Bayesian analysis of infectious diseases : COVID-19 and beyond
著者
書誌事項
Bayesian analysis of infectious diseases : COVID-19 and beyond
（Chapman & Hall/CRC biostatistics series）（A Chapman & Hall book）
CRC Press, 2021
- : pbk
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内容説明・目次
内容説明
Bayesian Analysis of Infectious Diseases -COVID-19 and Beyond shows how the Bayesian approach can be used to analyze the evolutionary behavior of infectious diseases, including the coronavirus pandemic. The book describes the foundation of Bayesian statistics while explicating the biology and evolutionary behavior of infectious diseases, including viral and bacterial manifestations of the contagion. The book discusses the application of Markov Chains to contagious diseases, previews data analysis models, the epidemic threshold theorem, and basic properties of the infection process. Also described are the chain binomial model for the evolution of epidemics.
Features:
Represents the first book on infectious disease from a Bayesian perspective.
Employs WinBUGS and R to generate observations that follow the course of contagious maladies.
Includes discussion of the coronavirus pandemic as well as many examples from the past, including the flu epidemic of 1918-1919.
Compares standard non-Bayesian and Bayesian inferences.
Offers the R and WinBUGS code on at www.routledge.com/9780367633868
目次
Contents
Author ......................................................................................iv
1. Introduction to Bayesian Inferences for Infectious Diseases..................1
2. Bayesian Analysis ...........................................................................................5
3. Infectious Diseases .................................................................................. .....39
4. Bayesian Inference for Discrete Markov Chains:
Their Relevance to Infectious Diseases.....................................................59
5. Biological Examples Modeled by Discrete Markov Chains................ 113
6. Inferences for Markov Chains in Continuous Time.............................149
7. Bayesian Inference: Biological Processes that Follow a
Continuous Time Markov Chain...........................................................195
8. Additional Information about Infectious Diseases..............................253
Index ..................................................................................................... 315
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