Syndromic surveillance: reports from a national conference, 2003

Overview of Syndromic Surveillance -- What is Syndromic Surveillance? -- Linking Better Surveillance to Better Outcomes -- Review of the 2003 National Syndromic Surveillance Conference - Lessons Learned and Questions To Be Answered -- -- System Descriptions -- New York City Syndromic Surveillance Systems -- Syndrome and Outbreak Detection Using Chief-Complaint Data - Experience of the Real-Time Outbreak and Disease Surveillance Project -- Removing a Barrier to Computer-Based Outbreak and Disease Surveillance - The RODS Open Source Project -- National Retail Data Monitor for Public Health Surveillance -- National Bioterrorism Syndromic Surveillance Demonstration Program -- Daily Emergency Department Surveillance System - Bergen County, New Jersey -- Hospital Admissions Syndromic Surveillance - Connecticut, September 2001-November 2003 -- BioSense - A National Initiative for Early Detection and Quantification of Public Health Emergencies -- Syndromic Surveillance at Hospital Emergency Departments - Southeastern Virginia -- -- Research Methods -- Bivariate Method for Spatio-Temporal Syndromic Surveillance -- Role of Data Aggregation in Biosurveillance Detection Strategies with Applications from ESSENCE -- Scan Statistics for Temporal Surveillance for Biologic Terrorism -- Approaches to Syndromic Surveillance When Data Consist of Small Regional Counts -- Algorithm for Statistical Detection of Peaks - Syndromic Surveillance System for the Athens 2004 Olympic Games -- Taming Variability in Free Text: Application to Health Surveillance -- Comparison of Two Major Emergency Department-Based Free-Text Chief-Complaint Coding Systems -- How Many Illnesses Does One Emergency Department Visit Represent? Using a Population-Based Telephone Survey To Estimate the Syndromic Multiplier -- Comparison of Office Visit and Nurse Advice Hotline Data for Syndromic Surveillance - Baltimore-Washington, D.C., Metropolitan Area, 2002 -- Progress in Understanding and Using Over-the-Counter Pharmaceuticals for Syndromic Surveillance -- -- Evaluation -- Evaluation Challenges for Syndromic Surveillance - Making Incremental Progress -- Measuring Outbreak-Detection Performance By Using Controlled Feature Set Simulations -- Evaluation of Syndromic Surveillance Systems - Design of an Epidemic Simulation Model -- Benchmark Data and Power Calculations for Evaluating Disease Outbreak Detection Methods -- Bio-ALIRT Biosurveillance Detection Algorithm Evaluation -- ESSENCE II and the Framework for Evaluating Syndromic Surveillance Systems -- Conducting Population Behavioral Health Surveillance by Using Automated Diagnostic and Pharmacy Data Systems -- Evaluation of an Electronic General-Practitioner-Based Syndromic Surveillance System -- National Symptom Surveillance Using Calls to a Telephone Health Advice Service - United Kingdom, December 2001-February 2003 -- Field Investigations of Emergency Department Syndromic Surveillance Signals - New York City -- Should We Be Worried? Investigation of Signals Generated by an Electronic Syndromic Surveillance System - Westchester County, New York -- -- Public Health Practice -- Public Health Information Network - Improving Early Detection by Using a Standards-Based Approach to Connecting Public Health and Clinical Medicine -- Information System Architectures for Syndromic Surveillance -- Perspective of an Emergency Physician Group as a Data Provider for Syndromic Surveillance -- SARS Surveillance Project - Internet-Enabled Multiregion Surveillance for Rapidly Emerging Disease -- Health Information Privacy and Syndromic Surveillance SystemsPapers from the second annual National Syndromic Surveillance Conference convened by the New York City Department of Health and Mental Hygiene, the New York Academy of Medicine, and the CDC in New York City during Oct. 23-24, 2003. Published as the September 24, 2004 supplement to vol. 53 of MMWR. Morbidity and mortality weekly report.1571461

Integration of Syndromic Surveillance Data into Public Health Practice at State and Local Levels in North Carolina

We sought to describe the integration of syndromic surveillance data into daily surveillance practice at local health departments (LHDs) and make recommendations for the effective integration of syndromic and reportable disease data for public health use

