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Public Health Syndromic Surveillance Systems

  • Hsinchun Chen
  • Daniel Zeng
  • Ping Yan
Chapter
Part of the Integrated Series in Information Systems book series (ISIS, volume 21)

Abstract

In this chapter, we summarize the key local, state, national, and international syndromic surveillance systems and related ongoing research programs of interest covered in our study. This summary provides the needed background information and application contexts. It also offers a current snapshot of syndromic surveillance practice in general. Note that as our primary focus is on public health surveillance, closely-related issues such as response planning and resource allocations strategies after an event is confirmed (e.g., Carley et al., 2003) are beyond the scope of this study.

For each system surveyed, we list its main contributors and stakeholders. We also include an overall system/project description, relevant data sources, syndromes monitored, data analysis and outbreak detection methods implemented, frequency of data collection and analysis, whether a GIS component is used, and its deployment strategy and status.

Keywords

Exponential Weight Move Average Syndromic Surveillance Public Health Surveillance Gastrointestinal Illness Outbreak Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In this chapter, we summarize the key local, state, national, and international syndromic surveillance systems and related ongoing research programs of interest covered in our study. This summary provides the needed background information and application contexts. It also offers a current snapshot of syndromic surveillance practice in general. Note that as our primary focus is on public health surveillance, closely-related issues such as response planning and resource allocations strategies after an event is confirmed (e.g., Carley et al.,2003) are beyond the scope of this study.

For each system surveyed, we list its main contributors and stakeholders. We also include an overall system/project description, relevant data sources, syndromes monitored, data analysis and outbreak detection methods implemented, frequency of data collection and analysis, whether a GIS component is used, and its deployment strategy and status.

Although our review is intended to be detailed and comprehensive, our effort has been hampered by the unavailability of the technical details of many syndromic surveillance systems from either the published literature or the publicly available sources such as project Web sites. Furthermore, despite our best effort, our literature review is unlikely to be exhaustive. As such, we may have missed some interesting and emerging local and/or international syndromic surveillance system implementations. Nonetheless, our review should provide the readers with a fairly detailed and up-to-date snapshot of the state-of-the-art research and successful implementations of syndromic surveillance systems for public health and biodefense.

2.1 Summary of nationwide syndromic surveillance systems

Thirteen nationwide syndromic surveillance systems plus two open source global public health status monitoring systems have been identified in our study. Table 2-1 presents a summary of these systems. Below we provide additional information for each of these systems.
Table 2-1.

Thirteen nationwide syndromic surveillance systems and two global online disease intelligence projects.

System

Stakeholders

Monitored datasets

Syndrome categories

Data analysis methods

Frequency

GIS

 

BioSense

CDC

Multiple

11

CUSUM,1 EWMA,2 and SMART6

Daily

Y

 

RODS

U. of Pittsburgh and Carnegie Mellon U.

Multiple

8

Autoregressive modeling, CUSUM, scan statistics, WSARE,3 PANDA4 and others

Every 8 h

Y

 

ESSENCE

DoD-GEIS5 and Johns Hopkins U

Multiple

8

CUSUM, EWMA, WSARE, SMART, and scan statistics

Daily

Y

 

RSVP

Sandia National Lab and State of NM Dept. of Health and clinicians, Los Alamos National Lab (LANL), U. of NM

Multiple

6

CUSUM, EWMA and wavelet algorithms

Daily

Y

 

EARS

CDC

Multiple

About 42

Shewhart chart, moving average, and variations of CUSUM (C1-MILD, C2-MEDIUM, and C3-ULTRA)

Daily

N

 

National Bioterrorism Syndromic Surveillance Demonstration Program

Harvard Medical School's Channing Lab

Multiple

12

Model-adjusted SaTScan™ approach and SMART

Daily

N

 

BioALIRT

DARPA, Johns Hopkins U., Walter Reed Army Institute of Research, U. of Pittsburgh and Carnegie Mellon U., etc.

Multiple

ILI, GI

Algorithms developed by RODS, CDC, ESSENCE, and IBM

Daily

N

 

BioDefend

U. of South Florida's Center for Biological Defense and Datasphere, LLC

Multiple

12

Time series pattern deviation detection, based on a 30-day rolling mean as threshold

Daily

N

 

BioStorm

Stanford U.

Multiple

Customized

A library of statistical methods and knowledge-based methods

N/A

N

 

BioPortal

U. of Arizona, U. of California, Davis, Kansas State U., National Taiwan U., Arizona/California Dept. of Public Health Services, New York State Dept. of Health

Multiple

40+

RSVC, Prospective SVC, and correlation analysis

N/A

Y

 

B-SAFER

DoD's National Biodefense Initiative and Dept. of Energy, in collaboration with the Los Alamos National Lab, U. of New Mexico Health Sciences Center, and the New Mexico Dept. of Health

Multiple

7

First-order model

Daily

N

 

INFERNO

Sponsored by National Institutes of Health

Multiple

GI

Retrospective daily time series

N/A

N

 

DiSTRIBuTE

International Society for Disease Surveillance (ISDS)

Multiple

ILI

Group aggregates by age and jurisdictional area

Daily

N

 

Argus

Argus Research Operations Center (AROC), ISIS Center, Georgetown University Medical Center

Multiple

N/A

Web Scanning, online media processing, epidemics caused social disruption detection, evaluation and tracking, Wilson - Collmann Scale heuristic staging model

N/A

N

 

HealthMap

Harvard Medical School and the Children's Hospital Boston Informatics Program

Multiple

N/A

Google Maps, xajax PHP AJAX library, Open Source Web Design (Blue Sky template by Jonas John), Fisher-Robinson Bayesian filtering

N/A

N (Google Maps)

 

1CUSUM: Cumulative Sums

2EWMA: Exponentially Weighted Moving Average

3WSARE: What is Strange About Recent Event

4PANDA: Population-Wide Anomaly Detection and Assessment

5DoD-GEIS: DoD-Global Emerging Infections Surveillance and Response System

6SMART: Small Area Regression and Testing

CDC's BioSense system is a national initiative to support early outbreak detection by providing technologies for timely data acquisition, near real-time reporting, automated outbreak identification, and related analytics (Bradley et al., 2005; Ma et al., 2005; Sokolow et al., 2005). BioSense collects ambulatory care data, emergency room diagnostic and procedural information from military and veteran medical facilities, and clinical laboratory test orders and results from LabCorp. BioSense also monitors over-the-counter (OTC) drug sales, and laboratory test results for environmental samples collected through the BioWatch effort. In its most recent implementation, BioSense aims to monitor 11 syndrome categories including fever, respiratory, gastrointestinal illness (GI), hemorrhagic illness, localized cutaneous lesion, lymphadenitis, neurologic, rash, severe illness and death, specific infection, and botulism-like/botulism.

The real-time outbreak detection system (RODS) is grounded in public health practice and focuses on collecting surveillance data for algorithm validation and investigating different types of novel data for outbreak detection (Espino et al., 2004; Tsui et al., 2003). It has been connected to 500+ hospitals' emergency departments nationwide for syndromic surveillance purposes. RODS collects chief complaints from emergency rooms, admission records from hospitals, and OTC drug sales data in real-time. Syndrome categories including respiratory, GI, botulinic, constitutional, neurologic, rash, hemorrhagic, and others are monitored with a collection of data analysis methods.

