Infectious Disease Informatics and Biosurveillance (eBook)

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.

PREFACE 6
SCOPE AND ORGANIZATION 7
AUDIENCE 10
TABLE OF CONTENTS 12
LIST OF CONTRIBUTORS 28
EDITORS’ BIOGRAPHIES 34
UNIT I: INFORMATICS INFRASTRUCTURE AND DATA SOURCES 54
Chapter 1 REAL-TIME PUBLIC HEALTH BIOSURVEILLANCE 55
CHAPTER OVERVIEW 55
1. INTRODUCTION 56
2. BACKGROUND AND RECENT HISTORY 57
2.1 Public Health Surveillance 57
2.2 Impact of the Fall of 2001 on Biosurveillance 57
2.3 BioSense, BioWatch and the National Biosurveillance Integration System 60
3. POLICY CONSIDERATIONS IN BIOSURVEILLANCE 62
3.1 Federalism 63
3.2 Privacy and Data Use 65
3.3 Other Policy Considerations 66
4. ACHIEVING INTEGRATED, REAL-TIME BIOSURVEILLANCE 66
4.1 Stakeholder Perspectives and Information Requirements 67
4.2 Analytic Requirements 68
4.3 Policy Requirements 70
5. CONCLUSION AND DISCUSSION 70
QUESTIONS FOR DISCUSSION 71
REFERENCES 71
SUGGESTED READING 73
ONLINE RESOURCES 74
Chapter 2 DESIGNING ETHICAL PRACTICE IN BIOSURVEILLANCE 75
CHAPTER OVERVIEW 75
1. INTRODUCTION 76
2. BACKGROUND 76
3. OVERVIEW: INFORMATION PROTECTION 77
3.1 Fair Information Practice Principles 78
3.2 Proprietary Information 79
3.3 Individually Identifiable Information 80
4. METHODS 82
4.1 Information Scenarios 82
4.2 Laws, Regulations, and Good Practice in Managing Sensitive Information 87
4.3 Case Study: The Terrorism Information Awareness Program 88
5. RESULTS AND ANALYSIS 89
5.1 Policies and Procedures 90
5.2 Technical Requirements 91
5.3 Doctrine Management Process 92
6. CONCLUSION 93
ACKNOWLEDGMENTS 93
QUESTIONS FOR DISCUSSION 94
REFERENCES 94
SUGGESTED READING 96
ONLINE RESOURCES 96
Chapter 3 USING EMERGENCY DEPARTMENT DATA FOR BIOSURVEILLANCE: THE NORTH CAROLINA EXPERIENCE 97
CHAPTER OVERVIEW 97
1. INTRODUCTION 98
2. LITERATURE REVIEW/OVERVIEW OF THE FIELD 100
2.1 History of ED Data Use for Biosurveillance 100
2.2 Current Status of ED Data Use for Biosurveillance 100
2.3 Infectious Disease Syndrome-Based Surveillance Using ED Data 101
2.4 ISDS Consultative Syndrome Group 102
3. TECHNICAL APPROACHES FOR GROUPING ED DATA INTO SYNDROMES FOR BIOSURVEILLANCE 103
3.1 Dealing with Negation 104
3.2 Issues with Diagnosis Code Data 104
4. BIOSURVEILLANCE IN NORTH CAROLINA 105
4.1 History of Syndrome Definitions in NC 106
4.2 The Importance of Data Quality 107
4.3 NC DETECT Case Studies 107
4.3.1 Public Health Surveillance During and After Hurricanes 107
4.3.2 Influenza 109
4.3.3 Early Event Detection 110
4.3.4 Bioterrorism Agent Report 110
4.3.5 Case Finding & Infectious Disease Outbreak Monitoring
4.3.6 Infectious Disease Retrospective Analyses 111
4.3.7 Injury Surveillance 112
4.4 Conclusions and Discussion 112
4.5 Evaluation of NC DETECT 112
5. CONCLUSION 114
ACKNOWLEDGEMENTS 114
QUESTIONS FOR DISCUSSION 114
REFERENCES 115
SUGGESTED READING 118
ONLINE RESOURCES 118
Chapter 4 CLINICAL LABORATORY DATA FOR BIOSURVEILLANCE 119
CHAPTER OVERVIEW 119
1. INTRODUCTION 119
2. TYPES OF SURVEILLANCE 120
2.1 Laboratory Data for Biosurveillance 122
2.2 The Clinical Laboratory 123
2.2.1 Development of the Clinical Laboratory 123
2.2.2 Laboratory Types 124
