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

Lyle D. Broemeling, Ph.D., is Director of Broemeling and Associates Inc., and is a consulting biostatistician. He has been involved with academic health science centers for about 20 years and has taught and been a consultant at the University of Texas Medical Branch in Galveston, the University of Texas MD Anderson Cancer Center and the University of Texas School of Public Health. His main interest is in developing Bayesian methods for use in medical and biological problems and in authoring textbooks in statistics. His previous books are Bayesian Biostatistics and Diagnostic Medicine, and Bayesian Methods for Agreement.

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