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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2013-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0069775 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850212754862899200 |
|---|---|
| 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 |
| collection | DOAJ |
| 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 |
| id | doaj-art-0269f8477c1a4e6cbcafd8e5cbaa2403 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-0269f8477c1a4e6cbcafd8e5cbaa24032025-08-20T02:09:17ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0188e6977510.1371/journal.pone.0069775Modelling the dynamics of an experimental host-pathogen microcosm within a hierarchical Bayesian framework.David LunnRobert J B GoudieChen WeiOliver KaltzOlivier RestifThe 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.https://doi.org/10.1371/journal.pone.0069775 |
| spellingShingle | 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. PLoS ONE |
| title | 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_short | 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 |
| url | https://doi.org/10.1371/journal.pone.0069775 |
| work_keys_str_mv | AT davidlunn modellingthedynamicsofanexperimentalhostpathogenmicrocosmwithinahierarchicalbayesianframework AT robertjbgoudie modellingthedynamicsofanexperimentalhostpathogenmicrocosmwithinahierarchicalbayesianframework AT chenwei modellingthedynamicsofanexperimentalhostpathogenmicrocosmwithinahierarchicalbayesianframework AT oliverkaltz modellingthedynamicsofanexperimentalhostpathogenmicrocosmwithinahierarchicalbayesianframework AT olivierrestif modellingthedynamicsofanexperimentalhostpathogenmicrocosmwithinahierarchicalbayesianframework |