In 1999, the Walter Reed Army Institute of Research (WRAIR) created the Electronic Surveillance System for the early notification of community-based epidemics (ESSENCE) (Lombardo et al., 2004). ESSENCE has been used to monitor the health status of military healthcare beneficiaries worldwide, relying on outpatient ICD-9 diagnostic codes for outbreak detection (Burkom et al., 2004; Lombardo et al., 2003, 2004). Military and civilian ambulatory visits, civilian emergency department chief-complaint records, school-absenteeism data, OTC and prescription medication sales, veterinary health records, and requests for influenza testing are used by ESSENCE to evaluate health status with a focus on cases of death, GI, neurological, rash, respiratory, sepsis, unspecified infection, and others. ESSENCE has been deployed in the National Capital Area, and 300 military clinics worldwide by 2003 (Lombardo et al., 2003).

The Rapid Syndrome Validation Project (RSVP) is an Internet-based population health surveillance tool designed to facilitate rapid communications between epidemiologists and healthcare providers (Zelicoff,2002; Zelicoff et al., 2001). Through RSVP, patient encounters labeled with syndrome categories (including flu-like illness, fever with skin findings, fever with altered mental status, acute bloody diarrhea, acute hepatitis, and acute respiratory distress) and clinicians' judgment regarding the severity of illness are reported to facilitate timely geographic and temporal analysis (Zelicoff,2002).

The Early Aberration Reporting System (EARS) is used to monitor bioterrorism activities during large-scale events. Its evolution to a standard surveillance tool began in the New York City and the national capital region following the terrorist attacks of September 11, 2001 (CDC,2006a; Hutwagner et al., 2003). Emergent department visits, 911 calls, physician office data, school and work absenteeism, and OTC drug sales are monitored for 42 syndrome categories (Hutwagner et al., 2003). EARS has been implemented in emergency departments in the state of New Mexico. It was also used for syndromic surveillance purposes at the 2000 Democratic National Convention, the 2001 Super Bowl, and the 2001 World Series.

The National Bioterrorism Syndromic Surveillance Demonstration Program covers a population of more than 20 million people. This program monitors and analyzes disease cases for neurologic, upper/lower GI, upper/lower respiratory, dermatologic, sepsis/fever, bioterrorism category A agents (anthrax, botulism, plague, smallpox, tularemia, and hemorrhagic fever), and influenza-like illness (ILI). These data utilized are derived from electronic patient-encounter records from participating healthcare organizations including ambulatory-care encounters and urgent-care encounters (Lazarus et al., 2001, 2002; Platt et al., 2003; Yih et al., 2004). This project provides a testbed for analyzing various outbreak detection algorithms and implements a model-adjusted SaTScan approach and the SMART algorithm (Kleinman et al., 2004).

The Bio-event Advanced Leading Indicator Recognition Technology (BioALIRT) program examines the use of spatial and other covariate information from disparate sources to improve the timeliness of outbreak detection in reaction to possible bioterrorism attacks (Buckeridge et al., 2005a; Siegrist et al.,2004). In a number of regions including Norfolk, Virginia; Pensacola, Florida; Charleston, South Carolina; Seattle, Washington; and Louisville, Kentucky, the BioALIRT system monitors military and civilian outpatient-visit records with ICD-9 codes, and military outpatient prescription records for unusual ILI and GI occurrences.

BioDefend is another program that aims to develop an effective and practical approach for rapid detection of outbreaks (2006b; Uhde et al., 2005). Patient encounter information is collected automatically or manually from clinics, emergency departments, and first aid stations at the first point of patient contact. Syndrome categories monitored include respiratory tract infection with fever, botulism-like, ILI, death with fever, GI, encephalitis/meningitis-like illness, febrile, rash with fever, fever of unknown origin, sepsis, contact dermatitis, and nontraumatic shock.

Biological Spatio-Temporal Outbreak Reasoning Module (BioStorm) aims to integrate disparate data sources and deploys various analytic problem solvers to support public health surveillance. The framework is ontology-based and consists of a data broker, a data mapper, a control structure and a library of statistical and spatial problem solvers (Buckeridge et al., 2002; Crubézy et al., 2005). It monitors and analyzes data such as 911 emergency calls collected from San Francisco, emergency department dispatch data from the Palo Alto Veterans Administration Medical Center, and emergency department respiratory records from hospitals in Norfolk, Virginia. On the basis of a customized knowledge base, BioStorm has implemented a library of statistical methods analyzing data as single or multiple time series and knowledge-based methods that relate detected abnormalities to knowledge about reportable diseases.

BioPortal is another biosurveillance system that provides a flexible and scalable infectious disease information sharing (across species and jurisdictions), alerting, analysis, and visualization platform (Chen and Xu, 2006; Zeng et al., 2005b). The system supports interactive, dynamic spatial-temporal analysis of epidemiological, textual and sequence data (Chen and Xu, 2006; Thurmond,2006; Zeng et al., 2005a). BioPortal makes available a sophisticated spatial-temporal visualization environment to help visualize public health case reports and analysis results. Similar to EARS, BioPortal uses customized syndrome categories, which were developed by the State of Arizona Department of Health Services and hospitals in Taiwan (Lu et al., 2008). A number of retrospective and prospective spatial-temporal clustering (hotspot analysis) approaches are developed and implemented in BioPortal for outbreak detection purposes. They are Risk-adjusted Support Vector Clustering (RSVC) (Zeng et al., 2004a), Prospective Support Vector Clustering (Chang et al., 2005, 2008), and space-time correlation analysis (Ma et al., 2006).

Bio-Surveillance Analysis, Feedback, Evaluation and Response (B-SAFER) is a Web-based infectious disease monitoring system that is part of the open source OpenEMed project (http://openemed.org/) for use in urgent care settings (Umland et al., 2003). It collects chief complaints, discharge diagnoses and disposition data for detection analysis concerning a group of syndromes including respiratory, GI, undifferentiated infection, lymphatic, skin, neurological, and other. The collected data are analyzed daily by a first-order model that uses regression to fit trends, seasonal effects, and day-of-week effects (Brillman et al., 2005).

INtegrated Forecasts and EaRly eNteric Outbreak (INFERNO) incorporates infectious disease epidemiology into adaptive forecasting and uses the concept of an outbreak signature as a composite of disease epidemic curves (Naumova et al., 2005). The system has been tested with a dataset of emergency department records associated with a substantial waterborne outbreak of cryptosporidiosis that occurred in Milwaukee, Wisconsin, in 1993.

The DiSTRIBuTE project is a proof-of-concept, distributed, influenza surveillance system. DiSTRIBuTE uses aggregate, influenza-like illness (ILI), emergency department data from existing syndromic surveillance systems developed by state and local public health departments. Data are aggregated by age group and three-digit zip code. The DiSTRIBuTE project complements traditional influenza morbidity surveillance by providing a consistent, timely, year-round, high volume, regional, age-group-specific indication of febrile illness in the community.

Two other global scale real-time disease event detection and tracking systems are taking a different approach from the systems discussed above. The Argus and HealthMap projects monitor online media from global sources, instead of disease cases reported by hospitals, clinics, and other health facilities. The two systems are built on top of open sources, exemplifying an idea of open development for public health informatics applications. Argus, developed at Georgetown University, relies on Internet technologies as “harvesting engines” to capture information relevant to the definitional criteria for biological-outbreak severity metrics. The system automatically collects official disease reports from WHO or unofficial international health status reports from ProMED as indicators of possible biological events, and relies on its team of multilingual analysts to evaluate the associations between the online media and existence of adverse health events.
Figure 2-1.

Surface-plot of scaled ED visits by age, with predominant RSV and influenza A and B, periods indicated.