2.2.3 Sources of Laboratory Data for Biosurveillance 126
2.2.4 Components of Laboratory Data for Biosurveillance 127
2.2.5 Data Standards for Biosurveillance 129
2.2.6 Data Analysis 129
2.2.7 Underlying Data Characteristics 130
Patient Characteristics 131
Provider Characteristics 132
External Drivers 133
2.3 Relevant Experience and Case Studies 134
2.3.1 The Emergence of West Nile Virus in New York City 134
2.3.2 Lyme Disease in New Jersey 135
2.3.3 Hepatitis in New York City 135
2.3.4 Projections in Florida 135
3. CONCLUSIONS AND DISCUSSION 136
QUESTIONS FOR DISCUSSION 136
REFERENCES 137
SUGGESTED READING 139
ONLINE RESOURCES 139
Chapter 5 BIOSURVEILLANCE BASED ON TEST ORDERS FROM VETERINARY DIAGNOSTIC LABS 140
CHAPTER OVERVIEW 140
1. INTRODUCTION 141
1.1 Wildlife as Sentinels of Disease 141
1.2 Pets as Sentinel Indicators of Disease 141
1.3 “One Medicine” 142
2. SURVEILLANCE FOR OUTBREAKS OF ZOONOTIC DISEASE 143
2.1 National Animal Health Reporting System 143
2.2 National Animal Health Laboratory Network 144
2.3 Veterinary Services Electronic Surveillance Project 144
2.4 Rapid Syndrome Validation Project for Animals 145
2.5 Other Manual Entry Systems 145
3. IMPROVING OUTBREAK DETECTION 145
3.1 Syndromic Surveillance 146
3.1.1 Preferred Data 147
3.1.2 Data Criteria 147
3.2 Veterinary Diagnostic Laboratories 148
3.2.1 Case Evidence to Support Using Data 148
3.2.2 Determining Animal Representation 150
3.2.3 Estimating Human Representation 150
3.2.4 Availability and Timeliness 152
4. CONCLUSION 153
QUESTIONS FOR DISCUSSION 154
REFERENCES 155
SUGGESTED READING 158
ONLINE RESOURCES 158
UNIT II: SURVEILLANCE ANALYTICS 159
Chapter 6 MARKOV SWITCHING MODELS FOR OUTBREAK DETECTION 160
CHAPTER OVERVIEW 160
1. INTRODUCTION 161
2. MARKOV SWITCHING MODELS 163
2.1 Time Series Generated by Markov Switching Models: An Illustrative Example 165
2.2 Estimation Methods for Markov Switching Models 167
3. BAYESIAN INFERENCE: AN OVERVIEW 169
3.1 Maximum Likelihood Estimation and Bayesian Inference: An Illustrative Example 169
3.1.1 Likelihood Maximization 169
3.1.2 Bayesian Inference 170
3.1.3 A Numerical Example 173
Data Generating Process 173
Likelihood Maximization 175
Bayesian Inference Using Gibbs Sampler 175
Estimation Results 175
3.2 Markov Chain Monte Carlo and Gibbs Sampler 176
4. CONDITIONAL POSTERIOR DISTRIBUTIONS OF THE MARKOV WITCHING MODELS 177
4.1 Conditional Posterior Distributions of Regression Parameters 178
4.2 Conditional Posterior Distributions of Transition Probability 181
4.3 Conditional Posterior Distributions of Hidden States 181
4.4 Estimating Markov Switching Models via the Gibbs Sampler 184
5. CASE STUDY 186
ACKNOWLEDGMENTS 191
QUESTIONS FOR DISCUSSION 191
REFERENCES 191
SUGGESTED READING 192
ONLINE RESOURCES 193
Chapter 7 DETECTION OF EVENTS IN MULTIPLE STREAMS OF SURVEILLANCE DATA 194
CHAPTER OVERVIEW 194
1. INTRODUCTION 195
2. MULTIVARIATE ANALYSIS 197
2.1 Modeling and Forecasting of Multivariate Baselines 197
2.2 Detection of Events in Multivariate Time Series 199
3. MULTI-STREAM ANALYSIS 203
3.1 Consensus Approach 204
3.1.1 Handcrafting Specific Detectors 206
3.1.2 Learning Specific Detectors from Data 207
