Mathematical Tools for Understanding Infectious Disease Dynamics
Regular price
€117.99
A01=Hans Heesterbeek
A01=Odo Diekmann
A01=Tom Britton
Addition
Almost surely
Approximation
Author_Hans Heesterbeek
Author_Odo Diekmann
Author_Tom Britton
Basic reproduction number
Binomial distribution
Birth rate
Branching process
Calculation
Category=MBNS
Category=PBWH
Computation
Contact process (mathematics)
Continuous-time Markov chain
Demography
Differential equation
Disease
Eigenvalues and eigenvectors
Elaboration
Epidemic
Epidemic model
Epidemiology
eq_isMigrated=1
eq_nobargain
Equation
Ergodic theory
Estimation
Estimator
Expected value
Exponential distribution
Exponential function
Exponential growth
Force of infection
Infectious period
Infectivity
Inference
Ingredient
Initial condition
Law of mass action
Life expectancy
Likelihood function
Linearization
Markov chain
Markov property
Monotonic function
Mortality rate
Next-generation matrix
Normal distribution
Notation
Ordinary differential equation
Parameter
Parasite load
Poisson distribution
Poisson point process
Population dynamics
Prevalence
Probability
Probability distribution
Proportionality (mathematics)
Quantity
Random variable
Scientific notation
Sign (mathematics)
Special case
Standard deviation
Statistical population
Stochastic
Stochastic calculus
Stochastic process
Subset
Summation
Symptom
Theory
Vaccination
Variance
Product details
- ISBN 9780691155395
- Weight: 1162g
- Dimensions: 178 x 254mm
- Publication Date: 18 Nov 2012
- Publisher: Princeton University Press
- Publication City/Country: US
- Product Form: Hardback
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Mathematical modeling is critical to our understanding of how infectious diseases spread at the individual and population levels. This book gives readers the necessary skills to correctly formulate and analyze mathematical models in infectious disease epidemiology, and is the first treatment of the subject to integrate deterministic and stochastic models and methods. Mathematical Tools for Understanding Infectious Disease Dynamics fully explains how to translate biological assumptions into mathematics to construct useful and consistent models, and how to use the biological interpretation and mathematical reasoning to analyze these models. It shows how to relate models to data through statistical inference, and how to gain important insights into infectious disease dynamics by translating mathematical results back to biology. This comprehensive and accessible book also features numerous detailed exercises throughout; full elaborations to all exercises are provided.
* Covers the latest research in mathematical modeling of infectious disease epidemiology * Integrates deterministic and stochastic approaches * Teaches skills in model construction, analysis, inference, and interpretation * Features numerous exercises and their detailed elaborations * Motivated by real-world applications throughout
Odo Diekmann is professor of mathematical analysis at Utrecht University. Hans Heesterbeek is professor of theoretical epidemiology at Utrecht University. Tom Britton is professor of mathematical statistics at Stockholm University.
