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Infectious Disease Informatics: Syndromic Surveillance for Public Health and Bio-Defense 2010 ed. [Kietas viršelis]

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  • Formatas: Hardback, 210 pages, aukštis x plotis: 235x155 mm, weight: 1120 g, 68 Illustrations, black and white; XXIV, 210 p. 68 illus., 1 Hardback
  • Serija: Integrated Series in Information Systems 21
  • Išleidimo metai: 17-Dec-2009
  • Leidėjas: Springer-Verlag New York Inc.
  • ISBN-10: 1441912770
  • ISBN-13: 9781441912770
Kitos knygos pagal šią temą:
Infectious Disease Informatics: Syndromic Surveillance for Public Health and Bio-Defense 2010 ed.Didesnis paveikslėlis
  • Formatas: Hardback, 210 pages, aukštis x plotis: 235x155 mm, weight: 1120 g, 68 Illustrations, black and white; XXIV, 210 p. 68 illus., 1 Hardback
  • Serija: Integrated Series in Information Systems 21
  • Išleidimo metai: 17-Dec-2009
  • Leidėjas: Springer-Verlag New York Inc.
  • ISBN-10: 1441912770
  • ISBN-13: 9781441912770
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781441912770.html
Raktažodžiai:
Computer-based infectious disease surveillance systems are capable of real-time or near real-time detection of serious illnesses and potential bioterrorism agent exposures and represent a major step forward in disease surveillance. Infectious Disease Informatics: Syndromic Surveillance for Public Health and Bio-Defense is an in-depth monograph that analyzes and evaluates the outbreak modeling and detection capabilities of existing surveillance systems under a unified framework, and presents the first book-length coverage of the subject from an informatics-driven perspective.Individual chapters consider the state of the art, including the facilitation of data collection, sharing and transmission; a focus on various outbreak detection methods; data visualization and information dissemination issues; and system assessment and other policy issues. Eight chapters then report on several real-world case studies, summarizing and comparing eight syndromic surveillance systems, including those that have been adopted by many public health agencies (e.g., RODS and BioSense). The book concludes with a discussion of critical issues and challenges, with a look to future directions.This book is an excellent source of current information for researchers in public health and IT. Government public health officials and private-sector practitioners in both public health and IT will find the most up-to-date information available, and students from a variety of disciplines, including public health, biostatistics, information systems, computer science, and public administration and policy will get a comprehensive look at the concepts, techniques, and practices of syndromic surveillance.

Computer-based infectious disease surveillance systems represent a major step forward in disease surveillance. This book analyzes and evaluates the outbreak modeling and detection capabilities of existing surveillance systems under a unified framework.

Recenzijos

From the reviews:

This book summarizes and describes the state-of-art research on the development and implementation of health surveillance systems that use early indicators of disease to identify outbreaks. The book was written for upper-level undergraduates and graduates in health sciences, computer science, and public administration, researchers in public health and IT, and government public health officials. This is the best book that presents a comprehensive coverage of syndromic surveillance systems. (Edward K. Mensah, Doodys Review Service, August, 2010)

