Mathematical and Statistical Estimation Approaches in Epidemiology

Herausgegeben:Chowell, Gerardo; Hayman, James M.; Bettencourt, Luís M. A.; Castillo-Chavez, Carlos

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Springer / Springer Netherlands
• Artikelnr. des Verlages: 12189496, 978-90-481-2312-4
• 2009
• Seitenzahl: 384
• Erscheinungstermin: 26. Juni 2009
• Englisch
• Abmessung: 241mm x 160mm x 29mm
• Gewicht: 678g
• ISBN-13: 9789048123124
• ISBN-10: 9048123127
• Artikelnr.: 26025140

Autorenporträt

Gerardo Chowell is an associate professor and a Second Century Initiative Scholar (2CI) in the School of Public Health at Georgia State University in Atlanta. His research program includes the development and application of quantitative approaches for understanding the transmission dynamics and control of infectious diseases including influenza, Ebola, and dengue fever. His work has appeared in high-impact journals including The New England Journal of Medicine, PLOS Medicine, and BMC Medicine, and has been cited by major media outlets including the Washington Post and TIME magazine. James (Mac) Hyman has developed and analyzed mathematical models for the transmission of HIV/AIDs, influenza, malaria, dengue fever, chikungunya, and infections.  His current focus is to identify approaches where these models can help public health workers be more effective in mitigating the impact of emerging diseases.  He was a research scientist at Los Alamos National Laboratory for over thirty years, is a past president of the Society for Industrial and Applied Mathematics (SIAM),  and now holds the Phillips Distinguished Chair in Mathematics at Tulane University.

Inhaltsangabe

The Basic Reproduction Number of Infectious Diseases: Computation and Estimation Using Compartmental Epidemic Models.- Stochastic Epidemic Modeling.- Two Critical Issues in Quantitative Modeling of Communicable Diseases: Inference of Unobservables and Dependent Happening.- The Chain of Infection, Contacts, and Model Parametrization.- The Effective Reproduction Number as a Prelude to Statistical Estimation of Time-Dependent Epidemic Trends.- Sensitivity of Model-Based Epidemiological Parameter Estimation to Model Assumptions.- An Ensemble Trajectory Method for Real-Time Modeling and Prediction of Unfolding Epidemics: Analysis of the 2005 Marburg Fever Outbreak in Angola.- Statistical Challenges in BioSurveillance.- Death Records from Historical Archives: A Valuable Source of Epidemiological Information.- Sensitivity Analysis for Uncertainty Quantification in Mathematical Models.- An Inverse Problem Statistical Methodology Summary.- The Epidemiological Impact of Rotavirus Vaccination Programs in the United States and Mexico.- Spatial and Temporal Dynamics of Rubella in Peru, 1997-2006: Geographic Patterns, Age at Infection and Estimation of Transmissibility.- The Role of Nonlinear Relapse on Contagion Amongst Drinking Communities.

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The Basic Reproduction Number of Infectious Diseases: Computation and Estimation Using Compartmental Epidemic Models.- Stochastic Epidemic Modeling.- Two Critical Issues in Quantitative Modeling of Communicable Diseases: Inference of Unobservables and Dependent Happening.- The Chain of Infection, Contacts, and Model Parametrization.- The Effective Reproduction Number as a Prelude to Statistical Estimation of Time-Dependent Epidemic Trends.- Sensitivity of Model-Based Epidemiological Parameter Estimation to Model Assumptions.- An Ensemble Trajectory Method for Real-Time Modeling and Prediction of Unfolding Epidemics: Analysis of the 2005 Marburg Fever Outbreak in Angola.- Statistical Challenges in BioSurveillance.- Death Records from Historical Archives: A Valuable Source of Epidemiological Information.- Sensitivity Analysis for Uncertainty Quantification in Mathematical Models.- An Inverse Problem Statistical Methodology Summary.- The Epidemiological Impact of Rotavirus Vaccination Programs in the United States and Mexico.- Spatial and Temporal Dynamics of Rubella in Peru, 1997–2006: Geographic Patterns, Age at Infection and Estimation of Transmissibility.- The Role of Nonlinear Relapse on Contagion Amongst Drinking Communities.

Rezensionen

From the reviews:

"Mathematical and Statistical Estimation Approaches in Epidemiology is a well written book ... . The book is aimed at public health experts, applied mathematicians and scientists in the life and social sciences particularly graduate or advanced undergraduate students. This is an excellent text for those with some knowledge of statistics mathematics ... . it suits the expectations for that category of readers that is written for and will be a useful reference on many bookshelves." (Peter Wludyka and Carmen Masnita Iusan, Technometrics, Vol. 53 (1), February, 2011)