The Dynamics of Infectious Diseases
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The Dynamics of Infectious Diseases

Modelling Epidemiology with R

About the Book

Epidemiology is complex. To understand its dynamics, and thus to be able control infectious disease, we must link what we know about the biology of the disease – its transmission and its effect on the host – into mathematical models describing how epidemiology works. This book is developed as a tutorial that will help you to understand and perform this process.

It will show you why you should understand dynamic processes by discussing simple epidemiological examples. You will learn how to model simple epidemiological dynamics and how to measure the parameters of these epidemiological systems. You will not only understand the theoretical basis of these aspects, but learn to program them with R, a software package that is becoming the standard in academic research.

About the Author

Jacob Koella
Jacob Koella

Jacob Koella is a professor of evolutionary biology and parasitology at the University of Neuchatel in Switzerland. 

With the equivalent of Master's degree in mechanical engineering (specialising in systems dynamics), a Phd in evolutionary ecology and several years experience working on the epidemiology of malaria in Tanzania, he conducts a considerable part of his research on mathematical theory combining the epidemiology and evolutionary ecology of parasites.

He teaches courses on mathematical modelling, parasitology and evolutionary ecology at the University of Neuchatel and at the ETH Zurich.

Table of Contents

  • Introduction
    • Why model epidemiology?
    • Understanding the complexity with R
    • Overview
  • How can we describe epidemics with R?
    • Stochastic model
    • Deterministic approximation
    • Take-home
  • How can we model a flu epidemic?
    • Density- or frequency-dependence
    • Epidemiological model
    • Take-home
  • How can we estimate the parameters of the flu epidemic?
    • Linear regression
    • Non-linear regression
    • Parameters of a flu epidemic
    • Assumptions of regression
    • Likelihood
    • Pitfalls
    • Take-home
  • How do we know whether there will be an outbreak?
    • Basic reproductive number
    • Vaccines
    • Estimating
    • Take-home
  • How can we deal with endemic infectious diseases?
    • Epidemiological patterns
    • Basic reproductive number
    • Control with vaccine
    • Estimating the basic reproductive number
    • Take-home
  • Why does measles cycle?
    • Intrinsic cycling
    • What maintains cycles?
    • Take-home
  • What if contact rates vary?
    • Effect of heterogeneity on
    • Effect of heterogeneity on prevalence
    • Take-home
  • Epilogue
  • About the author
  • Essentials of R
    • Installing R and an R interface
    • Getting started: R as a calculator
    • What if we want to store the results of our calculations?
    • Object types
    • Vectors
    • Matrices
    • Data frames
    • Control structures
    • Functions
    • Simple plots
  • Notes

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