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Disease Modelling and Public Health, Part A de Arni S.R. Srinivasa Rao

Disease Modelling and Public Health, Part A: Handbook of Statistics, cartea 36

Arni S.R. Srinivasa Rao, Saumyadipta Pyne, C. R. Rao
en Limba Engleză Hardback – 12 oct 2017
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
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Specificații

ISBN-13: 9780444639684
ISBN-10: 0444639683
Pagini: 500
Dimensiuni: 152 x 229 x 30 mm
Editura: ELSEVIER SCIENCE
Seria Handbook of Statistics

Public țintă

Researchers in academia, industry and government, as well as statistics students, health professionals, clinicians, data scientists and modellers.

Cuprins

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 5. Alternative Sampling Designs for Time-to-Event Data With Applications to Biomarker Discovery in Alzheimer’s Disease Michelle 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 10. Computational Modeling Approaches in Global Health: Sensitivity of Social Determinants on the Patterns of Health 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 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 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

Recenzii

"Simply put, the study converts qualitative data to quantitative data. Chapter 10 in the study is unique for a health scientist – it looks at these risk behaviors and draws important conclusions regarding the contraction of various chronic and infectious diseases. This data could influence the way in which college students are educated on health risk behaviors in the future." --The State Press