Zeng / Chen / Castillo-Chavez Infectious Disease Informatics and Biosurveillance

1. Auflage 2010
ISBN: 978-1-4419-6892-0
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 27, 488 Seiten

Reihe: Integrated Series in Information Systems

This book on Infectious Disease Informatics (IDI) and biosurveillance is intended to provide an integrated view of the current state of the art, identify technical and policy challenges and opportunities, and promote cross-disciplinary research that takes advantage of novel methodology and what we have learned from innovative applications. This book also fills a systemic gap in the literature by emphasizing informatics driven perspectives (e.g., information system design, data standards, computational aspects of biosurveillance algorithms, and system evaluation). Finally, this book attempts to reach policy makers and practitioners through the clear and effective communication of recent research findings in the context of case studies in IDI and biosurveillance, providing 'hands-on' in-depth opportunities to practitioners to increase their understanding of value, applicability, and limitations of technical solutions. This book collects the state of the art research and modern perspectives of distinguished individuals and research groups on cutting-edge IDI technical and policy research and its application in biosurveillance. The contributed chapters are grouped into three units. Unit I provides an overview of recent biosurveillance research while highlighting the relevant legal and policy structures in the context of IDI and biosurveillance ongoing activities. It also identifies IDI data sources while addressing information collection, sharing, and dissemination issues as well as ethical considerations. Unit II contains survey chapters on the types of surveillance methods used to analyze IDI data in the context of public health and bioterrorism. Specific computational techniques covered include: text mining, time series analysis, multiple data streams methods, ensembles of surveillance methods, spatial analysis and visualization, social network analysis, and agent-based simulation. Unit III examines IT and decision support for public health event response and bio-defense. Practical lessons learned in developing public health and biosurveillance systems, technology adoption, and syndromic surveillance for large events are discussed. The goal of this book is to provide an understandable interdisciplinary IDI and biosurveillance reference either used as a standalone textbook or reference for students, researchers, and practitioners in public health, veterinary medicine, biostatistics, information systems, computer science, and public administration and policy.

Carlos Castillo-Chavez is a Regents Professor, and Joaquin Bustoz Jr. Professor of Mathematical Biology at Arizona State University and the executive director of the Mathematical and Theoretical Biology Institute and Institute for Strengthening the Understanding of Mathematics and Science at the same university. He has won awards by the American Association for the Advancement of Science (AAAS) Mentor Award and Fellow (2007), the Stanislaw M. Ulam Distinguished Scholar by the Center for Nonlinear Studies at Los Alamos National Laboratory (2003), the Society for Advancement of Chicanos and Native Americans in Science (SACNAS) Distinguished Scientist Award (2001), the Presidential Award for Excellence in Science, Mathematics and Engineering Mentoring (1997), and the Presidential Faculty Fellowship Award from the National Science Foundation and the Office of the President of the United States (1992-1997). Dr. Hsinchun Chen is McClelland Professor of Management Information Systems at the University of Arizona and Andersen Consulting Professor of the Year (1999). He received the B.S. degree from the National Chiao-Tung University in Taiwan, the MBA degree from SUNY Buffalo, and the Ph.D. degree in Information Systems from the New York University. He is author/editor of 10 books and more than 130 SCI journal articles covering intelligence analysis, biomedical informatics, data/text/web mining, digital library, knowledge management, and Web computing. His recent books include: Medical Informatics: Knowledge Management and Data Mining