Research Data Leeds Repository

Citation

Carruthers, Jonathan and Lythe, Grant and López-García, Martín and Gillard, Joseph J. and Laws, Thomas R. and Lukaszewski, Roman and Molina-París, Carmen (2020) Dataset associated with "Stochastic dynamics of Francisella Tularensis infection and replication". University of Leeds. [Dataset] https://doi.org/10.5518/792

Dataset description

Python codes associated with "Stochastic dynamics of Francisella Tularensis infection and replication"

Subjects:	G000 - Mathematical sciences > G100 - Mathematics
Divisions:	Faculty of Engineering and Physical Sciences > School of Mathematics
Related resources:	Location Type https://eprints.whiterose.ac.uk/158287/ Publication https://doi.org/10.1371/journal.pcbi.1007752 Publication
License:	Creative Commons Attribution 4.0 International (CC BY 4.0)
Date deposited:	22 May 2020 11:47
URI:	https://archive.researchdata.leeds.ac.uk/id/eprint/677
Additional details Additional details Creators or authors: Creators ORCID Other ID Email Primary affiliation Primary affiliation ID Primary affiliation ID type Secondary affiliation Secondary affiliation ID Secondary affiliation ID type Carruthers, Jonathan https://orcid.org/0000-0002-1372-3254 University of Leeds https://ror.org/024mrxd33 ror Lythe, Grant https://orcid.org/0000-0001-7966-5571 University of Leeds https://ror.org/024mrxd33 ror López-García, Martín https://orcid.org/0000-0003-3833-8595 University of Leeds https://ror.org/024mrxd33 ror Gillard, Joseph J. https://orcid.org/0000-0001-7486-8927 Defence Science and Technology Laboratory https://ror.org/04jswqb94 ror Laws, Thomas R. https://orcid.org/0000-0002-0142-9162 Defence Science and Technology Laboratory https://ror.org/04jswqb94 ror Lukaszewski, Roman https://orcid.org/0000-0003-1774-5335 Defence Science and Technology Laboratory https://ror.org/04jswqb94 ror Molina-París, Carmen https://orcid.org/0000-0001-9828-6737 University of Leeds https://ror.org/024mrxd33 ror Type of data: Dataset Research funders: Medical Research Council Grant numbers: MR/N014855/1 Publication date: 2020 Resource language: English Metadata language: English Publisher: University of Leeds Contact email: jonty.carruthers@btinternet.com Last Modified: 20 Dec 2024 12:50

Files

Documentation

README [2kB] README [2kB] README [2kB] Security: Anyone File name: README.txt Content type: Documentation Mime-Type: text/plain License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 2kB

Program

FT ABC [6kB] FT ABC [6kB] FT ABC [6kB] Security: Anyone File name: FT_ABC.py Content type: Program Description: Approximate Bayesian Computation rejection sampling algorithm to infer parameters of the agent-based model, from an in vivo study of Francisella tularensis infection in mice License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 6kB

FT Agent-based model [19kB] FT Agent-based model [19kB] FT Agent-based model [19kB] Security: Anyone File name: FT_Agent-based_model.py Content type: Program Description: Simulates realisations of the agent-based model License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 19kB

FT Cohort analysis [7kB] FT Cohort analysis [7kB] FT Cohort analysis [7kB] Security: Anyone File name: FT_Cohort_analysis.py Content type: Program Description: Uses the cohort analysis to reproduce the predictions in Fig 7 License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 7kB