HealthMap brings together disparate data sources to achieve a unified and comprehensive view of the current global state of infectious diseases and their effect on human and animal health. This freely available Web site integrates outbreak data of varying reliability, ranging from news sources (such as Google News) to curated personal accounts (such as ProMED) to validated official alerts (such as World Health Organization). Through an automated text processing system, these data are aggregated by disease and displayed by location for user friendly access to the original alert. HealthMap provides a jumping-off point for real-time information on emerging infectious diseases and has particular interest for public health officials and international travelers.

2.2 Summary of syndromic surveillance systems at the local, county, and state levels

Twenty syndromic surveillance systems implemented at the local, county, and state levels have been identified in our study. Table 2-2 presents a summary of these systems. Note that technical information about these systems is often much more difficult to locate (in many cases unavailable publicly) when compared with nationwide systems.
Table 2-2

20 syndromic surveillance system implementations at local or state levels

System

Stakeholders

System description

 

Syndromic Surveillance Project in New York City

New York City Dept. of Health and Mental Hygiene (NYCDOHMH)

Central data repository, ETL data processing, and analytical tools based on SaTScan and ArcView desktop GIS

 

SSIC

U. Washington

Focus on early detection of possible bioterrorism attacks

 

Automated Epidemiological Geotemporal Integrated Surveillance (AEGIS)

Children’s Hospital Informatics Pro-gram at the Harvard-MIT Division of Health Sciences and Technology

A modular design to facilitate multiple health surveillance systems integration

 

Syndromal Surveillance Tally Sheet

EDs of Santa Clara County, California

A manual system relying on triage nurses’ counts of the num-bers of patients presenting the syndromes of interest

 

Syndromic Surveillance Using Automated Medical Records

Greater Boston

Outbreak detection with health plan data in the Greater Bos-ton area

 

New Hampshire (NH) Syn-dromic Surveillance System

Division of Public Health Services, NH Dept. of Health and Human Ser-vices (NH DHHS)

A system collecting syndromic data from multiple sites in-cluding EDs, schools, workplaces, as well as other electronic health surveillance systems such as BioSense

 

Connecticut Hospital Ad-missions Syndromic Sur-veillance

Connecticut Dept. of Public Health (CDPH)

The system monitors hospital admission data

 

Catalis Health System for syndromic surveillance in a rural outpatient clinic in Texas

Texas Dept. of State Health Services (DSHS)

In 2003, the Texas Regional Health Dept. piloted the Catalis software system monitoring three types of syndromes: Rash Fever illness, meningoencephalitis and ILI. The system proved to be 100 percent sensitive in reporting syndromes to the state

 

NC DETECT http://www.ncdetect.org/

North Carolina Division of Public Health (NCDPH)

NC DETECT provides statewide early event detection by monitoring data from EDs, the Carolinas Poison Center, and the Pre-hospital Medical Information System

 

SENDSS https://sendss.state.ga.us/sendss/login.screen

Georgia Division of Public Health

SendSS is a reporting and tracking tool of notifiable diseases deployed in Georgia. It is a web-based application allows case report, analysis and messaging among other functionalities

 

Syndromic surveillance sys-tem in Miami-Dade County

Office of Epidemiology & Disease Control, Miami-Dade County Health Dept.

The system monitors chief complaints data on a daily basis. The analysis system is coupled with ESNENCE. Public health status alerts are reviewed by an analyst each day

 

Early Event Detection in San Diego

San Diego County

The system monitors ER visits, paramedic transports, 911 calls, school absenteeism, and OTC sales for early event de-tection

 

Communicable Disease Re-porting and Surveillance System https://cdrs.doh.state.nj.us/cdrss/login/login.jsp

New Jersey ( Dept. of Health and Sen-ior Services (NJDHSS)

Since 2001, NJDHSS implemented the surveillance system to characterize ED visits/admissions trends, detect aberrations and generate daily reports (via e-mail or facsimile) of the number of ED visits and admissions from all 84 acute care hospitals with EDs statewide

 

EED in South Carolina

South Carolina Dept. of Health and Environmental Control

Major capability of the system includes surveillance with BioSense, OTC sales, and Palmetto Poison Center

 

Indiana's pilot program for syndromic surveillance

Indiana State Dept. of Health

Include a variety of sources: coroners' reports, calls to the Indiana Poison Control Center, school absenteeism counts, lab test orders, veterinary lab results, and reports from day-care centers

 

National Capitol Region’s ED syndromic surveillance system

Maryland, the District of Columbia, and Virginia

The system uses chief complaints for syndromic assignment

 

Michigan Disease Surveil-lance System Syndromic Surveillance Project

Michigan Dept. of Community Health (MDCH)

The Michigan system uses RODS developed at the University of Pittsburg for outbreak detection. By 2007, 65 facilities from across Michigan have participated the project

 

HESS and HASS

Missouri Dept. of Health and Senior Services

The state-wide syndromic surveillance uses ESSENCE and BioSense to analyze and visualize the syndromic data that are electronically reported by 85 hospitals across Missouri

 

North Dakota Department of Health Syndromic Surveil-lance Program

North Dakota Dept. of Health

Chief complaint data from nurse advice call center and EDs are analyzed for syndromic surveillance utilizing a commer-cial software RedBat®

 

Syndrome Reporting Infor-mation System (SYRIS)

Lubbock Health Dept.

The status of the health of the community can be instantane-ously communicated among doctors, public health officials, or government officials via time series or GIS analysis and visualization

 

The syndromic surveillance system implemented in New York City uses ETL (extract, transform, and load) middleware technology from iWay Software over secure, Web-based reporting channels to receive and process a high volume of daily reports at a central data repository. A custom analytical application based on spatial data analysis software SaTScan and ArcView desktop GIS and mapping software from ESRI is used to perform statistical analysis and related visualization functions (Heffernan et al., 2004a, 2004b).

Syndromic Surveillance Information Collection (SSIC) is a complex, heterogeneous database system intended to facilitate the early detection of possible bioterrorism attacks (with such agents as anthrax, brucellosis, plague, Q-fever, tularemia, smallpox, viralencephalitides, hemorrhagic fever, botulism toxins, staphylococcal enterotoxin-B, among others) as well as naturally occurring disease outbreaks including large foodborne disease outbreaks, emerging infections, and pandemic influenza (Karras,2005).

The Automated Epidemiological Geotemporal Integrated Surveillance (AEGIS) system is a surveillance effort initiated by the Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology since 2000 at the state of Massachusetts. The system adopted a modular design to address the challenges of scalability, robustness, and data security issues due to an emerging demand of integrating real-time public health surveillance systems into regional and national surveillance initiatives (Reis et al., 2007). The system consists of modeling modules, detection modules, and client modules.

The Syndromal Surveillance Tally Sheet program is based on the triage nurses' counts of the numbers of patients presenting the syndromes of interest collected from emergency departments of Santa Clara County, California (Bravata et al., 2002). (This manual system was proved to be staff and resource intensive and was replaced by an ESSENCE implementation in 2005.)
Figure 2-2.

Modeling, detection, and client modules implemented in the current AEGIS system (Reis et al., 2007).

The system used in the greater Boston area is for rapid identification of illness syndromes using automated records from 1996 through 1999 of approximately 250,000 health plan members in the area (Lazarus et al., 2001).

New Hampshire Syndromic Surveillance System collects information from multiple sites in New Hampshire including emergency departments, 23 city schools, 5 workplaces, participating pharmacies, as well as military and veteran medical facilities, and LabCorp through the BioSense program. Data are either key punched or electronically transferred into the Syndromic Tracking Encounter Management System (STEMS) for analysis and geo-coding (Miller et al., 2003).

