Data quality visualization for aggregate surveillance data with application to now-casting

This webinar will present a set of tools developed for visualizing data quality problems in aggregate surveillance data, in particular for data which accrues over a period of time. This work is based on a data quality analysis of aggregate data used for ILI surveillance within the Distribute system formerly operated by the ISDS. We will present a method developed as a result of this analysis to ‘nowcast’ complete data from incomplete, partially accruing data, as an example of how forecasting methods can be used to mitigate data quality problems.


March 13, 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

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

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

Enhancing EpiCenter Data Quality Analytics with R

The EpiCenter syndromic surveillance platform currently uses Java libraries for time series analysis. Expanding the data quality capabilities of EpiCenter requires new analysis methods. While the Java ecosystem has a number of resources for general software engineering, it has lagged behind on numerical tools. As a result, including additional analytics requires implementing the methods de novo.

August 29, 2017

Identifying Depression-Related Tweets from Twitter for Public Health Monitoring

Major depressive disorder has a lifetime prevalence of 16.6% in the United States. Social media platforms – e.g. Twitter, Facebook, Reddit – are potential resources for better understanding and monitoring population-level mental health status over time. Based on DSM-5 diagnostic criteria, our research aims to develop a natural language processing-based system for monitoring major depressive disorder at the population-level using public social media data.


October 10, 2017

Monitoring Media Content About Vaccines in the United States: Data from the Vaccine Sentimeter

The success of public health campaigns in decreasing or eliminating the burden of vaccine-preventable diseases can be undermined by media content influencing vaccine hesitancy in the population. A tool for tracking and describing the ever-growing platforms for such media content can help decide how and where to invest in campaigns to increase public confidence in vaccines.

September 01, 2017

Performance of Early Outbreak Detection Algorithms in Public Health Surveillance from a Simulation Study

Early detection of outbreaks is crucial in public health surveillance in order to enable rapid control measures. Statistical methods are widely used for outbreak detection but no study has proposed to evaluate and compare thoroughly the performance of these methods.


Evaluate the performance of 8 statistical methods for outbreak detection in health surveillance with historical data.

September 01, 2017

Place Matters: Revealing Infectious Disease Disparities Using Area-Based Poverty

Most public health surveillance systems in the United States do not capture individual-level measures of socioeconomic position. Without this information, socioeconomic disparities in health outcomes can be hidden. However, US Census data can be used to describe neighborhood-level socioeconomic conditions like poverty and crowding. Place matters. Neighborhood affects health independently of personal characteristics.

September 01, 2017

Using Bayesian Networks to Assist Decision-Making in Syndromic Surveillance

Syndromic surveillance systems often produce large numbers of detections due to excess activity (alarms) in their indicators. Few alarms are classified as alerts (public health events that may require a response). Decision-making in syndromic surveillance as to whether an alarm requires a response (alert) is often entirely based on expert knowledge. These approaches (known as heuristics) may work well and produce faster results than automated processes (known as normative), but usually rely on the expertise of a small group of experts who hold much of their knowledge implicitly.

September 20, 2017


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