Bayesian Analysis of Infectious Diseases: COVID-19 and Beyond - Chapman & Hall/CRC Biostatistics Series (Hardback)
  • Bayesian Analysis of Infectious Diseases: COVID-19 and Beyond - Chapman & Hall/CRC Biostatistics Series (Hardback)
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Bayesian Analysis of Infectious Diseases: COVID-19 and Beyond - Chapman & Hall/CRC Biostatistics Series (Hardback)

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£130.00
Hardback 332 Pages
Published: 08/02/2021
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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

Publisher: Taylor & Francis Ltd
ISBN: 9780367633868
Number of pages: 332
Weight: 612 g
Dimensions: 234 x 156 mm


MEDIA REVIEWS

"This book will be useful for both masters and undergraduate students in biostatistics, who are planning to pursue research in Bayesian approaches towards epidemics applications"

- Chitaranjan Mahapatra, International Society for Clinical Biostatistics, 72, 2021


"Since most available textbooks for infectious disease modeling present the subject from differential equations,
mathematical modeling perspective, this text is an important first step towards filling the gap from the statistical
perspective."

Marie V. Ozanne, Mount Holyoke College USA, Biometrics: A Journal of the International Biometric Society, December 2021.

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