In the state of Connecticut, a Hospital Admissions Syndromic Surveillance system is implemented by the Connecticut Department of Public Health. This system monitors hospital admissions from the previous day rather than outpatient visits as most other syndromic systems do (Dembek et al., 2004, 2005).

Catalis Health System for syndromic surveillance in Texas interfaces with available clinic practice management systems to produce a standardized dataset via a point-of-care electronic medical record (EMR). This system supports data flows directly from clinic providers to the health department for syndromic surveillance. Rural counties with limited epidemiological resources have benefited from this approach (Nekomoto et al.2003).

North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC Detect), formerly known as the North Carolina Bioterrorism and Emerging Infection Prevention System, analyzes a variety of data sources including the North Carolina Emergency Department Database (NCEDD) and the Carolinas Poison Center with the EARS software tool (2006d).

The Georgia Division of Public Health takes a centralized approach by comparing local data to those from other districts and state totals. The clinical and nonclinical data are collected, and the analysis results are displayed through a Web-based program called the State Electronic Notifiable Disease Surveillance System (SendSS) (2006k). The major functionalities of the Web-based application are shown in Figure 2-3..
Figure 2-3.

Homepage and menu navigation of SendSS Web application.

The syndromic surveillance system in Miami-Dade County, Florida, is a Web-based system where syndromic data are transferred from emergency departments to an ESSENCE server for data analysis and anomaly detection (2006m). On a daily basis, 14 county hospitals automatically transmit deidentified chief complaint data to the surveillance system. Each chief complaint is then placed into one of 10 syndrome categories including respiratory, gastrointestinal, hemorrhagic, influenza-like, shock/coma, neurologic, fever, febrile, rash, botulism-like, and other. ESSENCE performs automatic data analysis, establishing a baseline with a 28-day average. Daily case data are then analyzed against this baseline to identify statistically significant increases. An MDCHD analyst evaluates all alerts and develops a summary report on each day (Zhang et al., 2007).

The Early Event Detection system in San Diego constantly monitors emergency room visits, paramedic transports, 911 calls, school absenteeism data, and OTC sales for early event detection. It supports interoperability with local SAS/Minitab installations, ESSENCE, and BioSense (Johnson,2006).

The New Jersey syndromic system includes four components: emergency department-based surveillance using visit and admission data from participating hospitals statewide and a modified CUSUM method to detect aberrations, OTC pharmacy sales surveillance from RODS, an ILI surveillance module, and a Web-based Communicable Disease Reporting System (CDRS) for real time data transmission and reporting (Hamby,2006).

The early event detection (EED) system in South Carolina provides syndromic surveillance capabilities at the state/local level, using data from BioSense, OTC sales, and Palmetto Poison Center (Drociuk et al., 2004). The EED system is among a number of disease surveillance systems in South Carolina, including ESSENCE, BioSense, and sentinel providers network with ILI reporting. As of February 2006, there were 536 distinct sources providing OTC drug sales data.

Indiana's pilot program for syndromic surveillance is currently taking in data from 17 hospitals, most of them in Indianapolis. Indiana's system is expected to include a variety of sources: coroners' reports, calls to the Indiana Poison Control Center, school absenteeism counts, lab test orders, veterinary lab results, and reports from day care centers (Lober et al., 2002).

National Capitol Region's Emergency Department syndromic surveillance system is a cooperative effort between Maryland, the District of Columbia, and Virginia that uses chief complaints for syndromic assignment. Using a syndrome assignment matrix (Begier et al., 2003), the emergency department visits are coded into one of eight mutually exclusive syndromes: “death,” “sepsis,” “rash,” “respiratory” illness, “gastrointestinal” illness, “unspecified infection,” “neurologic” illness, and “other.”

The Michigan Syndromic Surveillance Project tracks emergent care registrations per day (primarily ED, some urgent care) and Poison Control Call Center data using RODS. MDCH and participants exchange data in real-time using virtual private networks (VPNs) to secure the data and HL-7 as the messaging format. Detection algorithms run every hour and send email alerts to public health officials when deviations are found. State and regional epidemiologists are provided with Web access to the charts and maps of the data analytical results (2006g).

The hospital electronic syndromic surveillance (HESS) and hospital admission syndromic surveillance (HASS) systems, implemented in the State of Missouri, are designed to provide an early warning system of public health emergencies including bioterrorism events, and offer outbreak detection and epidemiologic monitoring functions. HESS collects data electronically from existing electronic systems and requires all hospitals to participate, whereas HASS receives data on a paper form from selected sentinel hospitals (2006f). They use ESSENCE and BioSense to analyze, visualize, and report electronically ED data collected through HESS Reporting Rule. By 2007, electronic feeds were being collected automatically from 85 hospitals across the state. Figure 2-4 shows statewide syndromic surveillance coverage in Missouri.
Figure 2-4.

Missouri syndromic surveillance coverage; yellow dots are HASS, and red are HESS hospitals (Resch et al., 2007).

The North Dakota Department of Health Syndromic Surveillance Program is based on chief complaint data received electronically from seven large hospital emergency departments located in North Dakota's four largest cities. In addition, data from a call center in North Dakota's largest city are received and reviewed daily. They use the natural language translation tool SympTran® to translate free text chief complaints into symptoms and then group those into six syndrome groups (Goplin et al., 2007). Data analysis functions are provided by the commercial software called RedBat. The RedBat system will be briefly introduced in the next section. Over 50% of the state's population is currently involved in this program (2006h). They have also developed the North Dakota Electronic Animal Health Surveillance System for animal disease surveillance. The data analysis capability is provided by the CDC EARS.

Syndrome Reporting Information System (SYRIS) is a Web-based, real-time, clinician-driven syndromic surveillance system implemented in Lubbock, Texas. It provides two-way communication between clinicians and public health officials for high specificity, high signal-to-noise ratio outbreak detection in both human and wildlife species diseases.

2.3 Summary of industrial solutions for syndromic surveillance

We now discuss seven representative industrial solutions for syndromic surveillance, as summarized in Table 2-3.
Figure 2-5.

Screenshot of SYRIS system.

Table 2-3.

Seven industrial solutions for syndromic surveillance.

System

Company

 

LEADERS

Idaho Technology, Inc., Salt Lake City, Utah

 

FirstWatch Real-Time Early Warning System

Stout Solutions, LLC., Encinitas, California

 

STC syndromic surveillance product

Scientific Technologies' Corporation (STC), Tucson, Arizona

 

RedBat (Multi-use syndromic surveillance system for hospitals and public health agencies)

ICPA, Inc., Austin, Texas

 

EDIS (Emergisoft's Emergency Department Information System)

Emergisoft Corporate, Arlington, Texas

 

Spatiotemporal Epidemiological Modeler (STEM) tool

IBM Corporation, Almaden Research Center, California

 

Emergint Data Collection and Transformation System (DCTS)

Emergint, Inc., Louisville, Kentucky

 

The Lightweight Epidemiology Advanced Detection and Emergency Response System (LEADERS) is an Internet-based integrated medical surveillance system for collecting, storing, analyzing, and viewing critical medical incidents. LEADERS was deployed at the 1999 World Trade Organization Summit, the 2000 Republican and Democratic National Conventions, the Presidential Inaugural Activities, and the Super Bowl. Portions of LEADERS have been deployed by US military forces worldwide since 1998 (Ritter, 2002).