in Biomedicine and Intelligence and Security Informatics for International Security: Information Sharing and Data Mining, both published by Springer. Dr. Chen was ranked #8 in publication productivity in Information Systems (CAIS 2005) and #1 in Digital Library research (IP&M 2005) in two recent bibliometric studies. He serves on ten editorial boards including: ACM Transactions on Information Systems, ACM Journal on Educational Resources in Computing, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Systems, Man, and Cybernetics, Journal of the American Society for Information Science and Technology, Decision Support Systems, and International Journal on Digital Library. Dr. Chen is a Scientific Counselor/Advisor of the National Library of Medicine (USA), Academia Sinica (Taiwan), and National Library of China (China), and has served as an advisor for major NSF, DOJ, NLM, and other international research programs in digital library, digital government, medical informatics, and national security research. Dr. Chen is founding director of Artificial Intelligence Lab and Hoffman E-Commerce Lab. The UA Artificial Intelligence Lab, which houses 40+ researchers, has received more than $17M in research funding from NSF, NIH, NLM, DOJ, CIA, and other agencies over the past 15 years. The Hoffman E-Commerce Lab, which has been funded mostly by major IT industry partners, features one of the most advanced e-commerce hardware and software environments in the College of Management. Dr. Chen is conference co-chair of ACM/IEEE Joint Conference on Digital Libraries (JCDL) 2004 and has served as the conference/program co-chair for the past eight International Conferences of Asian Digital Libraries (ICADL), the premiere digital library meeting in Asia that he helped develop. Dr. Chen is also (founding) conference co-chair of the IEEE International Conferences on Intelligence and Security Informatics (ISI) 2003-2006. The ISI conference, which has been sponsored by NSF, CIA, DHS, and NIJ, has become the premiere meeting for international and homeland security IT research. Dr. Chen's COPLINK system, which has been quoted as a national model for public safety information sharing and analysis, has been adopted in more than 150 law enforcement and intelligence agencies. The COPLINK research had been featured in New York Times, Newsweek, Los Angeles Times, Washington Post, Boston Globe, among others. The COPLINK project was selected as a finalist by the prestigious International Association of Chiefs of Police (IACP)/Motorola 2003 Weaver Seavey Award for Quality in Law Enforcement in 2003. COPLINK research has recently been expanded to border protection (BorderSafe), disease and bioagent surveillance (BioPortal), and terrorism informatics research (Dark Web), funded by NSF, CIA, and DHS. Dr. Chen has also received numerous awards in information technology and knowledge management education and research including: AT&T Foundation Award, SAP Award, the Andersen Consulting Professor of the Year Award, the University of Arizona Technology Innovation Award, and the National Chaio-Tung University Distinguished Alumnus Award. Dr. Chen is an IEEE Fellow. William B. Lober MD MS is an Associate Professor at the University of Washington (UW) in the Schools of Nursing, Medicine, and Public Health & Community Medicine. Dr Lober directs the UW Clinical Informatics Research Group, which focuses on the development, integration, and evaluation of information systems to support individual and population health. His academic interests include information system-based surveillance; web-based information systems; support of population-based research in public health and biomedical research; computer supported collaborative work; and privacy and security. Dr Lober is a board member of the International Society for Disease Surveillance, is a chief editor of Advances in Disease Surveillance, and was the organizing chair of the 2005 Syndromic Surveillance Conference. He graduated from the UCSF/UC Berkeley Joint Medical Program, trained in Emergency Medicine at University of Arizona, is EM board certified, and completed a National Library of Medicine fellowship in Medical Informatics. In addition to his clinical training, he has a BSEE in Electrical Engineering