This paper continues an initiative conducted by the International Society for Disease Surveillance with funding from the Defense Threat Reduction Agency to connect near-term analytical needs of public health practice with technical expertise from the global research community. The goal is to enhance investigation capabilities of day-to-day population health monitors.
Modeling
Use case for 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
Use for 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 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
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.
Surveillance data on various notifiable diseases usually consist of multiple time series of daily, weekly, or monthly counts of new infections. Data are typically reported in several strata defined through administrative geographical areas, gender and/or age groups. Statistical modeling of the resulting multivariate time series is an important task in infectious disease epidemiology. We will discuss time series models - specifically developed for multivariate surveillance count data - that can be used for two distinct roles, understanding and prediction of disease spread.