FirstWatch integrates data from 911 calling systems, emergency departments, lab tests, pharmacies, poison controls and paramedic practices, all of which are monitored in real-time. Real-time alerting and reporting are also supported (2006e).

The Web-based STC syndromic surveillance product is compatible with the CDC NEDSS Logical Data Module (LDM). Its current clients include public health departments in Connecticut, Louisiana, New York City, and Washington, DC. The analysis and alerting algorithms implemented in the system such as CUSUM, 3rd Sigma, and STC's Zhang Methodology are applied to a variety of data sources that include OTC sales, school nurse visits, and emergency rooms (2006l).

RedBat automatically imports existing data from hospitals and public health agencies. Besides outbreak detection, it is also capable of tracking injuries, reportable diseases, asthma, and disaster victims (2006i).

Emergisoft is a software solution for syndromic surveillance that has been employed in the 1996 Olympics in Atlanta and in the metropolitan areas of New York City and Los Angeles (Emergisoft,2006).

A Spatiotemporal Epidemiological Modeler (STEM) tool, developed at the IBM Almaden Research Center, can be used to develop spatial and temporal models of emerging infectious diseases. These models can involve multiple populations/species and interactions between diseases. GIS data for every county in US have been integrated into the STEM application (Ford et al.,2005).

Emergint provides a syndromic surveillance system for data collection and processing. It can interface with care providers, laboratories, research organizations, and federal and state health departments. Emergint also provides data aggregation analysis as well as visualization functions (2004a).

2.4 Summary of international syndromic surveillance projects

The National Health Service (NHS) in the UK operates a NHS Direct Syndromic Surveillance system that monitors the nurse-led telephone helpline data collected electronically by the Health Protection Agency from all 23 NHS Direct sites in England and Wales (Doroshenko et al., 2005). Syndromes monitored include cold/influenza, cough, diarrhea, difficulty breathing, double vision, eye problems, lumps, fever, rash, and vomiting. Data streams are analyzed every 2 hours by statistical methods such as confidence intervals and control chart methods (Cooper et al., 2004).
Table 2-4.

Ten international syndromic surveillance systems.

System

Agency

 

National Health Service (NHS) Direct Syndromic Surveillance

Operated by NHS of UK

 

Early Warning Outbreak Recognition System (EWORS)

Association of South East Asian Nations

 

Alternative Surveillance Alert Program (ASAP)

Health Canada

 

Military syndromic surveillance for dengue fever outbreak

French Guiana in South America

 

Emergency Department Information System in Korea

Korea

 

Experimental Three Syndromic Surveillances in Japan

National Institute of Infectious Diseases, Japan

 

Australian Sentinel Practice Research Network (ASPREN)

The Royal Australian College of General Practitioners; the Dept. of General Practice, U. of Adelaide; Australian Dept. of Health and Ageing

 

New South Wales ED surveillance system

New South Wales, Australia

 

ILI surveillance in France

France

 

UMR S 707 (“Epidemiology, Information Systems, Modeling” project)

France

 

In Southeast Asia, the Association of Southeast Asian Nations (ASEAN) has developed the Early Warning Outbreak Recognition System (EWORS) for disease surveillance. EWORS collects data from a network of hospitals and provides technical approaches to distinguish epidemic from endemic diseases (EWORS,2006). Free-text or ICD-9 coded symptom reports can be collected through EWORS to monitor a number of infectious diseases, including malaria and hemorrhagic fever due to Hantaan virus infection. Statistical analysis methods are used for daily data analysis and visualization. The system is currently implemented by public health departments of Indonesia, Cambodia, Vietnam, and Laos.

In some high-income countries, syndromic surveillance has been a very effective approach to supporting real-time public health monitoring. However, in developing countries, where public health is more in hazard, while the information communication infrastructure is more fragile, syndromic surveillance systems are more critically needed but difficult to implement. Chretien identified such difficulties, and discussed some of the successful syndromic surveillance implementation cases in a recent work. Availability of technologies for health data capture and transmission in these underdeveloped areas and countries are investigated. Operational experiences of systems such as EWORS are presented (Chretien et al., 2008).

The Alternative Surveillance Alert Program (ASAP), initiated by Health Canada, currently monitors gastrointestinal disease trends by analyzing OTC antidiarrheal and anti-nausea sales data, and calls to Telehealth lines (Edge et al., 2003). The system is planned to be deployed at the community, provincial, and national levels.

A syndromic surveillance system called 2SE FAG system (Surveillance Spatiale des Epidémies au sein des Forces Armées en Guyane) was established to serve the military forces in French Guiana, a French overseas department in South America in 2004. The statistical analysis of military syndromic surveillance data with 2SE FAG is performed with Current Past Experienced Graph (CPEG) and the Exponential Weighted Moving Average (EWMA) method (Meynard et al., 2008). They showed that the system detected the dengue fever outbreak, which occurred in 2006 several weeks before traditional clinical surveillance, allowing quick and effective outbreak surveillance within the armed forces (Meynard et al., 2008).

In Korea, 120 emergency departments from 16 provinces and cities are now connected to the Korea Emergency Department Information System for daily analysis of acute respiratory syndrome. The system was initially developed for the 2002 Korea-Japan FIFA World Cup Games (Cho et al., 2003).

Japan’s National Institute of Infectious Diseases (NIID) has developed a syndromic surveillance system based on EARS syndrome categories and EARS software to analyze OTC sales data, outpatient visits, and ambulance transfer data in Tokyo (Ohkusa et al., 2005a, b). Approximately 5,000 sites nationwide in Japan are now connected to this system. The system was used for the 2000 G8 Summit and 2002 FIFA World Cup Games.

The Australian Sentinel Practice Research Network (ASPREN) is a national network of general practitioners who collect and report data on selected conditions such as ILI for weekly statistical analysis (Clothier et al.,2006). It is now being used by about 50 general practitioners nationwide in Australia.

The New South Wales ED surveillance system routinely collects computerized ED patient information from 30 EDs in New South Wales (Hope et al., 2008). The ED provisional diagnoses are classified into 37 syndromes, including gastrointestinal, influenza, pneumonia, other/unspecified respiratory infections, all injury and mental health presentations. Statistical control charts are used to automatically detect increases in syndrome activity, using Poisson z-scores of observed vs. expected day-of-week. Surveillance reports are updated four times per day (Muscatello et al., 2005).

ILI surveillance is practiced in 11,000 pharmacies throughout France (about 50% of all pharmacies in France) in 21 regions. This ILI surveillance system is a Web-based system that collects medication sales and weekly office visit data to provide forecasts of influenza outbreaks using a Poisson regression model (Vergu et al., 2006).

The UMR S 707, “Epidemiology, Information Systems, Modeling,” headed by Guy Thomas, was created in 2005. The research group in France is the preponderent use of the mathematics modelisation, the statistics, the computer sciences and its use of information system technologies to lead its work in the domain of epidemiology. A Web-based application was developed for online epidemiological time series analysis. The application allows estimating the periodic baseline level and associated upper forecast limit. The latter defines a threshold for epidemic detection. The burden of an epidemic is defined as the cumulated signal in excess of the baseline estimate. The user is guided through the necessary choices for analysis (Pelat et al., 2007).

2.5 Syndromic surveillance for special events

During natural or human-made disasters, real-time and comprehensive knowledge of public health conditions is critical to inform response and recovery activities. Priority health conditions include infectious disease cases, injuries, and mental health disorders.

