An Introduction to Infectious Disease Modelling
Mathematical models are increasingly being used to examine questions in infectious disease control. Applications include predicting the impact of vaccination strategies against common infections and determining optimal control strategies against HIV and pandemic influenza. This book introduces individuals interested in infectious diseases to this exciting and expanding area. The mathematical level of the book is kept as simple as possible, which makes the book accessible to those who have not studied mathematics to university level. Understanding is further enhanced by models that can be accessed online, which will allow readers to explore the impact of different factors and control strategies, and further adapt and develop the models themselves. The book is based on successful courses developed by the authors at the London School of Hygiene and Tropical Medicine. It will be of interest to epidemiologists, public health researchers, policy makers, veterinary scientists, medical statisticians and infectious disease researchers.
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3 How are models set up? II An introduction to differential equations
4 What do models tell us about the dynamics of infections?
5 Age patterns
6 An introduction to stochastic modelling
7 How do models deal with contact patterns?
8 Sexually transmitted infections
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activity groups age at infection age groups Anderson RM average force average number basic reproduction number become infectious calculate R0 cent Chapter children and adults cofactor cSTI difference equations differential equations effective contact Epidemiol epidemiology equals estimates example expression following the introduction force of infection given gonorrhoea herd immunity high-activity group HIV infection immunizing infections impact increases individuals become individuals mix randomly infectious disease infectious individuals infectious persons low-activity group mathematical matrix measles mixing patterns MMR vaccination mumps number of individuals number of infectious number of secondary number of susceptible obtain the following ofinfection Panel partner change rate partnerships period prevalence proportion of individuals random number rate of change result risk of infection rubella serial interval serotype sexual activity sexually transmitted diseases sexually transmitted infections step susceptible individuals tion total number totally susceptible population transmission dynamics transmission probability tuberculosis with-like mixing with-unlike