Presenters
Wayne Loschen, Johns Hopkins University Applied Physics Laboratory (JHU-APL)
Karl Soetebier, MPW, Georgia Division of Public Health
Paul Picciano, PhD, Aprima Medical Software
Frank Hardisty, Pennsylvania State University
Date and Time
Wednesday, September 23, 2009
Host
ISDS Research Committee
The March 2010 webinar of the ISDS Public Health Practice Committee gave an overview regarding how Florida is incorporating visualizations of their reportable disease data into their syndromic surveillance system. The presentation was followed by a general discussion regarding the need for and benefits of comparing - on a regular, systematic basis - reportable disease data and syndromic surveillance data.
Presenters
Aaron Kite-Powell, MS, Bureau of Epidemiology, Florida Department of Health
Date
The panelists will present their current technical research using Twitter data for disease surveillance. Presentation topics may include filtering and processing of tweets, as well as the analysis and presentation of findings for surveillance purposes.
Panelists
Courtney Corley, Pacific Northwest National Laboratory
Marcel Salathe, Pennsylvania State University
Mark Cameron, Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Since their introduction to the US market in 2007, electronic cigarettes (e-cigarettes) have posed considerable challenges to both public health authorities and government regulators, especially given the debate – in both the scientific world and the community at large – regarding the potential advantages (e.g. helping individuals quit smoking) and disadvantages (e.g. renormalizing smoking) associated with the product1. Similarly, hookah – a kind of waterpipe used to smoke flavored tobacco – has increased in popularity in recent years, is known to be particularly popular among younger people, and has prompted a range of regulatory responses2. One important – and currently largely unexplored – area of research involves exploring consumer perceptions and experiences of these emerging tobacco products. In this work, we use online health discussion forums in conjunction with text mining and novel data visualization techniques to investigate consumer perceptions and experiences of e-cigarettes and hookah, focusing on the automatic identification of symptoms associated with each product, and consumer motivations for product use. Previous related research has focused on using text-mining to analyze e-cigarette or hookah related Twitter posts3,4 and on the qualitative identification of e-cigarette related symptoms from online discussion forums5. The research reported in this abstract is – to the best of our knowledge – the first time that text mining techniques have been used with online health forums to understand e-cigarette or hookah use.
Objective
Our aim in this work is to apply text mining and novel visualization techniques to textual data derived from online health discussion forums in order to better understand consumers’ experiences and perceptions of electronic cigarettes and hookah.
The use of R is increasing in the public health disease surveillance community. The ISDS pre-conference workshops and newly formed R Group for Surveillance have been well attended and continue to grow in popularity. The use of R in the National Syndromic Surveillance Program (NSSP) has also been of value to many users who wish to analyze and visualize public health data using custom R scripts. This interest in R, combined with a desire from many ESSENCE users to create custom analytics and visualizations, led to a summer internship project to look into the feasibility and ways R could be integrated into ESSENCE.
Objective
The objective of this project is to give users the ability to run custom R scripts from within the ESSENCE system. This capability would allow for custom analytics and visualizations to be baked into the system for daily use. It would also provide a sandbox area for new ideas and features to be tested before being developed more fully into the ESSENCE codebase for a more seamless use in the future. The project must do this while maintaining a secure environment for public health data to reside.
Booz Allen Hamilton is developing a novel bio-surveillance prototype tool, the Digital Disease Detection Dashboard (D4) to address the questions fundamental to daily biosurveillance analysis and decision making: is something unusual happening (e.g., is an outbreak or novel disease emerging)?, What is the probability that what I’m seeing is by chance?, How confident am I that this data is really detecting a signal?, Why is this happening and can I explain it?; and How many cases should I expect? (e.g., magnitude of event over time). These questions focus on detection, confidence, variance, and forecasting and D4 integrates a number of diverse analytical tools and methods that are crucial to a complete biosurveillance program.
Objective
To develop a web-enabled Digital Disease Detection Dashboard (D4) that allows users to statistically model and forecast multiple data streams for public health biosurveillance. D4 is a user-friendly, cloudenabled, and R Shiny-powered application that provides intuitive visualization enabling immediate situational awareness through interactive data displays and multi-factor analysis of traditional and non-traditional data feeds. The objective of D4 is to support public health decision making with high confidence across all four aspects of the biosurveillance continuum—detection, investigation, response, and prevention.
Simulations of infectious disease spread have increasingly been used to inform public policy for planning and response to outbreaks. As these techniques have increased in sophistication a wider array of uses becomes appropriate. In particular, surveillance system design, evaluation, and interpretation can be greatly aided by simulation. This presentation will describe a style of highly detailed agent-based simulation and a synthetic information analysis platform that is well equipped for these tasks.
This year’s conference theme is “Harnessing Data to Advance Health Equity” – and Washington State researchers and practitioners at the university, state, and local levels are leading the way in especially novel approaches to visualize health inequity and the effective translation of evidence into surveillance practice.
Objective
Washington is leading the way in especially novel approaches. Our goal is to share some of these innovative methods and discuss how these are used in State and Local monitoring of Health
Recently signed in Denver, the Paris Declaration demonstrates a collective resolution to end AIDS by continually monitoring these goals. However siloed data and in/out migration results in poor capacity to track population level care indicators for persons living with HIV (PLWH). Surveillance should not only enumerate PLWH but also support prevention and care programming (1). We designed and implemented the HIV Data to Care Tool to describe the continuum, from case finding to HIV care. This study describes a system to combine data sources to inform local HIV surveillance, outreach, and care. Development objectives included targeted community and clinical interventions and evaluation, user defined reports to identify subpopulation disparities, and a persistent data visualization readily available to stakeholders.
Objective
To describe Denver Public Health’s model for designing a business intelligence (BI) tool for HIV surveillance and outreach and the impact after implementation.
Investigation of cases, clusters, and outbreaks of infectious disease is a complex process requiring substantial support from protocols, distributed and cooperative work, and information systems. We set out to identify public health information needs, the types of data required to meet these needs, and the potential alignment with visualizations of this data.
Objective
The goal of this work is to identify specific work practices in disease investigation that would be supported by data visualization, such as identifying exposure, contact, and spatiotemporal clustering.