In recent years, the world has been through a number of global scale deathly disasters. Some examples that have affected millions of lives include Hurricane Katrina in 2005, causing the most severe loss of life and property damage occurring in New Orleans, Louisiana; the outbreak of the SARS pandemic in 2002. In addition to large scale disasters, special events such as the Olympic Games, FIFA World Cup, or G8 Summit often involve participation of large populations. The temporary and sudden surge of population density in the event location brings potential health hazards to the participants, such as intensified infectious disease transmission and surging healthcare utilization. For instance, the 2008 Olympics in Beijing brought a large influx of people into the metropolitan area for 2 weeks. Population surge caused by the influx of a large number of tourists would significantly alter healthcare utilization patterns. It is critical to quickly identify any localized infectious disease outbreaks and prevent them from taking place.

Therefore, in this section, we discuss the category of syndromic surveillance practice that is concerned with syndromic surveillance for special and large-scale events. Teams of public health officials often need to work together to monitor public health status for such events (e.g., the 2002 World Series in Phoenix (Das et al., 2003), the wildfire outbreak in San Diego, 2003 (Johnson et al., 2005)). During Korea-Japan FIFA World Cup 2002 in Japan (Suzuki et al., 2003) and Korea (Cho et al., 2003), syndromic surveillance systems also played a role in public health status monitoring. Another two examples are syndromic surveillance systems implemented for the 2002 Kentucky Derby (Goss et al., 2003) and the G8 Summit in Gleneagles, Auchterarder, Scotland in 2005 (2005a). Typically, during the events data from regional emergency departments will be collected. Information concerning a predefined list of symptoms and probable diagnoses will also be collected manually using special-purpose forms or via a Web-based interface.Table 2-5 summarizes six representative efforts in this category.

In addition to the surveillance efforts of varying scopes as summarized above, there has been an increasing need for the development of syndromic surveillance systems and efforts at the global scale. World Health Organization (WHO)'s Epidemic and Pandemic Alert and Response program represents one such effort toward global syndromic surveillance. Note that the challenge of implementing a global surveillance system is more of a policy and administration nature as opposed to technical.
Table 2-5.

Six syndromic surveillance efforts for special events.

Syndromic surveillance systems for special events

Stakeholders/location

 

Syndromic surveillance for Korea-Japan FIFA World Cup 2002 in Japan

National Institute of Infectious Diseases, Japan

 

Communitywide syndromic surveillance for 2002 Kentucky Derby

University of Louisville Hospital and Jefferson County Health Dept.

 

Syndromic surveillance for Korea-Japan FIFA World Cup 2002 in Korea

Korea

 

Drop-in bioterrorism surveillance system for World Series 2002 in Phoenix, Arizona

Phoenix, Arizona

 

Syndromic surveillance during the wildfires outbreak in San Diego, 2003

San Diego County

 

Syndromic surveillance for G8 Summit in Gleneagles, Auchterarder, Scotland, July 2005

Scotland, UK

 

