Mathematical and Statistical Estimation Approaches in Epidemiology
Chowell, G., Hayman, J.M., Bettencourt, L.M.A., Castillo-Chavez, C. (Eds.)
2009, XIII, 363 p.
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Gives approaches for the estimation of the transmission potential of infectious diseases and other epidemiological parameters
Presents methods and applications for sensitivity and uncertainty quantification in epidemic modeling
Illustrates theory and methods using real applications
Serves as an essential resource for graduate students interested in the analysis of infectious disease outbreaks and epidemic modeling
Mathematical and Statistical Estimation Approaches in Epidemiology compiles t- oretical and practical contributions of experts in the analysis of infectious disease epidemics in a single volume. Recent collections have focused in the analyses and simulation of deterministic and stochastic models whose aim is to identify and rank epidemiological and social mechanisms responsible for disease transmission. The contributions in this volume focus on the connections between models and disease data with emphasis on the application of mathematical and statistical approaches that quantify model and data uncertainty. The book is aimed at public health experts, applied mathematicians and sci- tists in the life and social sciences, particularly graduate or advanced undergraduate students, who are interested not only in building and connecting models to data but also in applying and developing methods that quantify uncertainty in the context of infectious diseases. Chowell and Brauer open this volume with an overview of the classical disease transmission models of Kermack-McKendrick including extensions that account for increased levels of epidemiological heterogeneity. Their theoretical tour is followed by the introduction of a simple methodology for the estimation of, the basic reproduction number,R . The use of this methodology 0 is illustrated, using regional data for 1918–1919 and 1968 in uenza pandemics.
Chapter 1: The basic reproduction number of infectious diseases: Computation and estimation using compartmental epidemic models
Gerardo Chowell and Fred Brauer
Chapter 2: Stochastic Epidemic Modeling
Priscilla E. Greenwood and Luis F. Gordillo
Chapter 3: Two critical issues in quantitative modeling of communicable diseases: Inference of unobservables and dependent happening
Hiroshi Nishiura, Masayuki Kakehashi and Hisashi Inaba
Chapter 4: A note on the definition of contacts, measures of mixing, and model parametrization
Chapter 5: The effective reproduction number as a prelude to statistical estimation of time-dependent epidemic trends
Hiroshi Nishiura and Gerardo Chowell
Chapter 6: Sensitivity of Model-Based Epidemiological Parameter Estimation to Model Assumptions
Alun L. Lloyd
Chapter 7:An ensemble trajectory method for real-time modeling and prediction of unfolding epidemics: analysis of the 2005 Marburg fever outbreak in Angola
Luís M. A. Bettencourt
Chapter 8: Statistical Challenges in BioSurveillance
Tom Burr, Sarah Michalak and Rick Picard
Chapter 9:Death Records from Historical Archives: A Valuable Source of Epidemiological Information
Chapter 10: Sensitivity Analysis for Uncertainty Quantification in Mathematical Models
Leon Arriola and James M. Hyman
Chapter 11: An Inverse Problem Statistical Methodology Summary
H. T. Banks, Marie Davidian, John R. Samuels, Jr. and Karyn L.Sutton
Chapter 12: The epidemiological impact of rotavirus vaccination programs in the United States and Mexico
Eunha Shim and Carlos Castillo-Chavez
Chapter 13: Spatial and temporal dynamics of rubella in Peru, 1997-2006: Geographic patterns, age at infection and estimation of transmissibility
Daniel Rios-Doria, Gerardo Chowell, Cesar Munayco-Escate, Alvaro Witthembury and Carlos Castillo-Chavez
Chapter 14: TheRole of Nonlinear Relapse on Contagion Amongst Drinking Communities
Ariel Cintrón-Arias, Fabio Sánchez, Xiaohong Wang, Carlos Castillo-Chavez, Dennis M. Gorman and Paul J. Gruenwald