The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
Mathematical models of infectious disease transmission
Open accessAbstract
Mathematical analysis and modelling is central to infectious disease epidemiology. Here, we provide an intuitive introduction to the process of disease transmission, how this stochastic process can be represented mathematically and how this mathematical representation can be used to analyse the emergent dynamics of observed epidemics. Progress in mathematical analysis and modelling is of fundamental importance to our growing understanding of pathogen evolution and ecology. The fit of mathematical models to surveillance data has informed both scientific research and health policy. This Review is illustrated throughout by such applications and ends with suggestions of open challenges in mathematical epidemiology.
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
Model selection and multimodel inference: a practical information-theoretic approach.
Guthery, F. S., Burnham, K. P., & Anderson, D. R. (2003) · The Journal of Wildlife Management · doi:10.2307/3802723 · Cited by 41,495
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
Infectious Diseases of Humans: Dynamics and Control
(1992) · Annals of Internal Medicine · doi:10.7326/0003-4819-117-2-174_4 · Cited by 7,746
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
Burnham, K. P., & Anderson, D. R. (1998) · doi:10.1007/978-1-4757-2917-7 · Cited by 3,387
Superspreading and the effect of individual variation on disease emergence
Lloyd-Smith, J. O., Schreiber, S. J., Kopp, P. E., & Getz, W. M. (2005) · Nature · doi:10.1038/nature04153 · Cited by 2,514
Strategies for mitigating an influenza pandemic
Ferguson, N. M., Cummings, D. A. T., Fraser, C., Cajka, J. C., Cooley, P. C., & Burke, D. S. (2006) · Nature · doi:10.1038/nature04795 · Cited by 2,187
Self-citationModelling disease outbreaks in realistic urban social networks
Eubank, S., Guclu, H., Anil Kumar, V. S., Marathe, M. V., Srinivasan, A., Toroczkai, Z., & Wang, N. (2004) · Nature · doi:10.1038/nature02541 · Cited by 2,090
The Mathematical Theory of Infectious Diseases and its applications
Ling, N. R. (1978) · Immunology · Cited by 1,934
Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation
Diekmann, O., & Heesterbeek, H. (2000) · Socio-Environmental Systems Modeling · Cited by 1,833
Cited by
Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing
Ferretti, L., Wymant, C., Kendall, M., Zhao, L., Nurtay, A., Abeler-Dörner, L., Parker, M., Bonsall, D., & Fraser, C. (2020) · Science · doi:10.1126/science.abb6936 · Cited by 2,512
Self-citationInferring change points in the spread of COVID-19 reveals the effectiveness of interventions
Dehning, J., Zierenberg, J., Spitzner, F. P., Wibral, M., Neto, J. P., Wilczek, M., & Priesemann, V. (2020) · Science · doi:10.1126/science.abb9789 · Cited by 819
Small Proteins Can No Longer Be Ignored
Storz, G., Wolf, Y. I., & Ramamurthi, K. S. (2014) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-070611-102400 · Cited by 359
Sensitivity analysis of infectious disease models: methods, advances and their application
Wu, J., Dhingra, R., Gambhir, M., & Remais, J. V. (2013) · Journal of The Royal Society Interface · doi:10.1098/rsif.2012.1018 · Cited by 326
Using Mobile Phone Data to Predict the Spatial Spread of Cholera
Bengtsson, L., Gaudart, J., Lu, X., Moore, S., Wetter, E., Sallah, K., Rebaudet, S., & Piarroux, R. (2015) · Scientific Reports · doi:10.1038/srep08923 · Cited by 281
Metadata
- Authors
- Grassly, N. C., & Fraser, C.
- Venue
- Nature Reviews Microbiology
- Published
- 2008-05-13
- Volume
- 6
- Issue
- 6
- Pages
- 477-487
- ISSN
- 1740-1526 (ISSN-L)
- 1740-1534
- OpenAlex
- W1548037568
- Cited by
- 744
- References
- 95
- FWCI
- 3.18
- Type
- journal article
- Language
- en
- Open access
- bronze
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: Mathematical models of infectious disease transmission. https://app.cochat.ai/papers/10.1038/nrmicro1845
- The paper
- Grassly, N. C., & Fraser, C. (2008). Mathematical models of infectious disease transmission. Nature Reviews Microbiology. https://doi.org/10.1038/nrmicro1845
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.