© 2009

Mathematical and Statistical Estimation Approaches in Epidemiology

  • Gerardo Chowell
  • James M. Hyman
  • Luís M. A. Bettencourt
  • Carlos Castillo-Chavez

Table of contents

  1. Front Matter
    Pages I-XIII
  2. Priscilla E. Greenwood, Luis F. Gordillo
    Pages 31-52
  3. Tom Burr, Sarah Michalak, Rick Picard
    Pages 163-187
  4. H. Thomas Banks, Marie Davidian, John R. Samuels Jr., Karyn L. Sutton
    Pages 249-302
  5. Daniel Rios-Doria, Gerardo Chowell, Cesar Munayco-Escate, Alvaro Witthembury, Carlos Castillo-Chavez
    Pages 325-341
  6. Ariel Cintrón-Arias, Fabio Sánchez, Xiaohong Wang, Carlos Castillo-Chavez, Dennis M. Gorman, Paul J. Gruenewald
    Pages 343-360
  7. Back Matter
    Pages 361-363

About this book


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.


Epidemiological Epidemiology Infectious disease epidemiology Measure Observable SAS Time series dynamics epidemics infection infectious disease infectious diseases methodology

Editors and affiliations

  • Gerardo Chowell
    • 1
  • James M. Hyman
    • 2
  • Luís M. A. Bettencourt
    • 2
  • Carlos Castillo-Chavez
    • 3
  1. 1.Arizona State University School of Human Evolution & Social ChangeTempeUSA
  2. 2.Los Alamos National LaboratoryLos AlamosUSA
  3. 3.Dept. Mathematics & StatisticsArizona State UniversityTempeUSA

About the editors

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.

Bibliographic information


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)