from Tufts University and 10 years of industry experience in hardware and software engineering. Dr. Mark Thurmond is currently professor of epidemiology in the School of Veterinary Medicine at the University of California, Davis. He is Co-Director of the Center for Animal Disease Modeling and head of the FMD Lab. He first became involved with livestock as a young boy growing up in Northern California where he raised beef cattle. He has 34 years of experience in veterinary medicine, including clinical practice in dairy cattle, international programs in tropical veterinary medicine and education, and teaching and research in infectious diseases of livestock. His teaching includes epidemiologic methodology, infectious disease modeling, surveillance, foreign animal diseases, and infectious diseases of cattle. Past research includes work on the epidemiology of bovine abortion, bovine leukemia virus, bovine virus diarrhea virus, neosporosis, and vesicular stomatitis. Since 1997, his research has focused on global epidemiology and modeling of foot-and-mouth disease. These efforts have contributed to an understanding of the conceptual foundations for FMD surveillance and for the prospects of FMD transmission within California, rates of intra-herd transmission of FMD, and regional and global risks of FMD. Dr. Daniel Zeng received the M.S. and Ph.D. degrees in industrial administration from Carnegie Mellon University, Pittsburgh, PA, and the B.S. degree in economics and operations research from the University of Science and Technology of China, Hefei, China. Currently, he is an Associate Professor and the Director of the Intelligent Systems and Decisions Laboratory in the Department of Management Information Systems at the University of Arizona. His research interests include security informatics, infectious disease informatics, spatio-temporal data analysis, software agents and their applications, computational support for auctions and negotiations, and recommender systems. He has co-edited three books and published about 60 peer-reviewed articles in Management Information Systems and Computer Science journals, edited books, and conference proceedings. He received two best paper awards and two teaching awards in the past six years. He also serves on editorial boards of five Information Technology-related journals and is currently editing several special topic issues for major IEEE publications. He is active in MIS and IEEE professional organizations and conference activities and is Vice President for Technical Activities for the IEEE Intelligent Transportation Systems Society. He is also Vice President for Academic Activities, Chinese Association for Science and Technology (CAST-USA), a national professional organization.

Autoren/Hrsg.

Zeng, Daniel

Chen, Hsinchun

Castillo-Chavez, Carlos

Lober, William B.

Thurmond, Mark

Inhaltsverzeichnis

1;PREFACE;6
1.1;SCOPE AND ORGANIZATION;7
1.2;AUDIENCE;10
2;TABLE OF CONTENTS;12
3;LIST OF CONTRIBUTORS;28
4;EDITORS’ BIOGRAPHIES;34
5;UNIT I: INFORMATICS INFRASTRUCTURE AND DATA SOURCES;54
5.1;Chapter 1 REAL-TIME PUBLIC HEALTH BIOSURVEILLANCE;55
5.1.1;CHAPTER OVERVIEW;55
5.1.2;1. INTRODUCTION;56
5.1.3;2. BACKGROUND AND RECENT HISTORY;57
5.1.3.1;2.1 Public Health Surveillance;57
5.1.3.2;2.2 Impact of the Fall of 2001 on Biosurveillance;57
5.1.3.3;2.3 BioSense, BioWatch and the National Biosurveillance Integration System;60
5.1.4;3. POLICY CONSIDERATIONS IN BIOSURVEILLANCE;62
5.1.4.1;3.1 Federalism;63
5.1.4.2;3.2 Privacy and Data Use;65
5.1.4.3;3.3 Other Policy Considerations;66
5.1.5;4. ACHIEVING INTEGRATED, REAL-TIME BIOSURVEILLANCE;66
5.1.5.1;4.1 Stakeholder Perspectives and Information Requirements;67
5.1.5.2;4.2 Analytic Requirements;68
5.1.5.3;4.3 Policy Requirements;70
5.1.6;5. CONCLUSION AND DISCUSSION;70
5.1.7;QUESTIONS FOR DISCUSSION;71
5.1.8;REFERENCES;71
5.1.9;SUGGESTED READING;73
5.1.10;ONLINE RESOURCES;74
5.2;Chapter 2 DESIGNING ETHICAL PRACTICE IN BIOSURVEILLANCE;75
5.2.1;CHAPTER OVERVIEW;75
5.2.2;1. INTRODUCTION;76
5.2.3;2. BACKGROUND;76
5.2.4;3. OVERVIEW: INFORMATION PROTECTION;77
5.2.4.1;3.1 Fair Information Practice Principles;78
5.2.4.2;3.2 Proprietary Information;79
5.2.4.3;3.3 Individually Identifiable Information;80
5.2.5;4. METHODS;82
5.2.5.1;4.1 Information Scenarios;82
