Disease modelling and public health. Part A /

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 fact...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Srinivasa Rao, Arni S. R. (Editor ), Pyne, Saumyadipta (Editor ), Rao, C. Radhakrishna (Calyampudi Radhakrishna), 1920-2023 (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam, Netherlands : North Holland is an imprint of Elsevier, 2017.
Colección:Handbook of statistics (Amsterdam, Netherlands) ; v. 36.
Temas:
Epidemiology > Statistical methods.
Diseases > Statistical methods.
Epidemiology > statistics & numerical data
�Epid�emiologie > M�ethodes statistiques.
Maladies > M�ethodes statistiques.
MEDICAL > Forensic Medicine.
MEDICAL > Preventive Medicine.
MEDICAL > Public Health.
Diseases > Statistical methods
Epidemiology > Statistical methods
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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 Diseases2. 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 Diseases3.4. 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 Prediction3. Joint Models
  • 3.1. Model Specification
  • 3.2. Estimation
  • 3.3. Dynamic Prediction
  • 4. Assessing Predictive Performance
  • 4.1. Area Under the Receiver Operating Characteristic Curve
  • 4.2. Brier Score
  • 5. Example: The ARIC Study
  • 6. Discussion
  • Acknowledgments
  • References
  • Chapter 3: Statistical Models for Selected Infectious Diseases
  • 1. Common Cold and Asthma Exacerbation
  • 2. Influenza
  • 2.1. Surveillance and Estimates of the Center for Disease Control and Prevention
  • 2.2. Influenza and Respiratory Syncytial Virus in the United States2.3. Measles and Influenza Outbreaks
  • 2.4. SIRS and Hierarchical Bayesian Models
  • 2.5. Autoregressive and Bayesian Models for the Spread of Influenza
  • 2.6. Correlation of Surveillance Systems and Information Environment
  • 2.7. The Delphi System
  • 3. Tuberculosis
  • 3.1. Statistical Models for TB Incidence, Prevalence, and Mortality Estimates
  • 3.2. Mathematical Models
  • 3.3. Regression and Bayesian Models
  • 3.4. Statistical Relational Models for Structured Epidemiological Characteristics

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