3.2 Multi-Stream Spatial Scan 210
4. MULTI-DIMENSIONAL ANALYSIS 211
5. CONCLUSION 215
ACKNOWLEDGMENTS 216
QUESTIONS FOR DISCUSSION 216
REFERENCES 217
SUGGESTED READING 219
ONLINE RESOURCES 219
Chapter 8 ALGORITHM COMBINATION FOR IMPROVED PERFORMANCE IN BIOSURVEILLANCE 221
CHAPTER OVERVIEW 221
1. INTRODUCTION 221
2. CONTROL CHARTS AND BIOSURVEILLANCE 223
2.1 Control Chart Overview 225
2.2 Preprocessing Methods 226
3. DATA AND OUTBREAKS 227
3.1 Data Description 227
3.2 Outbreak Signatures 227
4. COMBINATION MODELS 228
4.1 Residual Combination 230
4.2 Control Chart Combination 230
5. EMPIRICAL STUDY AND RESULTS 231
5.1 Experiment Design 231
5.2 Results 232
5.2.1 Residuals Combination 232
5.2.2 Control Chart Combination 233
5.2.3 Combining Residuals and Monitoring 235
6. CONCLUSIONS 236
ACKNOWLEDGEMENTS 236
QUESTIONS FOR DISCUSSION 236
REFERENCES 237
ONLINE RESOURCES 237
Chapter 9 MODELING IN SPACE AND TIME 238
CHAPTER OVERVIEW 238
1. INTRODUCTION 239
2. MODELING: AN OVERVIEW 239
3. ABOUT STEM 240
3.1 A Common Collaborative Framework 241
3.2 A Common Representational Framework 242
3.3 Creating and Configuring Components 245
3.3.1 Labels 247
3.3.2 Disease Model Computations 249
4. CONCLUSION 250
ACKNOWLEDGEMENTS 251
QUESTIONS FOR DISCUSSION 251
REFERENCES 252
SUGGESTED READING 252
ONLINE RESOURCES 253
Chapter 10 SURVEILLANCE AND EPIDEMIOLOGYOF INFECTIOUS DISEASES USING SPATIALAND TEMPORAL CLUSTERING METHODS 254
CHAPTER OVERVIEW 254
1. INTRODUCTION 255
2. CURRENT COMMONLY USED METHODS IN SPATIAL, TEMPORAL, AND TEMPO-SPATIAL CLUSTERING 256
2.1 Temporal Clustering Methods 257
2.1.1 Historical Limit, the Concept of Moving Average, and Scan Statistics 257
Historical Limit 257
The Application of Moving Average 257
2.1.2 Cumulative Sum 259
2.1.3 Time Series 260
2.2 Spatial Clustering Methods 260
2.2.1 Global Clustering Test 261
2.2.2 Local Clustering Test 262
Scan Statistic 263
Local Indicator of Spatial Autocorrelation 264
GAM and Besag and Newell Tests 264
2.2.3 Focused Clustering Test 265
2.3 Spatial and Temporal Clustering Methods 265
2.3.1 Knox Method 266
2.3.2 Space-Time Scan Statistic 266
3. CASE STUDIES USING SPATIAL CLUSTERING METHODS IN INFECTIOUS DISEASE EPIDEMIOLOGY 267
3.1 Respiratory Spread 267
3.2 GI-Related Transmission 269
3.3 Vector-Borne Transmission: Dengue as an Example 269
3.4 Zoonosis: Rabies as an Example 271
3.5 EID: Avian Influenza as an Example 272
4. CONCLUSIONS, LIMITATIONS AND FUTURE DIRECTIONS 274
4.1 What We Have Learned in the Past 274
4.2 Limitations of GIS Studies and Unsolved Problems 275
4.2.1 Data Collection and Quality of GIS Data 275
4.2.2 Limitations in Statistical Methods and Interpretation of Data 276
4.3 Future Directions 277
4.3.1 Flexibility of the Cluster Method in Detecting Irregular Clusters 277
4.3.2 Adjustment for Personal Risk Factors 277
4.3.3 Bayesian Method for Better Prediction [43] 277
ACKNOWLEDGEMENTS 278
QUESTIONS FOR DISCUSSION 278
REFERENCES 278
SUGGESTED READING 281
ONLINE RESOURCES 281
Chapter 11 AGE-ADJUSTMENT IN NATIONAL BIOSURVEILLANCE SYSTEMS 282
CHAPTER OVERVIEW 282
1. INTRODUCTION 283
1.1 Disease Surveillance 283
1.2 Case Studies of Influenza: Age-Specificity Within Population Subgroups 283
