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British Medical Bulletin Advance Access published online on October 24, 2009
British Medical Bulletin, doi:10.1093/bmb/ldp038
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© The Author 2009. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org

Mathematical modelling of infectious diseases

M. J. Keeling* and L. Danon

Biological Sciences, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK

* Correspondence to: Prof. M. J. Keeling, Biological Sciences, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK. E-mail: m.j.keeling{at}warwick.ac.uk

Introduction: Mathematical models allow us to extrapolate from current information about the state and progress of an outbreak, to predict the future and, most importantly, to quantify the uncertainty in these predictions. Here, we illustrate these principles in relation to the current H1N1 epidemic.

Sources of data: Many sources of data are used in mathematical modelling, with some forms of model requiring vastly more data than others. However, a good estimation of the number of cases is vitally important.

Areas of agreement: Mathematical models, and the statistical tools that underpin them, are now a fundamental element in planning control and mitigation measures against any future epidemic of an infectious disease. Well-parameterized mathematical models allow us to test a variety of possible control strategies in computer simulations before applying them in reality.

Areas of controversy: The interaction between modellers and public-health practitioners and the level of detail needed for models to be of use.

Growing points: The need for stronger statistical links between models and data.

Areas timely for developing research: Greater appreciation by the medical community of the uses and limitations of models and a greater appreciation by modellers of the constraints on public-health resources.

Keywords: modelling • swine flu • prediction • uncertainty

Accepted for publication September 24, 2009.


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