Epidemics of the “common cold” and the dynamics of severe asthma exacerbation

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. 

March 14, 2017

Bayesian Surveillance for the Detection of Small Area Health Anomalies

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.

March 14, 2017

Cross-Disciplinary Consultancy to Enhance Predictions of Asthma Exacerbation Risk in Boston

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.

March 24, 2017

Models for Forecasting Asthma Exacerbations in Urban Environments

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

March 23, 2017

Models for Forecasting Asthma Exacerbations in Urban Environments

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

March 24, 2017

Early Estimation of the Basic Reproduction Number Using Minimal Outbreak Data

The basic reproduction number represents the number of secondary infections expected to be caused by an infectious individual introduced into an entirely susceptible population. It is a fundamental measure used to characterize infectious disease outbreaks and is essential in developing mathematical models to determine appropriate interventions. Much work has been done to investigate methods for estimating the basic reproduction number during the early stages of infectious disease outbreaks.

August 28, 2017

Ensuring the Week Goes Smoothly - Improving Daily Surveillance Visualization

Real-time syndromic surveillance requires daily surveillance of a range of health data sources. Most real-time data sources from health care systems exhibit large day of the week fluctuations as service provision and patient behaviour varies by day of the week. Regular day of the week effects are further complicated by the occurrence of public holidays (usually 8 per year in England), which can limit the availability of certain services and affect patient behaviour.

August 29, 2017

Infectious Disease Forecast Modeling

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

March 23, 2017

Infectious Disease Forecast Modeling

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

March 24, 2017

Computational Method for Epidemic Detection in Multiple populations

Currently Centers for Disease Control and Prevention (CDC) employ threshold rules to declare epidemic outbreaks, such as influenza, separately in each population. However each year influenza starts in one population and spreads population-to-population throughout the country. Therefore there is a need for an algorithm to declare the epidemic that uses information from multiple populations.

Objective

Detect epidemics over multiple Populations using computational methods

October 03, 2017

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