1.2.1 Influenza and Respiratory Infection Hospitalizations in Milwaukee, Wisconsin (1996–2006) 284
1.2.2 Pneumonia and Influenza in the US Elderly (1991–2004) 285
1.3 Population Dynamics 286
2. DENOMINATOR DATA SOURCES 287
2.1 Decennial Census 287
2.2 Intercensal Population Estimates 288
3. GRAPHICAL TOOLS TO ASSESS AGE PATTERNS OF DISEASE 289
3.1 Population Pyramids 289
3.1.1 Disease Pyramids 290
3.2 Lexis Surfaces 292
4. ANALYTICAL TOOLS FOR AGE-ADJUSTMENT 294
4.1 Age-Period-Cohort Analysis 294
4.1.1 Background 294
4.1.2 Model Specification 295
4.1.3 Graphical Tools 295
4.2 Standardization and Decomposition 296
4.2.1 Direct Standardization 297
4.2.2 Indirect Standardization 298
4.2.3 Decomposition 300
4.3 Summary Disease Measures 301
5. CONCLUSIONS 302
ACKNOWLEDGEMENTS 302
QUESTIONS FOR DISCUSSION 303
REFERENCES 303
SUGGESTED READING 304
ONLINE RESOURCES 305
Chapter 12 MODELING IN IMMUNIZATION AND BIOSURVEILLANCE RESEARCH 306
CHAPTER OVERVIEW 306
1. INTRODUCTION 306
1.1 Role of Modeling for Vaccination Programs 307
1.2 The Interface with Biosurveillance 308
2. MODELING OF VACCINATION PROGRAMS 309
2.1 Key Concepts 309
2.1.1 The Basic Reproductive Number (R0) 309
2.1.2 Force of Infection 309
2.1.3 Disease States 310
2.2 The SIR Model 310
2.2.1 The Basic Reproduction Number in the SIR Model 311
2.3 Endemic Dynamics 312
2.3.1 The Endemic SIR Model 312
2.3.2 Immunization 313
2.3.3 Waning of Immunity 313
2.3.4 Equilibrium 313
2.4 More Realistic Models 316
2.4.1 Age-Related Risks 316
2.4.2 Vaccine Efficacy 316
2.4.3 Stochasticity 317
2.5 Special Issues for Vaccination 317
2.5.1 Data Requirements and Surveillance 318
3. MATHEMATICAL MODELS FOR BIOSURVEILLANCE OF VACCINE-PREVENTABLE DISEASE 319
3.1 Models of the Reproductive Number in Immunization Biosurveillance 320
3.1.1 Surveillance of Disease Elimination 321
3.1.2 Surveillance of Epidemics 321
3.1.3 Parameter-Free Epidemic Surveillance 322
3.2 Summary 322
ACKNOWLEDGEMENTS 323
QUESTIONS FOR DISCUSSION 323
REFERENCES 324
SUGGESTED READING 325
Chapter 13 NATURAL LANGUAGE PROCESSING FOR BIOSURVEILLANCE 326
CHAPTER OVERVIEW 326
1. INTRODUCTION 327
2. OVERVIEW OF DATA SOURCES FOR BIOSURVEILLANCE 328
2.1 Surveillance from Non-clinical Data Sources 328
2.1.1 Structured Non-clinical Data 328
2.1.2 Textual Non-clinical Data 329
2.2 Surveillance from Clinical Data Sources 330
2.2.1 Structured Clinical Data 330
2.2.2 Textual Clinical Data 330
3. SURVEILLANCE FROM TEXTUAL CLINICAL DATA SOURCES 331
3.1 Methodologies for Processing Clinical Textual Data 331
3.1.1 Keyword-Based NLP Techniques 331
3.1.2 Statistical NLP Techniques 332
3.1.3 Symbolic NLP Techniques 333
3.1.4 Evaluation of Text Processing Methods in Biosurveillance 334
3.2 Textual Documentation Generated from a Visit to a Healthcare Facility 335
3.2.1 Making Use of Textual Documentation for Detection and Characterization 335
3.3 Overview of Clinical Textual Data Sources and Their Application in Biosurveillance 337
3.3.1 Triage Chief Complaints 341
3.3.1.1 Characteristics of Chief Complaint Classifiers 342
3.3.1.2 Performance of Chief Complaint Classifiers 345
3.3.2 Ambulatory Visit Notes 346