5.2.5.2;4.2 Laws, Regulations, and Good Practice in Managing Sensitive Information;87
5.2.5.3;4.3 Case Study: The Terrorism Information Awareness Program;88
5.2.6;5. RESULTS AND ANALYSIS;89
5.2.6.1;5.1 Policies and Procedures;90
5.2.6.2;5.2 Technical Requirements;91
5.2.6.3;5.3 Doctrine Management Process;92
5.2.7;6. CONCLUSION;93
5.2.8;ACKNOWLEDGMENTS;93
5.2.9;QUESTIONS FOR DISCUSSION;94
5.2.10;REFERENCES;94
5.2.11;SUGGESTED READING;96
5.2.12;ONLINE RESOURCES;96
5.3;Chapter 3 USING EMERGENCY DEPARTMENT DATA FOR BIOSURVEILLANCE: THE NORTH CAROLINA EXPERIENCE;97
5.3.1;CHAPTER OVERVIEW;97
5.3.2;1. INTRODUCTION;98
5.3.3;2. LITERATURE REVIEW/OVERVIEW OF THE FIELD;100
5.3.3.1;2.1 History of ED Data Use for Biosurveillance;100
5.3.3.2;2.2 Current Status of ED Data Use for Biosurveillance;100
5.3.3.3;2.3 Infectious Disease Syndrome-Based Surveillance Using ED Data;101
5.3.3.4;2.4 ISDS Consultative Syndrome Group;102
5.3.4;3. TECHNICAL APPROACHES FOR GROUPING ED DATA INTO SYNDROMES FOR BIOSURVEILLANCE;103
5.3.4.1;3.1 Dealing with Negation;104
5.3.4.2;3.2 Issues with Diagnosis Code Data;104
5.3.5;4. BIOSURVEILLANCE IN NORTH CAROLINA;105
5.3.5.1;4.1 History of Syndrome Definitions in NC;106
5.3.5.2;4.2 The Importance of Data Quality;107
5.3.5.3;4.3 NC DETECT Case Studies;107
5.3.5.3.1;4.3.1 Public Health Surveillance During and After Hurricanes;107
5.3.5.3.2;4.3.2 Influenza;109
5.3.5.3.3;4.3.3 Early Event Detection;110
5.3.5.3.4;4.3.4 Bioterrorism Agent Report;110
5.3.5.3.5;4.3.5 Case Finding & Infectious Disease Outbreak Monitoring;111
5.3.5.3.6;4.3.6 Infectious Disease Retrospective Analyses;111
5.3.5.3.7;4.3.7 Injury Surveillance;112
5.3.5.4;4.4 Conclusions and Discussion;112
5.3.5.5;4.5 Evaluation of NC DETECT;112
5.3.6;5. CONCLUSION;114
5.3.7;ACKNOWLEDGEMENTS;114
5.3.8;QUESTIONS FOR DISCUSSION;114
5.3.9;REFERENCES;115
5.3.10;SUGGESTED READING;118
5.3.11;ONLINE RESOURCES;118
5.4;Chapter 4 CLINICAL LABORATORY DATA FOR BIOSURVEILLANCE;119
5.4.1;CHAPTER OVERVIEW;119
5.4.2;1. INTRODUCTION;119
5.4.3;2. TYPES OF SURVEILLANCE;120
5.4.3.1;2.1 Laboratory Data for Biosurveillance;122
5.4.3.2;2.2 The Clinical Laboratory;123
5.4.3.2.1;2.2.1 Development of the Clinical Laboratory;123
5.4.3.2.2;2.2.2 Laboratory Types;124
5.4.3.2.3;2.2.3 Sources of Laboratory Data for Biosurveillance;126
5.4.3.2.4;2.2.4 Components of Laboratory Data for Biosurveillance;127
5.4.3.2.5;2.2.5 Data Standards for Biosurveillance;129
5.4.3.2.6;2.2.6 Data Analysis;129
5.4.3.2.7;2.2.7 Underlying Data Characteristics;130
5.4.3.2.7.1;Patient Characteristics;131
5.4.3.2.7.2;Provider Characteristics;132
5.4.3.2.7.3;External Drivers;133
5.4.3.3;2.3 Relevant Experience and Case Studies;134
5.4.3.3.1;2.3.1 The Emergence of West Nile Virus in New York City;134
5.4.3.3.2;2.3.2 Lyme Disease in New Jersey;135
5.4.3.3.3;2.3.3 Hepatitis in New York City;135
5.4.3.3.4;2.3.4 Projections in Florida;135
5.4.4;3. CONCLUSIONS AND DISCUSSION;136
5.4.5;QUESTIONS FOR DISCUSSION;136
5.4.6;REFERENCES;137
5.4.7;SUGGESTED READING;139
5.4.8;ONLINE RESOURCES;139
5.5;Chapter 5 BIOSURVEILLANCE BASED ON TEST ORDERS FROM VETERINARY DIAGNOSTIC LABS;140
5.5.1;CHAPTER OVERVIEW;140
5.5.2;1. INTRODUCTION;141
5.5.2.1;1.1 Wildlife as Sentinels of Disease;141
5.5.2.2;1.2 Pets as Sentinel Indicators of Disease;141
5.5.2.3;1.3 “One Medicine”;142
5.5.3;2. SURVEILLANCE FOR OUTBREAKS OF ZOONOTIC DISEASE;143
5.5.3.1;2.1 National Animal Health Reporting System;143
5.5.3.2;2.2 National Animal Health Laboratory Network;144
5.5.3.3;2.3 Veterinary Services Electronic Surveillance Project;144
5.5.3.4;2.4 Rapid Syndrome Validation Project for Animals;145
5.5.3.5;2.5 Other Manual Entry Systems;145
5.5.4;3. IMPROVING OUTBREAK DETECTION;145
5.5.4.1;3.1 Syndromic Surveillance;146
5.5.4.1.1;3.1.1 Preferred Data;147
5.5.4.1.2;3.1.2 Data Criteria;147
5.5.4.2;3.2 Veterinary Diagnostic Laboratories;148
5.5.4.2.1;3.2.1 Case Evidence to Support Using Data;148
5.5.4.2.2;3.2.2 Determining Animal Representation;150
5.5.4.2.3;3.2.3 Estimating Human Representation;150
5.5.4.2.4;3.2.4 Availability and Timeliness;152
5.5.5;4. CONCLUSION;153
5.5.6;QUESTIONS FOR DISCUSSION;154
5.5.7;REFERENCES;155
5.5.8;SUGGESTED READING;158
5.5.9;ONLINE RESOURCES;158
6;UNIT II: SURVEILLANCE ANALYTICS;159
6.1;Chapter 6 MARKOV SWITCHING MODELS FOR OUTBREAK DETECTION;160
