Book DetailAuthor/Editor(s): Niels G. Becker
Publication Date: April 28, 2015
Publisher: Chapman and Hall/CRC
Size: 8.20 MB
Book DescriptionModeling to Inform Infectious Disease Control shows you how to take advantage of mathematical models when developing strategies to mitigate infectious disease transmission. The book presents a way of modeling as well as modeling results that help to guide the effective management of infectious disease transmission and outbreak response. It discusses the requirements for preventing epidemics and ways to quantify the impact of preventative public health interventions on the size and dynamics of an epidemic. The book also illustrates how data are used to inform model choice.
Accessible to readers with diverse backgrounds, this book explains how to gain insight into the management of infectious diseases through statistical modeling. It includes end-of-chapter exercises, glossaries of infectious disease terminology and notation, supplementary technical material, and bibliographic notes for further reading.
- Focuses on the important uses of data to estimate key model parameters and inform the choice of model
- Requires only a modest amount of prior mathematics to understand the core material
- Covers outbreaks that arise when an infectious disease is newly introduced
- Maximizes relatively simple models by translating well-known results from the theory of branching processes to the infectious disease context
- Presents models that describe the dynamics of transmission over time and introduces time-dependent public health interventions
This new book seeks to fill an important gap in the literature on infectious disease modeling, namely separating the now well-developed mathematical and statistical theory of infectious diseases from its public health application to infectious disease control. Professor Becker bridges the two worlds by presenting a logical succession of simple models that relate to some of the pressing questions arising in outbreak control. The approach is very effective and has resulted in an engaging volume that, in my estimation, will become a classic of the literature and thus a worthy successor to the author’s earlier landmark volume on the subject. It will be essential reading for a broad range of scientists working on infectious diseases, notably statisticians, modelers, and epidemiologists with an interest in quantitative methods.
--Paddy Farrington, The Open University, UK