The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Edge-based compartmental modelling for infectious disease spread
Open accessAbstract
The primary tool for predicting infectious disease spread and intervention effectiveness is the mass action susceptible–infected–recovered model of Kermack & McKendrick. Its usefulness derives largely from its conceptual and mathematical simplicity; however, it incorrectly assumes that all individuals have the same contact rate and partnerships are fleeting. In this study, we introduce edge-based compartmental modelling, a technique eliminating these assumptions. We derive simple ordinary differential equation models capturing social heterogeneity (heterogeneous contact rates) while explicitly considering the impact of partnership duration. We introduce a graphical interpretation allowing for easy derivation and communication of the model and focus on applying the technique under different assumptions about how contact rates are distributed and how long partnerships last.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
A note on two problems in connexion with graphs
Dijkstra, E. W. (1959) · Numerische Mathematik · doi:10.1007/bf01386390 · Cited by 24,043
A contribution to the mathematical theory of epidemics
Kermack, W. O., & McKendrick, A. G. (1927) · Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character · doi:10.1098/rspa.1927.0118 · Cited by 11,701
Exact stochastic simulation of coupled chemical reactions
Gillespie, D. T. (1977) · The Journal of Physical Chemistry · doi:10.1021/j100540a008 · Cited by 10,136
Infectious Diseases of Humans: Dynamics and Control
Anderson, R. M., & May, R. M. (1991) · doi:10.1093/oso/9780198545996.001.0001 · Cited by 6,179
Epidemic Spreading in Scale-Free Networks
Pastor-Satorras, R., & Vespignani, A. (2001) · Physical Review Letters · doi:10.1103/physrevlett.86.3200 · Cited by 5,731
Diekmann, O., Heesterbeek, J. A. P., & Metz, J. A. J. (1990) · Journal of Mathematical Biology · doi:10.1007/bf00178324 · Cited by 5,091
Random graphs with arbitrary degree distributions and their applications
Newman, M. E. J., Strogatz, S. H., & Watts, D. J. (2001) · Physical Review E · doi:10.1103/physreve.64.026118 · Cited by 3,648
Spread of epidemic disease on networks
Newman, M. E. J. (2002) · Physical Review E · doi:10.1103/physreve.66.016128 · Cited by 3,218
Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases
Mossong, J., Hens, N., Jit, M., Beutels, P., Auranen, K., Mikolajczyk, R., Massari, M., Salmaso, S., Tomba, G. S., Wallinga, J., et al. (2008) · PLoS Medicine · doi:10.1371/journal.pmed.0050074 · Cited by 2,900
Cited by
Epidemic processes in complex networks
Pastor-Satorras, R., Castellano, C., Van Mieghem, P., & Vespignani, A. (2015) · Reviews of Modern Physics · doi:10.1103/revmodphys.87.925 · Cited by 3,697
Unification of theoretical approaches for epidemic spreading on complex networks
Wang, W., Tang, M., Eugene Stanley, H., & Braunstein, L. A. (2017) · Reports on Progress in Physics · doi:10.1088/1361-6633/aa5398 · Cited by 366
Metadata
- Authors
- Miller, J. C., Slim, A. C., & Volz, E. M.
- Venue
- Journal of The Royal Society Interface
- Publisher
- The Royal Society
- Published
- 2011-10-05
- Volume
- 9
- Issue
- 70
- Pages
- 890-906
- ISSN
- 1742-5662 (ISSN-L)
- 1742-5689
- OpenAlex
- W2115040920
- Cited by
- 278
- References
- 60
- FWCI
- 10.58
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Edge-based compartmental modelling for infectious disease spread. https://app.cochat.ai/papers/10.1098/rsif.2011.0403
- The paper
- Miller, J. C., Slim, A. C., & Volz, E. M. (2012). Edge-based compartmental modelling for infectious disease spread. Journal of The Royal Society Interface. https://doi.org/10.1098/rsif.2011.0403
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