3.3.3 Inpatient Reports: Progress and Findings 348
3.3.4 Discharge Reports 350
4. CONCLUSION AND DISCUSSION 351
ACKNOWLEDGMENTS 352
QUESTIONS FOR DISCUSSION 353
REFERENCES 353
SUGGESTED READING 357
ONLINE RESOURCES 357
Chapter 14 KNOWLEDGE MAPPING FOR BIOTERRORISM-RELATED LITERATURE 358
CHAPTER OVERVIEW 358
1. INTRODUCTION 358
2. LITERATURE REVIEW 360
2.1 Online Resources for Knowledge Mapping 360
2.2 Units of Analysis for Knowledge Mapping 362
2.3 Analysis Techniques for Knowledge Mapping 364
2.3.1 Text Mining 364
2.3.2 Network Analysis 365
2.3.3 Information Visualization 366
3. RESEARCH DESIGN 367
3.1 Data Acquisition 368
3.2 Data Parsing and Cleaning 368
3.3 Data Analysis 368
4. RESEARCH TESTBED 368
5. ANALYSIS RESULTS AND DISCUSSION 371
5.1 Human Agents/Diseases-Related BioterrorismResearch 372
5.1.1 Productivity Status 372
5.1.2 Collaboration Status 373
5.1.3 Emerging Topics 375
5.2 Animal Agents/Diseases-Related Bioterrorism Research 377
5.2.1 Productivity Status 377
5.2.2 Collaboration Status 379
5.2.3 Emerging Topics 379
6. CONCLUSION 381
ACKNOWLEDGEMENTS 382
QUESTIONS FOR DISCUSSION 382
REFERENCES 382
SUGGESTED READING 384
ONLINE RESOURCES 384
Chapter 15 SOCIAL NETWORK ANALYSIS FOR CONTACT TRACING 386
CHAPTER OVERVIEW 386
1. INTRODUCTION 387
2. NETWORK VISUALIZATION AND MEASURES IN SNA 388
3. SNA IN EPIDEMIOLOGY 390
3.1 Static Analysis of Linkage in a Contact Network for STDs 391
3.2 Transmission Dynamics of STDs 391
3.3 From STDs to Tuberculosis 393
3.4 Summary of SNA Studies in Epidemiology 394
4. A CASE STUDY: THE SARS OUTBREAK IN TAIWAN 395
4.1 Taiwan SARS Outbreak and Contact Tracing Dataset 395
4.2 Contact Network Construction 397
4.3 Connectivity Analysis 397
4.4 Topology Analysis 398
5. CONCLUSIONS 401
ACKNOWLEDGEMENTS 402
QUESTIONS FOR DISCUSSION 402
REFERENCES 403
SUGGESTED READINGS 405
ONLINE RESOURCES 405
UNIT III: EMERGENCY RESPONSE, AND CASE STUDIES 406
Chapter 16 MULTI-AGENT MODELING OF BIOLOGICAL AND CHEMICAL THREATS 407
CHAPTER OVERVIEW 407
1. INTRODUCTION 408
2. WHY MULTI-AGENT MODELING 408
3. BIOWAR 409
3.1 Agents 410
3.1.1 Agent Characteristics 410
3.1.2 Agent Behavior 411
3.1.3 Daily Agent Cycle 411
3.1.4 Agent Interaction 412
3.2 Diseases 413
3.2.1 Disease Model 413
3.2.2 Disease Introduction 414
3.2.3 Disease Progression 414
3.2.4 Medical Diagnosis and Treatment 415
3.3 Cities 417
3.4 Additional Features 418
3.4.1 Climate and Weather 418
3.4.2 Chemical Attacks 418
3.4.3 Interventions 418
3.4.4 Scalability and Configurability 419
4. ILLUSTRATIVE RESULTS 420
5. VALIDATION ISSUES 421
6. CONCLUSION 422
ACKNOWLEDGEMENTS 423
QUESTIONS FOR DISCUSSION 424
REFERENCES 424
SUGGESTED READINGS 425
ONLINE RESOURCES 425
Chapter 17 INTEGRATED HEALTH ALERTING AND NOTIFICATION 427
CHAPTER OVERVIEW 427
1. INTRODUCTION 428
2. AN INFRASTRUCTURE FOR HEALTH ALERT AND NOTIFICATION SYSTEMS 429
3. REQUIREMENTS FOR A HEALTH ALERT AND NOTIFICATION SYSTEM 430
3.1 System Architecture 430
3.1.1 System Components 433
3.1.2 Unified Messaging Concept 434
3.1.3 NYSDOH Communication Directory 435
3.1.4 Data Integration and Communication Using XML Messaging 436
3.1.5 Communication Methods 437
