Mathematical modelling of infectious diseases.
BACKGROUND
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.


METHODS
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.


RESULTS
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.


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


CONCLUSIONS
The need for stronger statistical links between models and data.


CONCLUSIONS
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.
Keeling
M J
MJ
Biological Sciences, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK. m.j.keeling@warwick.ac.uk
Danon
L
L
D003141:Communicable Diseases / Q000453:epidemiology*; D004196:Disease Outbreaks; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype; D007251:Influenza, Human / Q000453:epidemiology* / Q000517:prevention & control; D008954:Models, Biological; D012306:Risk