Epidemic Modelling : An Introduction (Cambridge Studies in Mathematical Biology)
Editorial Reviews
Review
'... It gives an excellent general introduction to epidemic modelling, starting with an interesting historical account and then describing the most important and useful models which can be used.' Robert MacMillan, Mathematical Gazette
'The book will be accessible ... and its study highly rewarding, to anyone with an interest in epidemic models...'. V. S. Isham, Short Book Reviews
'Daley and Gani's monograph is a concise and useful presentation of a variety of epidemiological models.' Daniel Haydon, Trends in Ecology and Evolution
Book Description
This general introduction to the mathematical techniques needed to understand epidemiology begins with an historical outline of some disease statistics dating from Daniel Bernoulli's smallpox data of 1760. The authors then go on to describe simple deterministic and stochastic models in continuous and discrete time for epidemics taking place in either homogeneous or stratified (nonhomogeneous) populations. They offer a range of methods for constructing and analyzing models, mostly in the context of viral and bacterial diseases of human populations. These models are contrasted with models for rumors and macro-parasitic diseases. Questions of fitting data to models, and the use of models to understand methods for controlling the spread of infection, are discussed. Exercises and complementary results at the end of each chapter extend the scope of the text.
Epidemic Modelling : An Introduction (Cambridge Studies in Mathematical Biology)
Epidemic Modelling: An Introduction (Cambridge Studies in Mathematical Biology),D. J. Daley,J. Gani,C. Cannings,F. C. Hoppensteadt,L. A. Segel,Cambridge University Press,0521640792,Epidemiology,Life Sciences - Biology - General,Mathematical Analysis,Mathematical Models In Biology,Mathematical models,Mathematics,Medical,Public Health,Science/Mathematics,Statistical methods,Epidemiology--Mathematical models,Human biology & related topics,Mathematics / Statistics,Probability & statistics,Public health & preventive medicine
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