3.2 Standard Alert Message Distribution Framework for Data Sharing Among Emergency Information Systems 438
4. CASE STUDY 439
5. DISCUSSION 445
6. CONCLUSIONS 446
ACKNOWLEDGEMENTS 447
QUESTIONS FOR DISCUSSION 448
REFERENCES 449
SUGGESTED READING 450
ONLINE RESOURCES 450
Chapter 18 DESIGN AND PERFORMANCE OF A PUBLIC HEALTH PREPAREDNESS INFORMATICS FRAMEWORK 451
CHAPTER OVERVIEW 451
1. INTRODUCTION 452
2. MODEL INFORMATICS FRAMEWORK FOR HEALTH INFORMATION EXCHANGE 456
3. NY STATE’S INFORMATICS FRAMEWORK FOR HEALTH INFORMATION EXCHANGE AND INTEGRAL SUPPORT OF PUBLIC HEALTH PREPAREDNESS 457
4. EVALUATION OF FRAMEWORK RESPONSE DURING A FULL-SCALE EXERCISE 461
4.1 Exercise Scenario, Scope, and Extent 461
4.2 Preparedness Functions Used in the Exercise 461
4.3 Exercise Injects and Data Pushed to Exercise Participants 463
4.4 Methodology Used in Measuring Preparedness Function Responses 465
4.5 Responses to Informational and PHEP Action-Request Injects 466
4.6 Responses to Operational Data Injects 468
4.7 Accessing Health Alert Postings by Key Roles at Local Health Departments and Hospitals 470
4.8 Usage of CDEX Event-Specific Website for Situational Awareness 470
4.9 Executive DashBoard Usage for Situational Awareness by Key Decision Makers 474
5. DISCUSSION AND LESSONS LEARNED 476
5.1 How Well Did the Observed Exercise Responses Meet Expectations? 476
5.2 How Does the HCS Informatics Framework Enable a State of PHEP “Readiness”? 477
6. CONCLUSIONS 479
ACKNOWLEDGEMENTS 479
QUESTIONS FOR DISCUSSION 480
REFERENCES 480
SUGGESTED READING 483
ONLINE RESOURCES 483
Chapter 19 SYSTEM EVALUATION AND USER TECHNOLOGY ADOPTION 484
CHAPTER OVERVIEW 484
1. INTRODUCTION 485
2. AN OVERVIEW OF BIOPORTAL 487
3. AN EXPERIMENT-BASED EVALUATION STUDY AND KEY RESULTS 488
3.1 Hypotheses 489
3.2 Experimental Design 490
3.3 Measurements 490
3.4 Subjects 491
3.5 Experimental Tasks 491
3.6 Data Collection 491
3.7 Evaluation Results 492
4. A FIELD USER STUDY AND KEY RESULTS 493
4.1 Research Questions 493
4.2 Measurements 493
4.3 Subjects 494
4.4 Tasks 494
4.5 Evaluation Results 494
5. CONCLUSION 495
QUESTIONS FOR DISCUSSION 496
Appendix 2: Listing of Analysis Scenarios and Tasks Used in the Experiment-Based Evaluation Study 498
Appendix 3: Listing of Analysis Scenarios and Tasks Used in the Field Evaluation Study 499
REFERENCES 500
SUGGESTED READING 501
ONLINE RESOURCES 502
Chapter 20 SYNDROMIC SURVEILLANCE FOR THE G8 HOKKAIDO TOYAKO SUMMIT MEETING 503
CHAPTER OVERVIEW 503
1. INTRODUCTION 504
2. BACKGROUND 505
3. METHODS 506
3.1. Syndromic Surveillance for Prescriptions 506
3.2. Syndromic Surveillance for Ambulance Transfer 509
3.3. Syndromic Surveillance for OTC Drug Sales 511
3.4. Joint Conference for Evaluation of Aberration Signals from the Syndromic Surveillance System 512
4. RESULTS 512
5. CONCLUSIONS AND DISCUSSION 514
6. OTHER SYNDROMIC SURVEILLANCE SYSTEMS AT THE EXPERIMENTAL LEVEL IN JAPAN 515
6.1. Syndromic Surveillance from EMRs 515
6.2. Syndromic Surveillance from Orders for Medical Examinations 519
6.3. Syndromic Surveillance from Absenteeism at School 519
6.4. Syndromic Surveillance for Nosocomial Outbreak 520
ACKNOWDLEDGEMENT 520
QUESTIONS FOR DISCUSSION 520
REFERENCES 521
INDEX 522