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Introduction to Statistical Methods for Biosurveillance

Book Description

Bioterrorism is not a new threat, but in an increasingly interconnected world, the potential for catastrophic outcomes is greater today than ever. The medical and public health communities are establishing biosurveillance systems designed to proactively monitor populations for possible disease outbreaks as a first line of defense. The ideal biosurveillance system should identify trends not visible to individual physicians and clinicians in near-real time. Many of these systems use statistical algorithms to look for anomalies and to trigger epidemiologic investigation, quantification, localization, and outbreak management. This book discusses the design and evaluation of statistical methods for effective biosurveillance for readers with minimal statistical training. Weaving public health and statistics together, it presents basic and more advanced methods, with a focus on empirically demonstrating added value. Although the emphasis is on epidemiologic and syndromic surveillance, the statistical methods can be applied to a broad class of public health surveillance problems.

Table of Contents

  1. Cover
  2. Introduction to Statistical Methods for Biosurveillance
  3. Title
  4. Copyright
  5. Dedication
  6. Table of Contents
  7. Preface
  8. Acknowledgments
  9. Part I Introduction to Biosurveillance
    1. 1 Overview
      1. 1.1 What Is Biosurveillance?
      2. 1.2 Biosurveillance Systems
      3. 1.3 Biosurveillance Utility and Effectiveness
      4. 1.4 Discussion and Summary
    2. 2 Biosurveillance Data
      1. 2.1 Types of Data
      2. 2.2 Types of Biosurveillance Data
      3. 2.3 Data Preparation
      4. 2.4 Discussion and Summary
  10. Part II Situational Awareness
    1. 3 Situational Awareness for Biosurveillance
      1. 3.1 What Is Situational Awareness?
      2. 3.2 A Theoretical Situational Awareness Model
      3. 3.3 Biosurveillance Situational Awareness
      4. 3.4 Extending the Situational Awareness Model: Situated Cognition
      5. 3.5 Discussion and Summary
    2. 4 Descriptive Statistics for Comprehending the Situation
      1. 4.1 Numerical Descriptive Statistics
      2. 4.2 Graphical Descriptive Statistics
      3. 4.3 Discussion and Summary
    3. 5 Statistical Models for Projecting the Situation
      1. 5.1 Modeling Time Series Data
      2. 5.2 Smoothing Models
      3. 5.3 Regression-Based Models
      4. 5.4 ARMA and ARIMA Models
      5. 5.5 Change Point Analysis
      6. 5.6 Discussion and Summary
  11. Part III Early Event Detection
    1. 6 Early Event Detection Design and Performance Evaluation
      1. 6.1 Notation and Assumptions
      2. 6.2 Design Points and Principles
      3. 6.3 Early Event Detection Methods Differ from Other Statistical Tests
      4. 6.4 Measuring Early Event Detection Performance
      5. 6.5 Discussion and Summary
    2. 7 Univariate Temporal Methods
      1. 7.1 Historical Limits Detection Method
      2. 7.2 Shewhart Detection Method
      3. 7.3 Cumulative Sum Detection Method
      4. 7.4 Exponentially Weighted Moving Average Detection Method
      5. 7.5 Other Methods
      6. 7.6 Discussion and Summary
    3. 8 Multivariate Temporal and Spatio-temporal Methods
      1. 8.1 Multivariate Temporal Methods
      2. 8.2 Spatio-temporal Methods
      3. 8.3 Discussion and Summary
  12. Part IV Putting It All Together
    1. 9 Applying the Temporal Methods to Real Data
      1. 9.1 Using Early Event Detection Methods to Detect Outbreaks and Attacks
      2. 9.2 Assessing How Syndrome Definitions Affect Early Event Detection Performance
      3. 9.3 Discussion and Summary
    2. 10 Comparing Methods to Better Understand and Improve Biosurveillance Performance
      1. 10.1 Performance Comparisons: A Univariate Example
      2. 10.2 Performance Comparisons: A Multivariate Example
      3. 10.3 Discussion and Summary
  13. Part V Appendices
    1. A A Brief Review of Probability, Random Variables, and Some Important Distributions
      1. A.1 Probability
      2. A.2 Random Variables
      3. A.3 Some Important Probability Distributions
    2. B Simulating Biosurveillance Data
      1. B.1 Types of Simulation
      2. B.2 Simulating Biosurveillance Data
      3. B.3 Discussion and Summary
    3. C Tables
  14. References
  15. Author Index
  16. Subject Index