References

  1. Begier, E.M., Sockwell, D., Branch, L.M., Davies-Cole, J.O., Jones, L.H., Edwards, L., Casani, J.A., and Blythe, D. 2003. "The National Capitol Region's Emergency Department Syndromic Surveillance System: Do Chief Complaint and Discharge Diagnosis Yield Different Results?" Emerg Infect Dis [serial on the Internet] (9:3).Google Scholar
  2. Bradley, C.A., Rolka, H., Walker, D., and Loonsk, J. 2005. "BioSense: Implementation of a National Early Event Detection and Situational Awareness System," MMWR (CDC) (54(Suppl)), pp. 11–20.Google Scholar
  3. Bravata, D.M., McDonald, K., Owens, D.K., Buckeridge, D., Haberland, C., and Rydzak, C. 2002. "Bioterrorism Preparedness and Response: Use of Information Technologies and Decision Support Systems," Evidence Report/Technology Assessment No (59).Google Scholar
  4. Brillman, J.C., Burr, T., Forslund, D., Joyce, E., Picard, R., and Umland, E. 2005. "Modeling Emergency Department Visit Patterns for Infectious Disease Complaints: Results and Application to Disease Surveillance," BMC Medical Informatics and Decision Making (5:4).PubMedCrossRefGoogle Scholar
  5. Buckeridge, D., Burkom, H., Campbell, M., Hogan, W., and Moore, A. 2005a. "Algorithms for Rapid Outbreak Detection: A Research Synthesis," Journal of Biomedical Informatics (38), pp. 99–113.PubMedCrossRefGoogle Scholar
  6. Buckeridge, D., Graham, J., O'Connor, J., Choy, M.K., Tu, S.W., and Musen, M. 2002. "Knowledge-Based Bioterrorism Surveillance," American Medical Informatics Association Symposium, San Antonio, TX.Google Scholar
  7. Burkom, H., Elbert, E., Feldman, A., and Lin, J. 2004. "Role of Data Aggregation in Biosurveillance Detection Strategies with Applications from Essence," MMWR (CDC) (53(Suppl)), pp. 67–73.Google Scholar
  8. Carley, K., Fridsma, D., Casman, E., Altman, N., Chang, J., Kaminsky, B., Nave, D., and Yahja, A. 2003. "Biowar: Scalable Multi-Agent Social and Epidemiological Simulation of Bioterrorism Events."Google Scholar
  9. CDC. 2006a. "Early Aberration Reporting System." Retrieved Feb 15, 2006, from http://www.bt.cdc.gov/surveillance/ears/
  10. Chang, W., Zeng, D., and Chen, H. 2005. "Prospective Spatio-Temporal Data Analysis for Security Informatics," In proceedings of the 8th IEEE International Conference on Intelligent Transportation Systems, Vienna, Austria.Google Scholar
  11. Chen, H., and Xu, J. 2006. "Intelligence and Security Informatics," Annual Review of Information Science and Technology (ARIST) (40), pp. 229–299.CrossRefGoogle Scholar
  12. Cho, J., Kim, J., Yoo, I., Ahn, M., Wang, S., Hur, T., Park, I., and Jeong, E. 2003. "Syndromic Surveillance Based on the Emergency Department in Korea," Journal of Urban Health: Bulletin of the New York Academy of Medicine (80:2), p. 124.Google Scholar
  13. Chretien, J.-P., Burkom, H.S., Sedyaningsih, E.R., Larasati, R.P., Lescano, A.G., Mundaca, C.C., Blazes, D.L., Munayco, C.V., Coberly, J.S., Ashar, R.J., and Lewis, S.H. 2008. "Syndromic Surveillance: Adapting Innovations to Developing Settings," PLoS Med (5:3), pp. 0367–0372.CrossRefGoogle Scholar
  14. Clothier, H.J., Fielding, J.E., and Kelly, H.A. 2006. "An Evaluation of the Australian Sentinel Practice Research Network (ASPREN) Surveillance for Influenza-Like Illness." Retrieved May 11, 2006, from http://www.health.gov.au/Internet/wcms/publishing.nsf/content/cda-cdi2903a.htm#data
  15. Cooper, G.F., Dash, D.H., Levander, J.D., Wong, W.K., Hogan, W.R., and Wagner, M.M. 2004. "Bayesian Biosurveillance of Disease Outbreaks," Twentieth Conference on Uncertainty in Artificial Intelligence, Banff, Alberta, Canada, pp. 94–103.Google Scholar
  16. Crubézy, M., O'Connor, Pincus, Z., and Musen, M.A. 2005. "Ontology-Centered Syndromic Surveillance for Bioterrorism," IEEE Intelligent Systems (20:5), pp. 26–35.CrossRefGoogle Scholar
  17. Das, D., Weiss, D., and Mostashari, F. 2003. "Enhanced Drop-in Syndromic Surveillance in New York City Following September 11, 2001," J Urban Health (80:1(suppl)), pp. 176–188.Google Scholar
  18. Dembek, Z., Carley, K., Siniscalchi, A., and Hadler, J. 2004. "Hospital Admissions Syndromic Surveillance - Connecticut, September 2001–November 2003," MMWR (CDC) (53(Suppl)), pp. 50–52.Google Scholar
  19. Doroshenko, A., Cooper, D., Smith, G., Gerard, E., Chinemana, F., Verlander, N., and Nicoll, A. 2005. "Evaluation of Syndromic Surveillance Based on National Health Service Direct Derived Data - England and Wales," MMWR (54(Suppl)), pp. 117–122.PubMedGoogle Scholar
  20. Drociuk, D., Gibson, J., and Hodge, J.J. 2004. "Health Information Privacy and Syndromic Surveillance Systems," MMWR (CDC) (53(Suppl)), pp. 221–225.Google Scholar
  21. Edge, V.L., Lim, G.H., Aramini, J.J., Sockett, P., and Pollari, F.L. 2003. "Development of an Alternative Surveillance Alert Program (ASAP): Syndromic Surveillance of Gastrointestinal Illness Using Pharmacy Over-The-Counter Sales," Journal of Urban Health: Bulletin of the New York Academy of Medicine (80:2), p. i138.Google Scholar
  22. Emergisoft. 2006. "Emergisoft's ED Sydromic Surveillance Solutions," Retrieved June 12, 2006, from http://www.emergisoft.com/productinfo/syndromic_surveillance/
  23. Espino, J.U., Wagner, M.M., Szczepaniak, C., Tsui, F.-C., Su, H., Olszewski, R., Liu, Z., Chapman, W.W., Zeng, X., Ma, L., Lu, Z., and Dara, J. 2004. "Removing a Barrier to Computer-Based Outbreak and Disease Surveillance-the Rods Open Source Project," MMWR (CDC) (53(Suppl)), pp. 34–41.Google Scholar
  24. EWORS. 2006. "Early Warning Outbreak Recognition System (EWORS)." Retrieved April 12, 2006, from http://www.namru2.med.navy.mil/ewors.htm
  25. Ford, D., Kaufman, J.H., Thomas, J., Eiron, I., and Hammer, M. 2005. "Spatiotemporal Epidemiological Modeler - a Tool for Spatiotemporal Modeling of Infectious Agents across the United States." Retrieved Oct 10, 2006, from http://www.alphaworks.ibm.com/tech/stem
  26. Goplin, J., Feist, M., and Miller, T. 2007. "Variation of Chief Complaint-Based Respiratory Symptom Data in One Hospital's Nurse Advice Call Center and Emergency Department," Advances in Disease Surveillance (2), p. 105.Google Scholar
  27. Goss, L., Carrico, R., Hall, C., and Humbaugh, K. 2003. "A Day at the Races: Communitywide Syndromic Surveillance During the 2002 Kentucky Derby Festival," Journal of Urban Health: Bulletin of the New York Academy of Medicine (80:2), p. i124.Google Scholar
  28. Hamby, T. 2006. "New Jersey Experience and Protocol Development." Biosurveillance Information Exchange Working Group.Google Scholar
  29. Heffernan, R., Mostashari, F., Das, D., Besculides, M., Rodriguez, C., Greenko, J., Steiner-Sichel, L., Balter, S., Karpati, A., Thomas, P., Phillips, M., Ackelsberg, J., Lee, E., Leng, J., Hartman, J., Metzger, K., Rosselli, R., and Weiss, D. 2004. "New York City Syndromic Surveillance Systems," MMWR (CDC) (53(Suppl)), pp. 23–27.Google Scholar
  30. Hope, K., Merritt, T., Eastwood, K., Main, K., Durrheim, D., Muscatello, D., Todd, K., and Zheng, W. 2008. "The Public Health Value of Emergency Department Syndromic Surveillance Following a Natural Disaster," Commun Dis Intell (32:1), pp. 92–94.Google Scholar
  31. Hutwagner L, Thompson W, Seeman GM, and T, T. 2003. "The Bioterrorism Preparedness and Response Early Aberration Reporting System (EARS)," J Urban Health (80(2 suppl 1)), pp. 89–96.Google Scholar
  32. Johnson, J.M. 2006. "To Ignore or Not to Ignore?" - Follow-up to Statistically Significant Signals." Biosurveillance Information Exchange Working Group.Google Scholar
  33. Johnson, J.M., Hicks, L., McClean, C., and Ginsberg, M. 2005. "Leveraging Syndromic Surveillance During the San Diego Wildfires, 2003," MMWR (CDC) (54(Suppl)), p. 190.Google Scholar
  34. Karras, B.T. 2005. "Syndromic Surveillance Information Collection - King County (SSIC-KC) for Bioterrorism Detection." Retrieved July 10, 2006, from http://www.phig.washington.edu/projectform_show.php?id=6
