The relationships between message passing, pairwise, Kermack–McKendrick and stochastic SIR epidemic models
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1406
- Type
- D - Journal article
- DOI
-
10.1007/s00285-017-1123-8
- Title of journal
- Journal of Mathematical Biology
- Article number
- -
- First page
- 1563
- Volume
- 75
- Issue
- 6-7
- ISSN
- 0303-6812
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2017
- 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
- Yes
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 10
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The work shows that many well-studied epidemic models can be derived from special cases of the recent ‘message passing’ representation of epidemics, which has the welcome effect of narrowing future lines of enquiry using such models. The work proves (1) that pairwise and message passing models do capture the underlying stochastic process more accurately than the famous Kermack-McKendrick model; (2) (for the first time) that a generalised message passing system has a unique feasible solution; and (3) that cycles in the contact network inhibit epidemic impact.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
