A5067433707- Data-Driven Disease Surveillance
- Anomaly Detection Techniques and Applications
- Public Health Policies and Education
- Influenza Virus Research Studies
- Bacillus and Francisella bacterial research
- COVID-19 epidemiological studies
- Zoonotic diseases and public health
- Advanced Statistical Process Monitoring
- Animal Disease Management and Epidemiology
- Respiratory viral infections research
- Emergency and Acute Care Studies
- Ethics in Clinical Research
- Primary Care and Health Outcomes
- Poisoning and overdose treatments
- Healthcare Policy and Management
- Chronic Disease Management Strategies
- Viral Infections and Outbreaks Research
- Opioid Use Disorder Treatment
- Vaccine Coverage and Hesitancy
- Food Security and Health in Diverse Populations
- Air Quality and Health Impacts
- Forecasting Techniques and Applications
- Climate Change and Health Impacts
- Artificial Intelligence in Healthcare
- Forensic Toxicology and Drug Analysis
Johns Hopkins University Applied Physics Laboratory
2013-2024
E Ink (South Korea)
2024
Boyds (United Kingdom)
2023
Allentown Public Library
2013-2015
Center for Surveillance, Epidemiology, and Laboratory Services
2013
Johns Hopkins University
2003-2013
Special Olympics
2012
Centers for Disease Control and Prevention
2009
Armed Forces Research Institute of Medical Science
2009
International Society for Infectious Diseases
2008
Abstract Modern biosurveillance is the monitoring of a wide range prediagnostic and diagnostic data for purpose enhancing ability public health infrastructure to detect, investigate, respond disease outbreaks. Statistical control charts have been central tool in classic surveillance also migrated into modern biosurveillance; however, new types monitored, processes underlying time series derived from these data, application context all deviate industrial setting which tools were originally...
Abstract For robust detection performance, traditional control chart monitoring for biosurveillance is based on input data free of trends, day‐of‐week effects, and other systematic behaviour. Time series forecasting methods may be used to remove this behaviour by subtracting forecasts from observations form residuals algorithmic input. We describe three forecast compare their predictive accuracy each 16 authentic syndromic streams. The are (1) a non‐adaptive regression model using long...
The District of Columbia (DC) Department Health, under a grant from the US Centers for Disease Control and Prevention, established an Environmental Public Health Tracking Program. As part this program, goals contextual pilot study are to quantify short-term associations between daily pediatric emergency department (ED) visits admissions asthma exacerbations with ozone particulate concentrations, broader socio-economic status age group. Data included counts de-identified asthma-related ED DC...
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The Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE II) is a prototype syndromic surveillance system capturing and analyzing public health indicators early detection disease outbreaks.This paper presents preliminary evaluation ESSENCE II according to CDC framework evaluating systems.Each major topic addressed in this assessment performance.ESSENCE captures data multiple formats, parses text strings into syndrome groupings, applies temporal...
Abstract The objective of this report is to provide a basis inform decisions about priorities for developing statistical research initiatives in the field public health surveillance emerging threats. Rapid information system advances have created vast opportunity secondary data sources enhance situational and status awareness populations. While medical informatics standardize healthcare‐seeking encounter records continue accelerating, it necessary adapt analytic methodologies mature sync...
The Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) is a secure web-based tool that enables health care practitioners to monitor indicators public importance detection and tracking disease outbreaks, consequences severe weather, other events concern. ESSENCE concept began in an internally funded project at Johns Hopkins University Applied Physics Laboratory, advanced with funding from State Maryland, broadened 1999 as collaboration Walter Reed...
The paucity of outbreak data from biologic terrorism and emerging infectious diseases limits the evaluation syndromic surveillance systems. Evaluation using naturally occurring outbreaks proxy disease (e.g., influenza) is one alternative but does not allow for rigorous evaluation. Another approach to inject simulated into real background data, existing simulation models generally do account such factors as spatial mobility explicitly incorporate knowledge agent.The objective this analysis...
Syndromic surveillance systems are used to monitor daily electronic data streams for anomalous counts of features varying specificity. The monitored quantities might be clinical diagnoses, sales over-the-counter influenza remedies, school absenteeism among a given age group, and so forth. Basic data-aggregation decisions these include determining which records count how group them in space time.This paper discusses the application spatial temporal strategies multiple alerting algorithms...
Abstract The primary objective of this ecologic and contextual study is to determine statistically significant short-term associations between air quality (daily ozone particulate concentrations) Medicaid patient general acute care daily visits for asthma exacerbations over 11 years Washington, DC residents, identify regions populations that may experience increased related quality. After removing long-term trends day-of-week effects in the data, Poisson regression was applied time series...
Abstract BioSense is a US national system that uses data from health information systems for automated disease surveillance. We studied 4 time-series algorithm modifications designed to improve sensitivity detecting artificially added data. To test these modified algorithms, we used reports of daily syndrome visits 308 Department Defense (DoD) facilities and 340 hospital emergency departments (EDs). At constant alert rate 1%, was improved both datasets by using minimum standard deviation...
This study introduces new information fusion algorithms to enhance disease surveillance systems with Bayesian decision support capabilities. A detection system was built and tested using chief complaints from emergency department visits, International Classification of Diseases Revision 9 (ICD-9) codes records outpatient visits civilian military facilities, influenza data health departments in the National Capital Region (NCR). Data anomalies were identified distribution time offsets between...
This paper describes the problem of public health monitoring for waterborne disease outbreaks using disparate evidence from surveillance data streams and environmental sensors. We present a combined approach along with examples recent project at Johns Hopkins University Applied Physics Laboratory in collaboration U.S. Environmental Protection Agency. The objective was to build module Electronic Surveillance System Early Notification Community-based Epidemics (ESSENCE) include water quality...
Broadly, this research aims to improve the outbreak detection performance and, therefore, cost effectiveness of automated syndromic surveillance systems by building novel, recombinant temporal aberration algorithms from components previously developed detectors.
Abstract This paper discusses further advances in making robust predictions with the Holt–Winters forecasts for a variety of syndromic time series behaviors and introduces control‐chart detection approach based on these forecasts. Using three collections data, we compare biosurveillance alerting methods quantified measures forecast agreement, signal sensitivity, time‐to‐detect. The study presents practical rules initialization parameterization series. Several outbreak scenarios are used...
Syndromic surveillance has expanded since 2001 in both scope and geographic reach benefited from research studies adapted numerous disciplines. The practice of syndromic continues to evolve rapidly. International Society for Disease Surveillance solicited input its global network on key questions, with the goal improving practice. A workgroup subject matter experts was convened February June 2016 review categorize proposed topics. identified 12 topic areas 4 categories: informatics,...
ABSTRACT Objective: We evaluated emergency department (ED) data, medical services (EMS) and public utilities data for describing an outbreak of carbon monoxide (CO) poisoning following a windstorm. Methods: Syndromic ED were matched against previously collected chart abstraction data. ran detection algorithms on selected time series derived from all 3 sources to identify health events associated with the CO outbreak. used spatial spatiotemporal scan statistics geographic areas that most...
Epidemiological modeling for infectious disease is important management and its routine implementation needs to be facilitated through better description of models in an operational context. A standardized model characterization process that allows selection or making manual comparisons available their results currently lacking. key need a universal framework facilitate understanding features. Los Alamos National Laboratory (LANL) has developed comprehensive can used characterize The was...