6.1.1;CHAPTER OVERVIEW;160
6.1.2;1. INTRODUCTION;161
6.1.3;2. MARKOV SWITCHING MODELS;163
6.1.3.1;2.1 Time Series Generated by Markov Switching Models: An Illustrative Example;165
6.1.3.2;2.2 Estimation Methods for Markov Switching Models;167
6.1.4;3. BAYESIAN INFERENCE: AN OVERVIEW;169
6.1.4.1;3.1 Maximum Likelihood Estimation and Bayesian Inference: An Illustrative Example;169
6.1.4.1.1;3.1.1 Likelihood Maximization;169
6.1.4.1.2;3.1.2 Bayesian Inference;170
6.1.4.1.3;3.1.3 A Numerical Example;173
6.1.4.1.3.1;Data Generating Process;173
6.1.4.1.3.2;Likelihood Maximization;175
6.1.4.1.3.3;Bayesian Inference Using Gibbs Sampler;175
6.1.4.1.3.4;Estimation Results;175
6.1.4.2;3.2 Markov Chain Monte Carlo and Gibbs Sampler;176
6.1.5;4. CONDITIONAL POSTERIOR DISTRIBUTIONS OF THE MARKOV WITCHING MODELS;177
6.1.5.1;4.1 Conditional Posterior Distributions of Regression Parameters;178
6.1.5.2;4.2 Conditional Posterior Distributions of Transition Probability;181
6.1.5.3;4.3 Conditional Posterior Distributions of Hidden States;181
6.1.5.4;4.4 Estimating Markov Switching Models via the Gibbs Sampler;184
6.1.6;5. CASE STUDY;186
6.1.7;ACKNOWLEDGMENTS;191
6.1.8;QUESTIONS FOR DISCUSSION;191
6.1.9;REFERENCES;191
6.1.10;SUGGESTED READING;192
6.1.11;ONLINE RESOURCES;193
6.2;Chapter 7 DETECTION OF EVENTS IN MULTIPLE STREAMS OF SURVEILLANCE DATA;194
6.2.1;CHAPTER OVERVIEW;194
6.2.2;1. INTRODUCTION;195
6.2.3;2. MULTIVARIATE ANALYSIS;197
6.2.3.1;2.1 Modeling and Forecasting of Multivariate Baselines;197
6.2.3.2;2.2 Detection of Events in Multivariate Time Series;199
6.2.4;3. MULTI-STREAM ANALYSIS;203
6.2.4.1;3.1 Consensus Approach;204
6.2.4.1.1;3.1.1 Handcrafting Specific Detectors;206
6.2.4.1.2;3.1.2 Learning Specific Detectors from Data;207
6.2.4.2;3.2 Multi-Stream Spatial Scan;210
6.2.5;4. MULTI-DIMENSIONAL ANALYSIS;211
6.2.6;5. CONCLUSION;215
6.2.7;ACKNOWLEDGMENTS;216
6.2.8;QUESTIONS FOR DISCUSSION;216
6.2.9;REFERENCES;217
6.2.10;SUGGESTED READING;219
6.2.11;ONLINE RESOURCES;219
6.3;Chapter 8 ALGORITHM COMBINATION FOR IMPROVED PERFORMANCE IN BIOSURVEILLANCE;221
6.3.1;CHAPTER OVERVIEW;221
6.3.2;1. INTRODUCTION;221
6.3.3;2. CONTROL CHARTS AND BIOSURVEILLANCE;223
6.3.3.1;2.1 Control Chart Overview;225
6.3.3.2;2.2 Preprocessing Methods;226
6.3.4;3. DATA AND OUTBREAKS;227
6.3.4.1;3.1 Data Description;227
6.3.4.2;3.2 Outbreak Signatures;227
6.3.5;4. COMBINATION MODELS;228
6.3.5.1;4.1 Residual Combination;230
6.3.5.2;4.2 Control Chart Combination;230
6.3.6;5. EMPIRICAL STUDY AND RESULTS;231
6.3.6.1;5.1 Experiment Design;231
6.3.6.2;5.2 Results;232
6.3.6.2.1;5.2.1 Residuals Combination;232
6.3.6.2.2;5.2.2 Control Chart Combination;233
6.3.6.2.3;5.2.3 Combining Residuals and Monitoring;235
6.3.7;6. CONCLUSIONS;236
6.3.8;ACKNOWLEDGEMENTS;236
6.3.9;QUESTIONS FOR DISCUSSION;236
6.3.10;REFERENCES;237
6.3.11;ONLINE RESOURCES;237
6.4;Chapter 9 MODELING IN SPACE AND TIME;238
6.4.1;CHAPTER OVERVIEW;238
6.4.2;1. INTRODUCTION;239
6.4.3;2. MODELING: AN OVERVIEW;239
6.4.4;3. ABOUT STEM;240
6.4.4.1;3.1 A Common Collaborative Framework;241
6.4.4.2;3.2 A Common Representational Framework;242
6.4.4.3;3.3 Creating and Configuring Components;245
6.4.4.3.1;3.3.1 Labels;247
6.4.4.3.2;3.3.2 Disease Model Computations;249
6.4.5;4. CONCLUSION;250
6.4.6;ACKNOWLEDGEMENTS;251
6.4.7;QUESTIONS FOR DISCUSSION;251
6.4.8;REFERENCES;252
6.4.9;SUGGESTED READING;252
6.4.10;ONLINE RESOURCES;253
6.5;Chapter 10 SURVEILLANCE AND EPIDEMIOLOGYOF INFECTIOUS DISEASES USING SPATIALAND TEMPORAL CLUSTERING METHODS;254
6.5.1;CHAPTER OVERVIEW;254
6.5.2;1. INTRODUCTION;255
6.5.3;2. CURRENT COMMONLY USED METHODS IN SPATIAL, TEMPORAL, AND TEMPO-SPATIAL CLUSTERING;256
6.5.3.1;2.1 Temporal Clustering Methods;257
6.5.3.1.1;2.1.1 Historical Limit, the Concept of Moving Average, and Scan Statistics;257
6.5.3.1.1.1;Historical Limit;257
6.5.3.1.1.2;The Application of Moving Average;257
6.5.3.1.2;2.1.2 Cumulative Sum;259
6.5.3.1.3;2.1.3 Time Series;260
6.5.3.2;2.2 Spatial Clustering Methods;260
6.5.3.2.1;2.2.1 Global Clustering Test;261
6.5.3.2.2;2.2.2 Local Clustering Test;262
6.5.3.2.2.1;Scan Statistic;263
6.5.3.2.2.2;Local Indicator of Spatial Autocorrelation;264
6.5.3.2.2.3;GAM and Besag and Newell Tests;264
6.5.3.2.3;2.2.3 Focused Clustering Test;265
