Forecasting Based on Surveillance Data

Authored by: Leonhard Held , Sebastian Meyer

Handbook of Infectious Disease Data Analysis

Print publication date:  November  2019
Online publication date:  November  2019

Print ISBN: 9781138626713
eBook ISBN: 9781315222912
Adobe ISBN:

10.1201/9781315222912-25

 Download Chapter

 

Abstract

In this chapter models of infectious disease transmission are introduced. The models contain randomness and specify the dependency of the infection events, which is a key characteristic of infectious disease data analysis. In the model the population is partitioned into susceptible (S), infectious (I), and recovered individuals (R), and considered is a continuous time version, which is well approximated by a deterministic set of differential equations, and a discrete generation type model known as the Reed-Frost or chain binomial model. Some key properties of the models are presented. A description is provided showing how these models can be extended by introducing birth and death to the host population, by adding more compartments to acknowledge a more complex infection cycle, and by introducing structure to the host population. Then an illustration is provided on how these models can be used to infer the values of key parameters from infectious disease data, and how this knowledge can be used to inform policy measures to control infectious diseases.

 Cite
Search for more...
Back to top

Use of cookies on this website

We are using cookies to provide statistics that help us give you the best experience of our site. You can find out more in our Privacy Policy. By continuing to use the site you are agreeing to our use of cookies.