2014.ISDS.Abstracts.Final.pdf


ISDS Annual Conference Proceedings 2014. This is an Open Access article distributed under the terms of the Creative Commons Attribution-
Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, 
and reproduction in any medium, provided the original work is properly cited.

ISDS 2014 Conference Abstracts

Digital Disease Detection Dashboard: Rapid Detection & 
Outbreak Management Tool

Karina N. Alvarez*, Catherine Ordun, Jane Blake, Kirsten A. Simmons, Keith Hansen, 
Dan Baker, Lynda Rowe, Yusra Ahmad, Donald M. Eby, Dimitrios Koutsonanos, Steve 
Escaravage and KC Decker

Booz Allen Hamilaton, Atlanta, GA, USA

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, cloud-
enabled, 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.

Introduction
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.

Description
D4 utilizes a big data cloud-based reference architecture and is 

comprised of widgets - each built in R Shiny Studio using R code. D4 
includes a user interactive map with “hot spots” of potential adverse 
events, customized databases with rapid query data visualizations, 
searchable social media (Twitter) using regular stacked expressions, 
and forecast models. Central to D4 is the Disease Correlation Model 
(DCM), which uses linear regression to test the statistical significance 
of the relationship between the outcome of interest and explanatory 
factors (e.g., city, climate, and social media data). Included is an 
adjusted R2 widget and p-value output to quickly quantify a DCM 
output goodness-of-fit. In addition, a forecasted widget calculates the 
number of estimated cases per day, over the number of days in the 
future by using estimates from a linear regression model that only 
includes time as the explanatory factor. For disease detection data 
generated from high-throughput laboratory analyses (e.g., real time 
PCR detection assays), a Laboratory Quality Status widget evaluates 
all associated data generated though quality management systems 
and outputs a Boolean (TRUE/FALSE) test result to provide the 
operator with a rapid quality assurance validation to laboratory data.
D4 integrates multiple data streams into an R coding environment 
that enables open-source and plug-play statistical and widget-
development capabilities. D4 backend resides in an Amazon cloud 
instance allowing for scalability, speed, and controlled distributed 
access. As a prototype, D4 currently injects examples of the multiple 
data streams that may be of interest such as social media (Twitter), 
climate data (precipitation, temperature), 911 call data, and two 

explanatory data sets that are epidemiological data (foodborne illness 
cases, flu patient counts) and other laboratory diagnosis data. D4 
is currently modeling three US cities and its architecture enables 
access and filtering for hundreds of historic and real-time data feeds 
via representational state transfer (REST) application programming 
interface (API) calls, enabling incorporation of standardized, reliable, 
and timely information.

Audience Engagement
D4 is bringing new innovations and analytical tools in the area of 

bio-surveillance, disease detection, and disease reporting. Depending 
on the source and quality of data, it can provide forecasting 
models with high accuracy accounting for different parameters 
and characteristics different types of disease demonstrate. Besides 
the advanced analytical tools D4 offers, what makes this platform 
distinct and innovative is the ability to accept and handle data from 
multiple sources and formats and allows for increased sensitivity and 
specificity of the system.

Keywords
Biosurveillance; Disease detection; Open-Source Analytics; 
Visualizations; R

Acknowledgments

JR Charlton and Kris Ford

*Karina N. Alvarez

E-mail: alvarez_karina@bah.com    

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * (1):e2, 201