetics | Migration | Phylodynamics | Population Genetics | Epidemiology | Molecular EpidemiologyCorrespondence: demaio@ebi.ac.uk

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Section: Resultsmentioning

confidence: 99%

Sampling bias and model choice in continuous phylogeography: getting lost on a random walk

Kalkauskas

Perron

Sun

et al. 2020

Preprint

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“…Overall, when performing Bayesian phylodynamic analyses during emerging epidemics, researchers should take careful consideration in selecting the model. Our findings suggest the birth–death model is more robust when faced with data with low sequence diversity, given that the sampling process is correctly specified, unlike the coalescent model which required considerably more intersequence variability to improve performance [ 31 ]. A limitation of our study is that we generated a phylogenetic tree representing a cluster event with constant sampling over time and our empirical data analyses were also intensely sampled over time within a single cluster.…”

Section: Discussionmentioning

confidence: 99%

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

Lam

Duchêne

2021

Viruses

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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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“…A more flexible alternative is offered by skyline methods that can allow the epidemic growth rate, population size and other parameters to vary across different time intervals [116]. Recent developments in skyline techniques that can explicitly model the sampling process [117] and those that allow the inclusion of occurrence data with low-quality or no sequence information [118] offer promising avenues for coherent phylodynamic analyses of ancient DNA. Further research into the performance of these methods is needed to assess their accuracy in reconstructing past epidemics.…”

Section: Pathogen Phylogeography and Phylodynamicsmentioning

confidence: 99%

The Recovery, Interpretation and Use of Ancient Pathogen Genomes

Duchêne

Carmichael

et al. 2020

Current Biology

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The ability to sequence genomes from ancient biological material has provided a rich source of information for evolutionary biology and engaged considerable public interest. Although most studies of ancient genomes have focused on vertebrates, particularly archaic humans, newer technologies allow the capture of microbial pathogens and microbiomes from ancient and historical human and non-human remains. This coming of age has been made possible by techniques that allow the preferential capture and amplification of discrete genomes from a background of predominantly host and environmental DNA. There are now near-complete ancient genome sequences for three pathogens of considerable historical interest-premodern bubonic plague (Yersinia pestis), smallpox (Variola virus) and cholera (Vibrio cholerae)-and for three equally important endemic human disease agents-Mycobacterium tuberculosis (tuberculosis), Mycobacterium leprae (leprosy) and Treponema pallidum pallidum (syphilis). Genomic data from these pathogens have extended earlier work by paleopathologists. There have been efforts to sequence the genomes of additional ancient pathogens, with the potential to broaden our understanding of the infectious disease burden common to past populations from the Bronze Age to the early 20 th century. In this review we describe the state-of-the-art of this rapidly developing field, highlight the contributions of ancient pathogen genomics to multidisciplinary endeavors and describe some of the limitations in resolving questions about the emergence and long-term evolution of pathogens.

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Infectious disease phylodynamics with occurrence data

Cited by 5 publications

References 39 publications

Sampling bias and model choice in continuous phylogeography: getting lost on a random walk

Sampling bias and model choice in continuous phylogeography: getting lost on a random walk

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

The Recovery, Interpretation and Use of Ancient Pathogen Genomes

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