Preface ix
Author Biographies xiii
Acknowledgments xvii
PART I: SYNDROMIC SURVEILLANCE SYSTEMS
Infectious Disease Informatics: An Introduction and An Analysis Framework
3(6)
Public Health Syndromic Surveillance Systems
9(24)
Summary of Nationwide Syndromic Surveillance Systems
10(7)
Summary of Syndromic Surveillance Systems at the Local, County, and State Levels
17(8)
Summary of Industrial Solutions for Syndromic Surveillance
25(2)
Summary of International Syndromic Surveillance Projects
27(2)
Syndromic Surveillance for Special Events
29(4)
Syndromic Surveillance Data Sources and Collection Strategies
33(16)
Data Sources for Public Health Syndromic Surveillance
33(9)
Comparison of Data Sources
37(5)
Standardized Vocabularies
42(4)
Existing Data Standards Used in Syndromic Surveillance
43(3)
Data Entry and Data Transmission
46(3)
Data Entry Approaches
47(1)
Secure Data Transmission
47(2)
Data Analysis and Outbreak Detection
49(24)
Syndrome Classification
49(6)
Syndrome Classification Approaches
51(3)
Performance of Syndrome Classification Approaches
54(1)
A Taxonomy of Outbreak Detection Methods
55(6)
Retrospective vs. Prospective Syndromic Surveillance
55(1)
Temporal, Spatial, and Spatial-Temporal Outbreak Detection Methods
56(5)
Temporal Data Analysis
61(4)
Statistical Process Control (SPC)-Based Anomaly Detection
61(1)
Serfling Statistic
62(1)
Autoregressive Model-Based Anomaly Detection
63(1)
Hidden Markov Model (HMM)-Based Models
64(1)
Spatial Data Analysis
65(4)
Generalized Linear Mixed Models and SMART Algorithm
66(1)
Spatial Scan Statistic and Its Variations
67(2)
Risk-Adjusted Support Vector Clustering (RSVC) Algorithm
69(1)
Spatial-Temporal Data Analysis
69(1)
Rule-Based Anomaly Detection with Bayesian Network Modeling
69(1)
Population-Wide Anomaly Detection and Assessment (PANDA)
70(1)
Monitoring Multiple Data Streams
70(1)
Special Events Surveillance
71(1)
Summary of Data Analysis Process for Syndromic Surveillance
72(1)
Data Visualization, Information Dissemination, and Alerting
73(16)
Scope and Taxonomy
74(1)
Visual Information Display
74(10)
Visualization of Time-Series Data
75(2)
Visualization of Spatial Information
77(2)
GIS for Disease Event Visualization
79(4)
Spatial-Temporal Disease Modeling and Other Visualization Examples
83(1)
Interactive Visual Data Exploration
84(1)
Summary of Data Visualization in Syndromic Surveillance Applications
85(1)
Information Dissemination and Reporting
86(3)
System Assessment and Evaluation
89(20)
Syndromic Surveillance System Evaluation Framework
90(1)
Evaluation of Outbreak Detection Algorithms
91(10)
Evaluation Methodology
91(1)
Real Data Testing
91(1)
Fully Synthetic Data Testing
92(2)
Semisynthetic Data Testing
94(1)
Evaluation Metrics for Outbreak Detection Algorithms
95(3)
Summary of Representative Evaluation Studies
98(3)
Evaluation of Data Collection and Information Dissemination Components
101(1)
Assessment of Interface Features and System Usability
101(2)
System Usability Evaluation Methodology
101(1)
System Usability Evaluation Metrics
102(1)
Summary of System Usability Evaluation Studies
102(1)
Summary and Discussion
103(6)
PART II: SYNDROMIC SURVEILLANCE SYSTEM CASE STUDIES
BioSense
109(12)
BioSense Data Collection and Preprocessing
112(1)
BioSense Data Analysis
113(1)
BioSense Data Visualization, Information Dissemination, and Reporting
114(2)
Case Study: Monitoring Health Effects of Wildfires Using BioSense
116(3)
Further Readings
119(2)
RODS
121(12)
RODS Data Collection
122(2)
RODS Data Analysis
124(2)
RODS Visualization, Information Dissemination, and Reporting
126(2)
Case Study: Syndromic Surveillance with RODS for the 2002 Winter Olympics
128(3)
Further Readings
131(2)
BioPortal
133(14)
BioPortal Data Collection
135(1)
BioPortal Data Analysis
135(1)
BioPortal Visualization, Information Dissemination, and Reporting
136(6)
Case Study: Foot-and-Mouth Disease Situational Awareness
142(2)
Further Readings
144(3)
ESSENCE
147(10)
ESSENCE Data Collection
149(1)
ESSENCE Data Analysis and System Evaluation
150(2)
ESSENCE Interface, Information Dissemination, and Reporting
152(3)
Further Readings
155(2)
New York City Syndromic Surveillance Systems
157(10)
NYC ED Syndromic Surveillance System Data Collection
158(1)
NYC ED Syndromic Surveillance System Data Analysis and Field Investigations
159(1)
NYC ED Syndromic Surveillance System Visualization, Information Dissemination, and Reporting
160(2)
Case Study: Respiratory Illness Surveillance Using Multiple Syndromic Systems in New York City
162(2)
Further Readings
164(3)
EARS
167(10)
EARS Data Collection and Data Preprocessing
168(1)
Key EARS Aberration Detection Methods
169(2)
EARS Visualization, Information Dissemination, and Reporting
171(2)
Case Study: PostHurricane Public Health Surveillance with EARS
173(1)
Further Readings
174(3)
Argus
177(6)
HealthMap
183(4)
Challenges and Future Directions
187(4)
Challenges for Syndromic Surveillance Research
187(1)
Summary and Future Directions
188(3)
References 191(16)
Subject Index 207