6.5.3.3;2.3 Spatial and Temporal Clustering Methods;265
6.5.3.3.1;2.3.1 Knox Method;266
6.5.3.3.2;2.3.2 Space-Time Scan Statistic;266
6.5.4;3. CASE STUDIES USING SPATIAL CLUSTERING METHODS IN INFECTIOUS DISEASE EPIDEMIOLOGY;267
6.5.4.1;3.1 Respiratory Spread;267
6.5.4.2;3.2 GI-Related Transmission;269
6.5.4.3;3.3 Vector-Borne Transmission: Dengue as an Example;269
6.5.4.4;3.4 Zoonosis: Rabies as an Example;271
6.5.4.5;3.5 EID: Avian Influenza as an Example;272
6.5.5;4. CONCLUSIONS, LIMITATIONS AND FUTURE DIRECTIONS;274
6.5.5.1;4.1 What We Have Learned in the Past;274
6.5.5.2;4.2 Limitations of GIS Studies and Unsolved Problems;275
6.5.5.2.1;4.2.1 Data Collection and Quality of GIS Data;275
6.5.5.2.2;4.2.2 Limitations in Statistical Methods and Interpretation of Data;276
6.5.5.3;4.3 Future Directions;277
6.5.5.3.1;4.3.1 Flexibility of the Cluster Method in Detecting Irregular Clusters;277
6.5.5.3.2;4.3.2 Adjustment for Personal Risk Factors;277
6.5.5.3.3;4.3.3 Bayesian Method for Better Prediction [43];277
6.5.6;ACKNOWLEDGEMENTS;278
6.5.7;QUESTIONS FOR DISCUSSION;278
6.5.8;REFERENCES;278
6.5.9;SUGGESTED READING;281
6.5.10;ONLINE RESOURCES;281
6.6;Chapter 11 AGE-ADJUSTMENT IN NATIONAL BIOSURVEILLANCE SYSTEMS;282
6.6.1;CHAPTER OVERVIEW;282
6.6.2;1. INTRODUCTION;283
6.6.2.1;1.1 Disease Surveillance;283
6.6.2.2;1.2 Case Studies of Influenza: Age-Specificity Within Population Subgroups;283
6.6.2.2.1;1.2.1 Influenza and Respiratory Infection Hospitalizations in Milwaukee, Wisconsin (1996–2006);284
6.6.2.2.2;1.2.2 Pneumonia and Influenza in the US Elderly (1991–2004);285
6.6.2.3;1.3 Population Dynamics;286
6.6.3;2. DENOMINATOR DATA SOURCES;287
6.6.3.1;2.1 Decennial Census;287
6.6.3.2;2.2 Intercensal Population Estimates;288
6.6.4;3. GRAPHICAL TOOLS TO ASSESS AGE PATTERNS OF DISEASE;289
6.6.4.1;3.1 Population Pyramids;289
6.6.4.1.1;3.1.1 Disease Pyramids;290
6.6.4.2;3.2 Lexis Surfaces;292
6.6.5;4. ANALYTICAL TOOLS FOR AGE-ADJUSTMENT;294
6.6.5.1;4.1 Age-Period-Cohort Analysis;294
6.6.5.1.1;4.1.1 Background;294
6.6.5.1.2;4.1.2 Model Specification;295
6.6.5.1.3;4.1.3 Graphical Tools;295
6.6.5.2;4.2 Standardization and Decomposition;296
6.6.5.2.1;4.2.1 Direct Standardization;297
6.6.5.2.2;4.2.2 Indirect Standardization;298
6.6.5.2.3;4.2.3 Decomposition;300
6.6.5.3;4.3 Summary Disease Measures;301
6.6.6;5. CONCLUSIONS;302
6.6.7;ACKNOWLEDGEMENTS;302
6.6.8;QUESTIONS FOR DISCUSSION;303
6.6.9;REFERENCES;303
6.6.10;SUGGESTED READING;304
6.6.11;ONLINE RESOURCES;305
6.7;Chapter 12 MODELING IN IMMUNIZATION AND BIOSURVEILLANCE RESEARCH;306
6.7.1;CHAPTER OVERVIEW;306
6.7.2;1. INTRODUCTION;306
6.7.2.1;1.1 Role of Modeling for Vaccination Programs;307
6.7.2.2;1.2 The Interface with Biosurveillance;308
6.7.3;2. MODELING OF VACCINATION PROGRAMS;309
6.7.3.1;2.1 Key Concepts;309
6.7.3.1.1;2.1.1 The Basic Reproductive Number (R0);309
6.7.3.1.2;2.1.2 Force of Infection;309
6.7.3.1.3;2.1.3 Disease States;310
6.7.3.2;2.2 The SIR Model;310
6.7.3.2.1;2.2.1 The Basic Reproduction Number in the SIR Model;311
6.7.3.3;2.3 Endemic Dynamics;312
6.7.3.3.1;2.3.1 The Endemic SIR Model;312
6.7.3.3.2;2.3.2 Immunization;313
6.7.3.3.3;2.3.3 Waning of Immunity;313
6.7.3.3.4;2.3.4 Equilibrium;313
6.7.3.4;2.4 More Realistic Models;316
6.7.3.4.1;2.4.1 Age-Related Risks;316
6.7.3.4.2;2.4.2 Vaccine Efficacy;316
6.7.3.4.3;2.4.3 Stochasticity;317
6.7.3.5;2.5 Special Issues for Vaccination;317
6.7.3.5.1;2.5.1 Data Requirements and Surveillance;318
6.7.4;3. MATHEMATICAL MODELS FOR BIOSURVEILLANCE OF VACCINE-PREVENTABLE DISEASE;319
6.7.4.1;3.1 Models of the Reproductive Number in Immunization Biosurveillance;320
6.7.4.1.1;3.1.1 Surveillance of Disease Elimination;321
6.7.4.1.2;3.1.2 Surveillance of Epidemics;321
6.7.4.1.3;3.1.3 Parameter-Free Epidemic Surveillance;322
6.7.4.2;3.2 Summary;322
6.7.5;ACKNOWLEDGEMENTS;323
6.7.6;QUESTIONS FOR DISCUSSION;323
6.7.7;REFERENCES;324
6.7.8;SUGGESTED READING;325
6.8;Chapter 13 NATURAL LANGUAGE PROCESSING FOR BIOSURVEILLANCE;326
6.8.1;CHAPTER OVERVIEW;326
6.8.2;1. INTRODUCTION;327
6.8.3;2. OVERVIEW OF DATA SOURCES FOR BIOSURVEILLANCE;328
6.8.3.1;2.1 Surveillance from Non-clinical Data Sources;328
6.8.3.1.1;2.1.1 Structured Non-clinical Data;328
6.8.3.1.2;2.1.2 Textual Non-clinical Data;329
6.8.3.2;2.2 Surveillance from Clinical Data Sources;330
