Bayesian analysis of infectious diseases : COVID-19 and beyond / Lyle D. Broemeling.
Material type:
TextSeries: Publisher: Boca Raton : CRC Press, 2021Copyright date: ©2021Description: 1 online resource (x, 331 pages) : illustrations (black and white)Content type: text Media type: computer Carrier type: online resourceISBN: 9781000336474; 1000336476; 9781000336313; 100033631X; 9781003125983; 1003125980Subject(s): Communicable diseases -- Statistics | Bayesian statistical decision theory | MATHEMATICS / Probability & Statistics / Bayesian Analysis | MEDICAL / Biostatistics | MEDICAL / Infectious DiseasesDDC classification: 616.90015195 LOC classification: RA643 | .B76 2021Online resources: Taylor & Francis | OCLC metadata license agreement Summary: 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 a companion website with the R and WinBUGS code.
Contents--Author iv...1. Introduction to Bayesian Inferences for Infectious Diseases..................12. Bayesian Analysis -- 53. Infectious Diseases ........394. Bayesian Inference for Discrete Markov Chains:Their Relevance to Infectious Diseases.........595. Biological Examples Modeled by Discrete Markov Chains................ 1136. Inferences for Markov Chains in Continuous Time.........1497. Bayesian Inference: Biological Processes that Follow a Continuous Time Markov Chain.......1958. Additional Information about Infectious Diseases.......253Index ...... 315
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 a companion website with the R and WinBUGS code.
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