Disease Modelling and Public Health, Part A (Volume 36) (Handbook of Statistics, Volume 36)

Disease Modelling and Public Health, Part A, Volume 36 addresses new challenges in existing and emerging diseases with a variety of comprehensive chapters that cover Infectious Disease Modeling, Bayesian Disease Mapping for Public Health, Real time estimation of the case fatality ratio and risk factor of death, Alternative Sampling Designs for Time-To-Event Data with Applications to Biomarker Discovery in Alzheimer's Disease, Dynamic risk prediction for cardiovascular disease: An illustration using the ARIC Study, Theoretical advances in type 2 diabetes, Finite Mixture Models in Biostatistics, and Models of Individual and Collective Behavior for Public Health Epidemiology. As a two part volume, the series covers an extensive range of techniques in the field. It present a vital resource for statisticians who need to access a number of different methods for assessing epidemic spread in population, or in formulating public health policy. Presents a comprehensive, two-part volume written by leading subject experts Provides a unique breadth and depth of content coverage Addresses the most cutting-edge developments in the field Includes chapters on Ebola and the Zika virus; topics which have grown in prominence and scholarly output Contents Contributors Preface Section I: Introduction and Disease Modeling 1. Fundamentals of Mathematical Models of Infectious Diseases and Their Application to Data Analyses • Masayuki Kakehashi and Shoko Kawano 2. Dynamic Risk Prediction for Cardiovascular Disease: An Illustration Using the ARIC Study • Jessica K. Barrett, Michael J. Sweeting, and Angela M.Wood 3. Statistical Models for Selected Infectious Diseases • Poduri S.R.S. Rao 4. Finite Mixture Models in Biostatistics • Sharon X. Lee, Shu-Kay Ng, and Geoffrey J. McLachlan Section II: Methods for Public Health Data 5. Alternative Sampling Designs for Time-to-Event Data With Applications to Biomarker Discovery in Alzheimer’s Disease • Michelle M. Nuño and Daniel L. Gillen 6. Real-Time Estimation of the Case Fatality Ratio and Risk Factors of Death • Hiroshi Nishiura 7. Nonparametric Regression of State Occupation Probabilities in a Multistate Model • Sutirtha Chakraborty, Somnath Datta, and Susmita Datta 8. Gene Set Analysis: As Applied to Public Health and Biomedical Studies • Shabnam Vatanpour and Irina Dinu 9. Causal Inference in the Study of Infectious Disease • Bradley C. Saul, Michael G. Hudgens, and M. Elizabeth Halloran Section III: Computing 10. Computational Modeling Approaches Linking Health and Social Sciences: Sensitivity of Social Determinants on the Patterns of Health Risk Behaviors and Diseases • Anuj Mubayi 11. Data-Driven Computational Disease Spread Modeling: From Measurement to Parametrization and Control • Stefan Engblom and Stefan Widgren 12. Individual and Collective Behavior in Public Health Epidemiology • Jiangzhuo Chen, Bryan Lewis, Achla Marathe, Madhav Marathe, Samarth Swarup, and Anil K.S. Vullikanti Section IV: Mathematical Modeling and Methods 13. Theoretical Advances in Type 2 Diabetes • Pranay Goel 14. Helminth Dynamics: Mean Number of Worms, Reproductive Rates • Arni S.R. Srinivasa Rao and Roy M. Anderson Section V: Bayesian Methods 15. Bayesian Methods in Public Health • Wesley O. Johnson, Elizabeth B. Ward, and Daniel L.Gillen 16. Bayesian Disease Mapping for Public Health • Andrew Lawson and Duncan Lee Index "Front Cover"--"Disease Modelling and Public Health"--"Copyright" -- "Contents" -- "Contributors" -- "Preface" -- "Section I: Introduction and Disease Modeling" -- "Chapter 1: Fundamentals of Mathematical Models of Infectious Diseases and Their Application to Data Analyses" -- "1. Introduction: Fundamentals of Infectious Disease Dynamical Models" -- "1.1. Population Dynamics of Biological Populations" -- "1.2. Infectious Disease Spread Models, or Theoretical Epidemiology" -- "1.3. Important Concepts in Infectious Disease Epidemiology" -- "1.4. Important Concepts From Dynamical Models of Infectious Diseases" -- "2. Analyses of Whole Population: Macroscopic Analyses" -- "2.1. Data Description" -- "2.2. Simple Regression Analysis" -- "2.3. The Effect of School Closure" -- "2.4. Incorporating Exposed Phase: SEIR Model" -- "2.5. Distributions of Latent and Infectious Periods" -- "2.6. Multiple Subgroups and Generation Matrix" -- "3. Stochastic Model of Infectious Disease Spread: Microscopic Model Considering Each Class" -- "3.1. Analyses for Counted Data" -- "3.2. Modeling the Reporting Delay" -- "3.3. Modeling the Transition of Infectious Diseases" -- "3.4. Reconstruction of the Values of State Variables of the System" -- "3.5. Analysis and Simulation, and the Validity of the Model" -- "4. An Analysis of Spatial Distribution" -- "4.1. Location of Schools" -- "4.2. Estimating Transition Kernel" -- "4.3. Influence of the Network of Transmission" -- "5. Conclusion" -- "References" -- "Further Reading" -- "Chapter 2: Dynamic Risk Prediction for Cardiovascular Disease: An Illustration Using the ARIC Study" -- "1. Introduction" -- "2. Landmarking" -- "2.1. The Landmarking Method" -- "2.2. Dynamic Prediction" -- "3. Joint Models" -- "3.1. Model Specification" -- "3.2. Estimation" -- "3.3. Dynamic Prediction" -- "4. Assessing Predictive Performance