Use for the Analytic Solutions for Real-Time Surveillance: Asyndromic Cluster Detection consultancy held June 9-10, 2015 at the University of North Carolina, Chapel Hill.
Problem Summary
Data Analytics
Materials associated with the Analytic Solutions for Real-Time Biosurveillance: Assessing Risk for Emerging Arboviral Disease consultancy held June 14-15, 2016 at the Arizona Department of Health Services.
Problem Summary
Materials associated with the Analytic Solutions for Real-Time Biosurveillance: Negation Processing in Free Text Emergency Department Data for Public Health Surveillance consultancy held January 19-20, 2017 at the University of Utah, Salt Lake City.
Problem Summary
False positive syndrome hits are created when a syndromic classification process cannot properly identify negated terms. For example, a visit is classified into a fever syndrome when the chief complaint or triage note says “denies fever.”
Materials associated with the Analytic Solutions for Real-Time Biosurveillance: Reportable Disease Cluster Detection in the Context of Sporadic Adoption of PCR-based Diagnostic Tests consultancy call held May 3, at 12 pm ET.
Problem Summary
Materials associated with the Analytic Solutions for Real-Time Biosurveillance: Models for Forecasting Asthma Exacerbations in Urban Environments consultancy held March 30-31, 2016 at the Boston Public Health Commission (BPHC).
Problem Summary
Materials associated with the Analytic Solutions for Real-Time Biosurveillance: Infectious Disease Forecast Modeling consultancy held October 29-30, 2015 in Falls Church, Virginia.
Problem Summary
Materials associated with the Analytic Solutions for Real-Time Surveillance: Asyndromic Cluster Detection consultancy held June 9-10, 2015 at the University of North Carolina, Chapel Hill.
Problem Summary
Public Health England uses data from four national syndromic surveillance systems to support public health programmes and identify unusual activity. Each system monitors a wide range of respiratory, gastrointestinal and other syndromes at a local, regional and national level. As a result, over 12,000 ‘signals’ (combining syndrome and geography) need to be assessed each day to identify aberrations. In this webinar I will describe how the ‘big data’ collected daily are translated into useful information for public health surveillance.
The surveillance task when faced with small area health data is more complex than in the time domain alone. Both changes in time and space must be considered. Such questions as ‘where will the infection spread to next?’ and, ‘when will the infection arrive here’, or ‘when do we see the end of the epidemic?’ are all spatially specific questions that are commonly of concern for both the public and public health agencies. Hence both spatial and temporal dimensions of the surveillance task must be considered.
Common colds are one of the principal causes of severe exacerbations in asthmatic people, reflected in epidemic-like waves of asthma hospitalizations. Most studies do not estimate the effect of infectious causes of exacerbations, and cannot account for how this risk changes through time.