6.8.3.2.1;2.2.1 Structured Clinical Data;330
6.8.3.2.2;2.2.2 Textual Clinical Data;330
6.8.4;3. SURVEILLANCE FROM TEXTUAL CLINICAL DATA SOURCES;331
6.8.4.1;3.1 Methodologies for Processing Clinical Textual Data;331
6.8.4.1.1;3.1.1 Keyword-Based NLP Techniques;331
6.8.4.1.2;3.1.2 Statistical NLP Techniques;332
6.8.4.1.3;3.1.3 Symbolic NLP Techniques;333
6.8.4.1.4;3.1.4 Evaluation of Text Processing Methods in Biosurveillance;334
6.8.4.2;3.2 Textual Documentation Generated from a Visit to a Healthcare Facility;335
6.8.4.2.1;3.2.1 Making Use of Textual Documentation for Detection and Characterization;335
6.8.4.3;3.3 Overview of Clinical Textual Data Sources and Their Application in Biosurveillance;337
6.8.4.3.1;3.3.1 Triage Chief Complaints;341
6.8.4.3.1.1;3.3.1.1 Characteristics of Chief Complaint Classifiers;342
6.8.4.3.1.2;3.3.1.2 Performance of Chief Complaint Classifiers;345
6.8.4.3.2;3.3.2 Ambulatory Visit Notes;346
6.8.4.3.3;3.3.3 Inpatient Reports: Progress and Findings;348
6.8.4.3.4;3.3.4 Discharge Reports;350
6.8.5;4. CONCLUSION AND DISCUSSION;351
6.8.6;ACKNOWLEDGMENTS;352
6.8.7;QUESTIONS FOR DISCUSSION;353
6.8.8;REFERENCES;353
6.8.9;SUGGESTED READING;357
6.8.10;ONLINE RESOURCES;357
6.9;Chapter 14 KNOWLEDGE MAPPING FOR BIOTERRORISM-RELATED LITERATURE;358
6.9.1;CHAPTER OVERVIEW;358
6.9.2;1. INTRODUCTION;358
6.9.3;2. LITERATURE REVIEW;360
6.9.3.1;2.1 Online Resources for Knowledge Mapping;360
6.9.3.2;2.2 Units of Analysis for Knowledge Mapping;362
6.9.3.3;2.3 Analysis Techniques for Knowledge Mapping;364
6.9.3.3.1;2.3.1 Text Mining;364
6.9.3.3.2;2.3.2 Network Analysis;365
6.9.3.3.3;2.3.3 Information Visualization;366
6.9.4;3. RESEARCH DESIGN;367
6.9.4.1;3.1 Data Acquisition;368
6.9.4.2;3.2 Data Parsing and Cleaning;368
6.9.4.3;3.3 Data Analysis;368
6.9.5;4. RESEARCH TESTBED;368
6.9.6;5. ANALYSIS RESULTS AND DISCUSSION;371
6.9.6.1;5.1 Human Agents/Diseases-Related BioterrorismResearch;372
6.9.6.1.1;5.1.1 Productivity Status;372
6.9.6.1.2;5.1.2 Collaboration Status;373
6.9.6.1.3;5.1.3 Emerging Topics;375
6.9.6.2;5.2 Animal Agents/Diseases-Related Bioterrorism Research;377
6.9.6.2.1;5.2.1 Productivity Status;377
6.9.6.2.2;5.2.2 Collaboration Status;379
6.9.6.2.3;5.2.3 Emerging Topics;379
6.9.7;6. CONCLUSION;381
6.9.8;ACKNOWLEDGEMENTS;382
6.9.9;QUESTIONS FOR DISCUSSION;382
6.9.10;REFERENCES;382
6.9.11;SUGGESTED READING;384
6.9.12;ONLINE RESOURCES;384
6.10;Chapter 15 SOCIAL NETWORK ANALYSIS FOR CONTACT TRACING;386
6.10.1;CHAPTER OVERVIEW;386
6.10.2;1. INTRODUCTION;387
6.10.3;2. NETWORK VISUALIZATION AND MEASURES IN SNA;388
6.10.4;3. SNA IN EPIDEMIOLOGY;390
6.10.4.1;3.1 Static Analysis of Linkage in a Contact Network for STDs;391
6.10.4.2;3.2 Transmission Dynamics of STDs;391
6.10.4.3;3.3 From STDs to Tuberculosis;393
6.10.4.4;3.4 Summary of SNA Studies in Epidemiology;394
6.10.5;4. A CASE STUDY: THE SARS OUTBREAK IN TAIWAN;395
6.10.5.1;4.1 Taiwan SARS Outbreak and Contact Tracing Dataset;395
6.10.5.2;4.2 Contact Network Construction;397
6.10.5.3;4.3 Connectivity Analysis;397
6.10.5.4;4.4 Topology Analysis;398
6.10.6;5. CONCLUSIONS;401
6.10.7;ACKNOWLEDGEMENTS;402
6.10.8;QUESTIONS FOR DISCUSSION;402
6.10.9;REFERENCES;403
6.10.10;SUGGESTED READINGS;405
6.10.11;ONLINE RESOURCES;405
7;UNIT III: EMERGENCY RESPONSE, AND CASE STUDIES;406
7.1;Chapter 16 MULTI-AGENT MODELING OF BIOLOGICAL AND CHEMICAL THREATS;407
7.1.1;CHAPTER OVERVIEW;407
7.1.2;1. INTRODUCTION;408
7.1.3;2. WHY MULTI-AGENT MODELING;408
7.1.4;3. BIOWAR;409
7.1.4.1;3.1 Agents;410
7.1.4.1.1;3.1.1 Agent Characteristics;410
7.1.4.1.2;3.1.2 Agent Behavior;411
7.1.4.1.3;3.1.3 Daily Agent Cycle;411
7.1.4.1.4;3.1.4 Agent Interaction;412
7.1.4.2;3.2 Diseases;413
7.1.4.2.1;3.2.1 Disease Model;413
7.1.4.2.2;3.2.2 Disease Introduction;414
7.1.4.2.3;3.2.3 Disease Progression;414
7.1.4.2.4;3.2.4 Medical Diagnosis and Treatment;415
7.1.4.3;3.3 Cities;417
7.1.4.4;3.4 Additional Features;418
7.1.4.4.1;3.4.1 Climate and Weather;418
7.1.4.4.2;3.4.2 Chemical Attacks;418
7.1.4.4.3;3.4.3 Interventions;418
7.1.4.4.4;3.4.4 Scalability and Configurability;419
7.1.5;4. ILLUSTRATIVE RESULTS;420
7.1.6;5. VALIDATION ISSUES;421
7.1.7;6. CONCLUSION;422
7.1.8;ACKNOWLEDGEMENTS;423
7.1.9;QUESTIONS FOR DISCUSSION;424
7.1.10;REFERENCES;424
7.1.11;SUGGESTED READINGS;425
7.1.12;ONLINE RESOURCES;425
