An Agent-Based Lattice Model for the Emergence of Anti-Microbial Resistance
- Submitting institution
-
Aston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 38249446
- Type
- D - Journal article
- DOI
-
10.1016/j.jtbi.2019.110080
- Title of journal
- Journal of Theoretical Biology
- Article number
- 110080
- First page
- -
- Volume
- 486
- Issue
- -
- ISSN
- 0022-5193
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
0
- Research group(s)
-
A - Aston Institute of Urban Technology and the Environment (ASTUTE)
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Combining techniques from statistical physics, agent-based simulation and machine learning, this work offers a new method to understand the dynamics of how antibiotic resistance can evolve. The developed method has led to a collaboration with Aston University's College of Health and Life Sciences, with real experiments done (awarded a grant of £2000) generating data that is currently being analysed. A PhD project has been designed and a student is currently being sought. The theme has great potential for generating grants since it falls under the research area ‘Statistics and Applied Probability’ of EPSRC’s portfolio, currently marked for growth.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