  35. Kleinman, K., Lazarus, R., and Platt, R. 2004. "A Generalized Linear Mixed Models Approach for Detecting Incident Cluster/Signals of Disease in Small Areas, with an Application to Biological Terrorism (with Invited Commentary)," American Journal of Epidemiology (159), pp. 217–224.PubMedCrossRefGoogle Scholar
  36. Lazarus, R., Kleinman, K., Dashevsky, I., DeMaria, A., and Platt, R. 2001. "Using Automated Medical Records for Rapid Identification of Illness Syndromes (Syndromic Surveillance): The Example of Lower Respiratory Infection," BMC Public Health (1:9).PubMedCrossRefGoogle Scholar
  37. Lober, W.B., Karras, B.T., and Wagner, M.M. 2002. "Roundtable on Bioterrorism Detection: Information System-Based Surveillance," Journal of American Medical Informatics Association (9), pp. 105–115.CrossRefGoogle Scholar
  38. Lombardo, J., Burkom, H., Elbert, E., Magruder, S.F., Lewis, S.H., Loschen, W., Sari, J., Sniegoski, C., Wojcik, R., and Pavlin, J. 2003. "A Systems Overview of the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE II)," Journal of Urban Health: Bulletin of the New York Academy of Medicine (80:2), pp. 32–42.Google Scholar
  39. Lombardo, J., Burkom, H., and Pavlin, J. 2004. "Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE II), Framework for Evaluating Syndromic Surveillance Systems," Syndromic surveillance: report from a national conference, 2003. MMWR 2004 (53(Suppl)), pp. 159–165.Google Scholar
  40. Lu, H.-M., Zeng, D., Trujillo, L., Komatsu, K., and Chen, H. 2008. "Ontology-Enhanced Automatic Chief Complaint Classification for Syndromic Surveillance," Journal of Biomedical Informatics (41:2), pp. 340–356.PubMedCrossRefGoogle Scholar
  41. Ma, H., Rolka, H., Mandl, K., Buckeridge, D., Fleischauer, A., and Pavlin, J. 2005. "Implementation of Laboratory Order Data in Biosense Early Event Detection and Situation Awareness System," MMWR (CDC) (54(Suppl)), pp. 27–30.Google Scholar
  42. Ma, J., Zeng, D., and Chen, H. 2006. "Spatial-Temporal Cross-Correlation Analysis: A New Measure and a Case Study in Infectious Disease Informatics," ISI 2006, San Diego, CA: Springer Lecture Notes in Computer Science, pp. 542–547.Google Scholar
  43. Meynard, J.-B., Chaudet, H., Texier, G., Ardillon, V., Ravachol, F., Deparis, X., Jefferson, H., Dussart, P., Morvan, J., and Boutin, J.-P. 2008. "Value of Syndromic Surveillance within the Armed Forces for Early Warning During a Dengue Fever Outbreak in French Guiana in 2006," BMC Medical Informatics and Decision Making (8:29).PubMedCrossRefGoogle Scholar
  44. Miller, S., Fallon, K., and Anderson, L. 2003. "New Hampshire Emergency Department Syndromic Surveillance System " Journal Journal of Urban Health (80(Suppl)), p. 118.CrossRefGoogle Scholar
  45. Muscatello, D., Churches, T., Kaldor, J., Zheng, W., Chiu, C., and Correll, P. 2005. "An Automated, Broad-Based, near Realtime Public Health Surveillance System Using Presentations to Hospital Emergency Departments in New South Wales, Australia," BMC Public Health (5), pp. 141–152.PubMedCrossRefGoogle Scholar
  46. Naumova, E.N., O'Neil, E., and MacNeill, I. 2005. "INFERNO: A System for Early Outbreak Detection and Signature Forecasting," MMWR (CDC) (54(Suppl)), pp. 77–83.Google Scholar
  47. Nekomoto, T.S., Riggins, W.S., and Franklin, M. 2003. "Pilot Results: Syndromic Surveillance Utilizing Catalis Health Point-of-Care Technology in a Rural Texas Outpatient Clinic." Retrieved June 23, 2006, from www.thecatalis.com/syndromic/SyndromicSurveillanceusingCatalis.pdf
  48. Ohkusa, Y., Shigematsu, M., Taniguchi, K., and Okabe, N. 2005a. "Experimental Surveillance Using Data on Sales of Over-The-Counter Medications - Japan, November 2003 - April 2004," MMWR (CDC) (54(Suppl)), pp. 47–52.Google Scholar
  49. Pelat, C., Boëlle, P.-Y., Cowling, B.J., Carrat, F., Flahault, A., Ansart, S., and Valleron, A.-J. 2007. "Online Detection and Quantification of Epidemics," BMC Medical Informatics and Decision Making (7:29).PubMedCrossRefGoogle Scholar
  50. Platt, R., Bocchino, C., Caldwell, B., Harmon, R., Kleinman, K., Lazarus, R., Nelson, A.F., Nordin, J.D., and Ritzwoller, D.P. 2003. "Syndromic Surveillance Using Minimum Transfer of Identifiable Data: The Example of the National Bioterrorism Syndromic Surveillance Demonstration Program," Journal of Urban Health: Bulletin of the New York Academy of Medicine (80:2).Google Scholar
  51. Reis, B.Y., Kohane, I.S., and Mandl, K.D. 2007. "An Epidemiological Network Model for Disease Outbreak Detection," PLoS Medicine (4:6), pp. 1019–1031.CrossRefGoogle Scholar
  52. Ritter, T. 2002. "Leaders: Lightweight Epidemiology Advanced Detection and Emergency Response System," SPIE, pp. 110–120.Google Scholar
  53. Siegrist, D., McClellan, G., Campbell, M., Foster, V., Burkom, H., Hogan, W., Cheng, K., Pavlin, J., and Kress, A. 2004. "Evaluation of Algorithms for Outbreak Detection Using Clinical Data from Five US Cities." Technical Report, Darpa Bio-Alirt Program.Google Scholar
  54. Sokolow, L.Z., Grady, N., Rolka, H., Walker, D., McMurray, P., English-Bullard, R., and Loonsk, J. 2005. "Deciphering Data Anomalies in Biosense," MMWR (CDC) (54(Suppl)), pp. 133–140.Google Scholar
  55. Suzuki, S., Ohyama, T., Taniguchi, K., Kimura, M., Kobayashi, J., Okabe, N., Sano, T., Kuwasaki, T., and Nakatani, H. 2003. "Web-Based Japanese Syndromic Surveillance for Fifa World Cup 2002," Journal of Urban Health: Bulletin of the New York Academy of Medicine (80:2), p. i123.Google Scholar
  56. Thurmond, M. 2006. "Global Foot-and-Mouth Disease Modeling and Surveillance." Arizona Biosurveillnace Workshop.Google Scholar
  57. Tsui, F.-C., Espino, J.U., Dato, V.M., Gesteland, P.H., Hutman, J., and Wagner, M.M. 2003. "Technical Description of Rods: A Real-Time Public Health Surveillance System," Journal of American Medical Informatics Association 2003 (10), pp. 399–408.CrossRefGoogle Scholar
  58. Uhde, K.B., Farrell, C., Geddie, Y., Leon, M., and Cattani, J. 2005. "Early Detection of Outbreaks Using the Biodefend? Syndromic Surveillance System - Florida, May 2002 - July 2004 " MMWR (CDC) (54(Suppl)), p. 204.Google Scholar
  59. Umland, E., Brillman, J., Koster, F., Joyce, E., Forslund, D., Picard, R., Burr, T., Sewell, C., Castle, S., and Bersell, K. 2003. "Fielding the Bio-Surveillance Analysis, Feedback, Evaluation and Response (B-Safer) System," BTR Albuquerque, NM, pp. 185–190.Google Scholar
  60. Vergu, E., Grais, R.F., Sarter, H., Fagot, J.-P., Lambert, B., Valleron, A.-J., and Flahault, A. 2006. "Medication Sales and Syndromic Surveillance, France," Emerging Infectious Diseases [serial on the Internet] (12:3), pp. 416–421.CrossRefGoogle Scholar
  61. Yih, W., Caldwell, B., and Harmon, R. 2004. "The National Bioterrorism Syndromic Surveillance Demonstration Program," MMWR (CDC) (53(Suppl)), pp. 43–46.Google Scholar
  62. Zelicoff, A. 2002. "The Rapid Syndrome Validation Project (RSVP) ™ Users' Manual and Description."Google Scholar
  63. Zelicoff, A., Brillman, J., and Forslund, D. 2001. "The Rapid Syndrome Validation Project (R RSVP)," AMIA Symposium, pp. 771–775.Google Scholar
  64. Zeng, D., Chang, W., and Chen, H. 2004a. "A Comparative Study of Spatio-Temporal Hotspot Analysis Techniques in Security Informatics," 7th IEEE Transactions on Intelligent Transportation Systems, Washington, DC, pp. 106–111.Google Scholar
  65. Zeng, D., Chen, H., Tseng, C., Chang, W., Eidson, M., Gotham, I., and Lynch, C. 2005a. "Bioportal: A Case Study in Infectious Disease Informatics." JCDL, Denver, CA, USA, p. 418.Google Scholar
  66. Zhang, G., O'Connell, E., Leguen, F., Bustamante, M., Rodriguez, D., and Borroto-Ponce, R. 2007. "Use of Epidemiological Knowledge to Create Syndromic Surveillance Reports," Advances in Disease Surveillance (4), p. 211.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Hsinchun Chen
    • 1
  • Daniel Zeng
    • 2
    • 3
  • Ping Yan
    • 2
  1. 1.Department of Management Information SystemsEller College of Management University of ArizonaTucsonUSA
  2. 2.Department of Management Information SystemsEller College of Management University of ArizonaTucsonUSA
  3. 3.Chinese Academy of SciencesBeijingChina

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