7.2;Chapter 17 INTEGRATED HEALTH ALERTING AND NOTIFICATION;427
7.2.1;CHAPTER OVERVIEW;427
7.2.2;1. INTRODUCTION;428
7.2.3;2. AN INFRASTRUCTURE FOR HEALTH ALERT AND NOTIFICATION SYSTEMS;429
7.2.4;3. REQUIREMENTS FOR A HEALTH ALERT AND NOTIFICATION SYSTEM;430
7.2.4.1;3.1 System Architecture;430
7.2.4.1.1;3.1.1 System Components;433
7.2.4.1.2;3.1.2 Unified Messaging Concept;434
7.2.4.1.3;3.1.3 NYSDOH Communication Directory;435
7.2.4.1.4;3.1.4 Data Integration and Communication Using XML Messaging;436
7.2.4.1.5;3.1.5 Communication Methods;437
7.2.4.2;3.2 Standard Alert Message Distribution Framework for Data Sharing Among Emergency Information Systems;438
7.2.5;4. CASE STUDY;439
7.2.6;5. DISCUSSION;445
7.2.7;6. CONCLUSIONS;446
7.2.8;ACKNOWLEDGEMENTS;447
7.2.9;QUESTIONS FOR DISCUSSION;448
7.2.10;REFERENCES;449
7.2.11;SUGGESTED READING;450
7.2.12;ONLINE RESOURCES;450
7.3;Chapter 18 DESIGN AND PERFORMANCE OF A PUBLIC HEALTH PREPAREDNESS INFORMATICS FRAMEWORK;451
7.3.1;CHAPTER OVERVIEW;451
7.3.2;1. INTRODUCTION;452
7.3.3;2. MODEL INFORMATICS FRAMEWORK FOR HEALTH INFORMATION EXCHANGE;456
7.3.4;3. NY STATE’S INFORMATICS FRAMEWORK FOR HEALTH INFORMATION EXCHANGE AND INTEGRAL SUPPORT OF PUBLIC HEALTH PREPAREDNESS;457
7.3.5;4. EVALUATION OF FRAMEWORK RESPONSE DURING A FULL-SCALE EXERCISE;461
7.3.5.1;4.1 Exercise Scenario, Scope, and Extent;461
7.3.5.2;4.2 Preparedness Functions Used in the Exercise;461
7.3.5.3;4.3 Exercise Injects and Data Pushed to Exercise Participants;463
7.3.5.4;4.4 Methodology Used in Measuring Preparedness Function Responses;465
7.3.5.5;4.5 Responses to Informational and PHEP Action-Request Injects;466
7.3.5.6;4.6 Responses to Operational Data Injects;468
7.3.5.7;4.7 Accessing Health Alert Postings by Key Roles at Local Health Departments and Hospitals;470
7.3.5.8;4.8 Usage of CDEX Event-Specific Website for Situational Awareness;470
7.3.5.9;4.9 Executive DashBoard Usage for Situational Awareness by Key Decision Makers;474
7.3.6;5. DISCUSSION AND LESSONS LEARNED;476
7.3.6.1;5.1 How Well Did the Observed Exercise Responses Meet Expectations?;476
7.3.6.2;5.2 How Does the HCS Informatics Framework Enable a State of PHEP “Readiness”?;477
7.3.7;6. CONCLUSIONS;479
7.3.8;ACKNOWLEDGEMENTS;479
7.3.9;QUESTIONS FOR DISCUSSION;480
7.3.10;REFERENCES;480
7.3.11;SUGGESTED READING;483
7.3.12;ONLINE RESOURCES;483
7.4;Chapter 19 SYSTEM EVALUATION AND USER TECHNOLOGY ADOPTION;484
7.4.1;CHAPTER OVERVIEW;484
7.4.2;1. INTRODUCTION;485
7.4.3;2. AN OVERVIEW OF BIOPORTAL;487
7.4.4;3. AN EXPERIMENT-BASED EVALUATION STUDY AND KEY RESULTS;488
7.4.4.1;3.1 Hypotheses;489
7.4.4.2;3.2 Experimental Design;490
7.4.4.3;3.3 Measurements;490
7.4.4.4;3.4 Subjects;491
7.4.4.5;3.5 Experimental Tasks;491
7.4.4.6;3.6 Data Collection;491
7.4.4.7;3.7 Evaluation Results;492
7.4.5;4. A FIELD USER STUDY AND KEY RESULTS;493
7.4.5.1;4.1 Research Questions;493
7.4.5.2;4.2 Measurements;493
7.4.5.3;4.3 Subjects;494
7.4.5.4;4.4 Tasks;494
7.4.5.5;4.5 Evaluation Results;494
7.4.6;5. CONCLUSION;495
7.4.7;QUESTIONS FOR DISCUSSION;496
7.4.7.1;Appendix 2: Listing of Analysis Scenarios and Tasks Used in the Experiment-Based Evaluation Study;498
7.4.7.2;Appendix 3: Listing of Analysis Scenarios and Tasks Used in the Field Evaluation Study;499
7.4.8;REFERENCES;500
7.4.9;SUGGESTED READING;501
7.4.10;ONLINE RESOURCES;502
7.5;Chapter 20 SYNDROMIC SURVEILLANCE FOR THE G8 HOKKAIDO TOYAKO SUMMIT MEETING;503
7.5.1;CHAPTER OVERVIEW;503
7.5.2;1. INTRODUCTION;504
7.5.3;2. BACKGROUND;505
7.5.4;3. METHODS;506
7.5.4.1;3.1. Syndromic Surveillance for Prescriptions;506
7.5.4.2;3.2. Syndromic Surveillance for Ambulance Transfer;509
7.5.4.3;3.3. Syndromic Surveillance for OTC Drug Sales;511
7.5.4.4;3.4. Joint Conference for Evaluation of Aberration Signals from the Syndromic Surveillance System;512
7.5.5;4. RESULTS;512
7.5.6;5. CONCLUSIONS AND DISCUSSION;514
7.5.7;6. OTHER SYNDROMIC SURVEILLANCE SYSTEMS AT THE EXPERIMENTAL LEVEL IN JAPAN;515
7.5.7.1;6.1. Syndromic Surveillance from EMRs;515
7.5.7.2;6.2. Syndromic Surveillance from Orders for Medical Examinations;519
7.5.7.3;6.3. Syndromic Surveillance from Absenteeism at School;519
7.5.7.4;6.4. Syndromic Surveillance for Nosocomial Outbreak;520
7.5.8;ACKNOWDLEDGEMENT;520
7.5.9;QUESTIONS FOR DISCUSSION;520
7.5.10;REFERENCES;521
8;INDEX;522