Organization Of The Book

We have organized this book into six parts.

Part I: The Problem of Biosurveillance comprises this introductory chapter and Chapters 2 through 4. Chapter 2 ("Outbreaks and Investigations'') provides examples of outbreaks that have been investigated by governmental public health, hospital infection control, and the animal healthcare system. Chapter 3 ("Case Detection, Outbreak Detection, and Outbreak Characterization") provides an overview of the basic tasks of biosurveillance, explaining in detail the methods used to detect and characterize the outbreaks described in Chapter 2. Chapter 4 ("Functional Requirements for Biosurveillance'') discusses biosurveillance from the perspective of a system analyst or engineer.

Part II: Organizations that Conduct Biosurveillance and the Data They Collect (Chapters 5-12) discusses governmental public health, the human healthcare system, the animal healthcare system, laboratories, water departments, the food and drug industries, and other organizations that conduct biosurveillance. The chapters discuss the types of professionals that work in these organizations, the organizations themselves, and the information systems used by these organizations.

Part III: Data Analysis (Chapters 13-20) discusses methods for detection of individual cases, methods for detecting anomalous numbers of cases in a population, and methods for elucidating characteristics of outbreaks. The first two chapters discuss algorithms for detection of individual cases ("Case-Detection Algorithms'') and the simplest algorithms for detecting outbreaks ("Classical Time-Series Methods for Biosurveillance''). The last chapter (Chapter 20) discusses methods for evaluating both case-detection and outbreak-detection algorithms. The remaining chapters cover more advanced topics, including spatial scanning, multivariate analysis, atmospheric dispersion modeling, natural language processing, and Bayesian biosurveillance.

Part IV: Newer Types of Biosurveillance Data (Chapters 21-28) discusses what research has found about the value of newer types of biosurveillance data, such as school absenteeism, sales of over-the-counter medications, and data from sensors (including physiological sensors and remote sensing from space-based satellites). Because many of these types of data are still the subject of active research, we devote the first chapter in Part IV to research methods for evaluating surveillance data.

Part V: Decision Making (Chapters 29-31) discusses the types of decisions faced by biosurveillance personnel, the types of errors in judgment to which decision makers are prone, and formal methods for modeling decisions. Part V uses an extended example of a common decision problem that is currently at the forefront in the biosurveillance community: Whether and how to react to anomalies in newer types of surveillance data.

Part VI: Building and Field Testing Biosurveillance Systems (Chapters 32-37) covers implementation issues. To avoid hearing "Professor, you left out a whole bunch of stuff,''3 the final part of this book covers pragmatic issues related to building biosurveillance systems. Although data and analysis are the foundations of biosurveillance, organizations that wish to build biosurveillance systems must attend to proper architectural design, use of standards, legal issues, and project management. Chapter 37 discusses methods for field testing of operational biosurveillance systems.

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