A Bayesian model of acquisition and clearance of bacterial colonization

Research output: Other contributionScientificpeer-review

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A Bayesian model of acquisition and clearance of bacterial colonization. / Järvenpää, Marko; Sater, Mohamad; Lagoudas, Georgia; Blainey, Paul C.; Miller, Loren; McKinnell, James; Huang, Susan; Grad, Yonatan; Marttinen, Pekka.

2018, Workshop paper in ML4H: Machine Learning for Health NeurIPS workshop.

Research output: Other contributionScientificpeer-review

Harvard

Järvenpää, M, Sater, M, Lagoudas, G, Blainey, PC, Miller, L, McKinnell, J, Huang, S, Grad, Y & Marttinen, P 2018, A Bayesian model of acquisition and clearance of bacterial colonization..

APA

Järvenpää, M., Sater, M., Lagoudas, G., Blainey, P. C., Miller, L., McKinnell, J., ... Marttinen, P. (2018, Dec). A Bayesian model of acquisition and clearance of bacterial colonization.

Vancouver

Järvenpää M, Sater M, Lagoudas G, Blainey PC, Miller L, McKinnell J et al. A Bayesian model of acquisition and clearance of bacterial colonization. 2018.

Author

Järvenpää, Marko ; Sater, Mohamad ; Lagoudas, Georgia ; Blainey, Paul C. ; Miller, Loren ; McKinnell, James ; Huang, Susan ; Grad, Yonatan ; Marttinen, Pekka. / A Bayesian model of acquisition and clearance of bacterial colonization. 2018.

Bibtex - Download

@misc{c913770bd8c844df878ae3c9eff5d9fa,
title = "A Bayesian model of acquisition and clearance of bacterial colonization",
abstract = "Bacterial populations that colonize a host play important roles in host health, including serving as a reservoir that transmits to other hosts and from which invasive strains emerge, thus emphasizing the importance of understanding rates of acquisition and clearance of colonizing populations. Studies of colonization dynamics have been based on assessment of whether serial samples represent a single population or distinct colonization events. A common solution to estimate acquisition and clearance rates is to use a fixed genetic distance threshold. However, this approach is often inadequate to account for the diversity of the underlying within-host evolving population, the time intervals between consecutive measurements, and the uncertainty in the estimated acquisition and clearance rates. Here, we summarize recently submitted work \cite{jarvenpaa2018named} and present a Bayesian model that provides probabilities of whether two strains should be considered the same, allowing to determine bacterial clearance and acquisition from genomes sampled over time. We explicitly model the within-host variation using population genetic simulation, and the inference is done by combining information from multiple data sources by using a combination of Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC). We use the method to analyse a collection of methicillin resistant Staphylococcus aureus (MRSA) isolates.",
author = "Marko J{\"a}rvenp{\"a}{\"a} and Mohamad Sater and Georgia Lagoudas and Blainey, {Paul C.} and Loren Miller and James McKinnell and Susan Huang and Yonatan Grad and Pekka Marttinen",
year = "2018",
month = "12",
language = "English",
type = "Other",

}

RIS - Download

TY - GEN

T1 - A Bayesian model of acquisition and clearance of bacterial colonization

AU - Järvenpää, Marko

AU - Sater, Mohamad

AU - Lagoudas, Georgia

AU - Blainey, Paul C.

AU - Miller, Loren

AU - McKinnell, James

AU - Huang, Susan

AU - Grad, Yonatan

AU - Marttinen, Pekka

PY - 2018/12

Y1 - 2018/12

N2 - Bacterial populations that colonize a host play important roles in host health, including serving as a reservoir that transmits to other hosts and from which invasive strains emerge, thus emphasizing the importance of understanding rates of acquisition and clearance of colonizing populations. Studies of colonization dynamics have been based on assessment of whether serial samples represent a single population or distinct colonization events. A common solution to estimate acquisition and clearance rates is to use a fixed genetic distance threshold. However, this approach is often inadequate to account for the diversity of the underlying within-host evolving population, the time intervals between consecutive measurements, and the uncertainty in the estimated acquisition and clearance rates. Here, we summarize recently submitted work \cite{jarvenpaa2018named} and present a Bayesian model that provides probabilities of whether two strains should be considered the same, allowing to determine bacterial clearance and acquisition from genomes sampled over time. We explicitly model the within-host variation using population genetic simulation, and the inference is done by combining information from multiple data sources by using a combination of Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC). We use the method to analyse a collection of methicillin resistant Staphylococcus aureus (MRSA) isolates.

AB - Bacterial populations that colonize a host play important roles in host health, including serving as a reservoir that transmits to other hosts and from which invasive strains emerge, thus emphasizing the importance of understanding rates of acquisition and clearance of colonizing populations. Studies of colonization dynamics have been based on assessment of whether serial samples represent a single population or distinct colonization events. A common solution to estimate acquisition and clearance rates is to use a fixed genetic distance threshold. However, this approach is often inadequate to account for the diversity of the underlying within-host evolving population, the time intervals between consecutive measurements, and the uncertainty in the estimated acquisition and clearance rates. Here, we summarize recently submitted work \cite{jarvenpaa2018named} and present a Bayesian model that provides probabilities of whether two strains should be considered the same, allowing to determine bacterial clearance and acquisition from genomes sampled over time. We explicitly model the within-host variation using population genetic simulation, and the inference is done by combining information from multiple data sources by using a combination of Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC). We use the method to analyse a collection of methicillin resistant Staphylococcus aureus (MRSA) isolates.

UR - https://ml4health.github.io/2018/pages/papers.html

M3 - Other contribution

ER -

ID: 31096552