Data-driven Disease Surveillance

The recent and still ongoing pandemic of SARS-CoV-2 has shown that an infectious disease outbreak can have serious consequences on public health and economy. In this situation, public health officials constantly aim to control and reduce the number of infections in order to avoid overburdening health care system. Besides minimizing personal contact through political measures, a fundamental approach to contain the spread of diseases is to isolate infected individuals. The effectiveness of the latter approach strongly depends on a timely detection of the outbreak as the tracking of individuals can quickly become infeasible when the number of cases increases. Hence, a key factor in the containment of an infectious disease is the early detection of a potential larger outbreak, commonly known as outbreak detection. For this purpose, epidemiologists rely on a variety of statistical surveillance methods in order to maintain an overview of the current situation of infections by either monitoring confirmed cases or cases with early symptoms. Mainly based on statistical hypothesis testing, these methods automatically raise an alarm if an unexpected increase in the number of infections is observed. The practical usefulness of such methods highly depends on the trade-off between the ability to detect outbreaks and the chances of raising a false alarm. However, this hypothesis-based approach to disease surveillance has several limitations. On the one hand, it is a hand-crafted approach which requires domain knowledge to set up the statistical methods, especially if early symptoms are monitored. On the other hand, outbreaks of emerging infectious diseases with different symptom patterns are likely to be missed by such a surveillance system. In this thesis, we focus on data-driven disease surveillance and address these challenges in the following ways. To support epidemiologists in the process of defining reliable disease patterns for monitoring cases with early symptoms, we present a novel approach to discover such patterns in historic data. With respect to supervised learning, we propose a fusion classifier which can combine the output of multiple statistical methods using the univariate time series of infection counts as the only source of information. In addition, we develop algorithms based on unsupervised learning which frame the task of outbreak detection as a general anomaly detection task. This even includes the surveillance of emerging infectious diseases. Therefore, we contribute a novel framework and propose a new approach based on sum-product networks to monitor multiple disease patterns simultaneously. Our results show that data-driven approaches are ideal to assist epidemiologists by processing large amounts of data that cannot fully be understood and analyzed by humans. Most significantly, the incorporation of additional information into the surveillance through machine learning techniques shows reliable and promising results

Emerg Infect Dis

Emerging Infectious Diseases is providing access to these abstracts on behalf of the ICEID 2022 program committee (http://www.iceid.org), which performed peer review. ICEID is organized by the Centers for Disease Control and Prevention and Task Force for Global Health, Inc.Emerging Infectious Diseases has not edited or proofread these materials and is not responsible for inaccuracies or omissions. All information is subject to change. Comments and corrections should be brought to the attention of the authors.Suggested citation: Authors. Title [abstract]. International Conference on Emerging Infectious Diseases 2022 poster and oral presentation abstracts. Emerg Infect Dis. 2022 Sep [date cited]. http://www.cdc.gov/EID/pdfs/ICEID2022.pdf2022PMC94238981187

Real-time classifiers from free-text for continuous surveillance of small animal disease

A wealth of information of epidemiological importance is held within unstructured narrative clinical records. Text mining provides computational techniques for extracting usable information from the language used to communicate between humans, including the spoken and written word. The aim of this work was to develop text-mining methodologies capable of rendering the large volume of information within veterinary clinical narratives accessible for research and surveillance purposes. The free-text records collated within the dataset of the Small Animal Veterinary Surveillance Network formed the development material and target of this work. The efficacy of pre-existent clinician-assigned coding applied to the dataset was evaluated and the nature of notation and vocabulary used in documenting consultations was explored and described. Consultation records were pre-processed to improve human and software readability, and software was developed to redact incidental identifiers present within the free-text. An automated system able to classify for the presence of clinical signs, utilising only information present within the free-text record, was developed with the aim that it would facilitate timely detection of spatio-temporal trends in clinical signs. Clinician-assigned main reason for visit coding provided a poor summary of the large quantity of information exchanged during a veterinary consultation and the nature of the coding and questionnaire triggering further obfuscated information. Delineation of the previously undocumented veterinary clinical sublanguage identified common themes and their manner of documentation, this was key to the development of programmatic methods. A rule-based classifier using logically-chosen dictionaries, sequential processing and data-masking redacted identifiers while maintaining research usability of records. Highly sensitive and specific free-text classification was achieved by applying classifiers for individual clinical signs within a context-sensitive scaffold, this permitted or prohibited matching dependent on the clinical context in which a clinical sign was documented. The mean sensitivity achieved within an unseen test dataset was 98.17 (74.47, 99.9)% and mean specificity 99.94 (77.1, 100.0)%. When used in combination to identify animals with any of a combination of gastrointestinal clinical signs, the sensitivity achieved was 99.44% (95% CI: 98.57, 99.78)% and specificity 99.74 (95% CI: 99.62, 99.83). This work illustrates the importance, utility and promise of free-text classification of clinical records and provides a framework within which this is possible whilst respecting the confidentiality of client and clinician