Modelling the dynamics of an experimental host-pathogen microcosm within a hierarchical Bayesian framework.

The advantages of Bayesian statistical approaches, such as flexibility and the ability to acknowledge uncertainty in all parameters, have made them the prevailing method for analysing the spread of infectious diseases in human or animal populations. We introduce a Bayesian approach to experimental h...

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Autores principales:	David Lunn, Robert J B Goudie, Chen Wei, Oliver Kaltz, Olivier Restif
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0269f8477c1a4e6cbcafd8e5cbaa2403
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spelling	oai:doaj.org-article:0269f8477c1a4e6cbcafd8e5cbaa24032021-11-18T09:01:33ZModelling the dynamics of an experimental host-pathogen microcosm within a hierarchical Bayesian framework.1932-620310.1371/journal.pone.0069775https://doaj.org/article/0269f8477c1a4e6cbcafd8e5cbaa24032013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23936351/?tool=EBIhttps://doaj.org/toc/1932-6203The advantages of Bayesian statistical approaches, such as flexibility and the ability to acknowledge uncertainty in all parameters, have made them the prevailing method for analysing the spread of infectious diseases in human or animal populations. We introduce a Bayesian approach to experimental host-pathogen systems that shares these attractive features. Since uncertainty in all parameters is acknowledged, existing information can be accounted for through prior distributions, rather than through fixing some parameter values. The non-linear dynamics, multi-factorial design, multiple measurements of responses over time and sampling error that are typical features of experimental host-pathogen systems can also be naturally incorporated. We analyse the dynamics of the free-living protozoan Paramecium caudatum and its specialist bacterial parasite Holospora undulata. Our analysis provides strong evidence for a saturable infection function, and we were able to reproduce the two waves of infection apparent in the data by separating the initial inoculum from the parasites released after the first cycle of infection. In addition, the parameter estimates from the hierarchical model can be combined to infer variations in the parasite's basic reproductive ratio across experimental groups, enabling us to make predictions about the effect of resources and host genotype on the ability of the parasite to spread. Even though the high level of variability between replicates limited the resolution of the results, this Bayesian framework has strong potential to be used more widely in experimental ecology.David LunnRobert J B GoudieChen WeiOliver KaltzOlivier RestifPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 8, p e69775 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q David Lunn Robert J B Goudie Chen Wei Oliver Kaltz Olivier Restif Modelling the dynamics of an experimental host-pathogen microcosm within a hierarchical Bayesian framework.
description	The advantages of Bayesian statistical approaches, such as flexibility and the ability to acknowledge uncertainty in all parameters, have made them the prevailing method for analysing the spread of infectious diseases in human or animal populations. We introduce a Bayesian approach to experimental host-pathogen systems that shares these attractive features. Since uncertainty in all parameters is acknowledged, existing information can be accounted for through prior distributions, rather than through fixing some parameter values. The non-linear dynamics, multi-factorial design, multiple measurements of responses over time and sampling error that are typical features of experimental host-pathogen systems can also be naturally incorporated. We analyse the dynamics of the free-living protozoan Paramecium caudatum and its specialist bacterial parasite Holospora undulata. Our analysis provides strong evidence for a saturable infection function, and we were able to reproduce the two waves of infection apparent in the data by separating the initial inoculum from the parasites released after the first cycle of infection. In addition, the parameter estimates from the hierarchical model can be combined to infer variations in the parasite's basic reproductive ratio across experimental groups, enabling us to make predictions about the effect of resources and host genotype on the ability of the parasite to spread. Even though the high level of variability between replicates limited the resolution of the results, this Bayesian framework has strong potential to be used more widely in experimental ecology.
format	article
author	David Lunn Robert J B Goudie Chen Wei Oliver Kaltz Olivier Restif
author_facet	David Lunn Robert J B Goudie Chen Wei Oliver Kaltz Olivier Restif
author_sort	David Lunn
title	Modelling the dynamics of an experimental host-pathogen microcosm within a hierarchical Bayesian framework.
title_short	Modelling the dynamics of an experimental host-pathogen microcosm within a hierarchical Bayesian framework.
title_full	Modelling the dynamics of an experimental host-pathogen microcosm within a hierarchical Bayesian framework.
title_fullStr	Modelling the dynamics of an experimental host-pathogen microcosm within a hierarchical Bayesian framework.
title_full_unstemmed	Modelling the dynamics of an experimental host-pathogen microcosm within a hierarchical Bayesian framework.
title_sort	modelling the dynamics of an experimental host-pathogen microcosm within a hierarchical bayesian framework.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/0269f8477c1a4e6cbcafd8e5cbaa2403
work_keys_str_mv	AT davidlunn modellingthedynamicsofanexperimentalhostpathogenmicrocosmwithinahierarchicalbayesianframework AT robertjbgoudie modellingthedynamicsofanexperimentalhostpathogenmicrocosmwithinahierarchicalbayesianframework AT chenwei modellingthedynamicsofanexperimentalhostpathogenmicrocosmwithinahierarchicalbayesianframework AT oliverkaltz modellingthedynamicsofanexperimentalhostpathogenmicrocosmwithinahierarchicalbayesianframework AT olivierrestif modellingthedynamicsofanexperimentalhostpathogenmicrocosmwithinahierarchicalbayesianframework
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Modelling the dynamics of an experimental host-pathogen microcosm within a hierarchical Bayesian framework.

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