To introduce Soda Pop, an R/Shiny application designed to be a
disease agnostic time-series clustering, alarming, and forecasting
tool to assist in disease surveillance “triage, analysis and reporting”
workflows within the Biosurveillance Ecosystem (BSVE) . In this
poster, we highlight the new capabilities that are brought to the BSVE
by Soda Pop with an emphasis on the impact of metholodogical
The Biosurveillance Ecosystem (BSVE) is a biological and
chemical threat surveillance system sponsored by the Defense Threat
Reduction Agency (DTRA). BSVE is intended to be user-friendly,
multi-agency, cooperative, modular and threat agnostic platform
for biosurveillance . In BSVE, a web-based workbench presents
the analyst with applications (apps) developed by various DTRAfunded
researchers, which are deployed on-demand in the cloud
(e.g., Amazon Web Services). These apps aim to address emerging
needs and refine capabilities to enable early warning of chemical and
biological threats for multiple users across local, state, and federal
Soda Pop is an app developed by Pacific Northwest National
Laboratory (PNNL) to meet the current needs of the BSVE for
early warning and detection of disease outbreaks. Aimed for use by
a diverse set of analysts, the application is agnostic to data source
and spatial scale enabling it to be generalizable across many diseases
and locations. To achieve this, we placed a particular emphasis on
clustering and alerting of disease signals within Soda Pop without
strong prior assumptions on the nature of observed diseased counts.
Although designed to be agnostic to the data source, Soda Pop was
initially developed and tested on data summarizing Influenza-Like
Illness in military hospitals from collaboration with the Armed Forces
Health Surveillance Branch. Currently, the data incorporated also
includes the CDC’s National Notifiable Diseases Surveillance System
(NNDSS) tables  and the WHO’s Influenza A/B Influenza Data
(Flunet) . These data sources are now present in BSVE’s Postgres
data storage for direct access.
Soda Pop is designed to automate time-series tasks of data
summarization, exploration, clustering, alarming and forecasting.
Built as an R/Shiny application, Soda Pop is founded on the powerful
statistical tool R . Where applicable, Soda Pop facilitates nonparametric
seasonal decomposition of time-series; hierarchical
agglomerative clustering across reporting areas and between diseases
within reporting areas; and a variety of alarming techniques including
Exponential Weighted Moving Average alarms and Early Aberration
Soda Pop embeds these techniques within a user-interface designed
to enhance an analyst’s understanding of emerging trends in their data
and enables the inclusion of its graphical elements into their dossier
for further tracking and reporting. The ultimate goal of this software
is to facilitate the discovery of unknown disease signals along with
increasing the speed of detection of unusual patterns within these
Soda Pop organizes common statistical disease surveillance tasks
in a manner integrated with BSVE data source inputs and outputs.
The app analyzes time-series disease data and supports a robust set of
clustering and alarming routines that avoid strong assumptions on the
nature of observed disease counts. This attribute allows for flexibility
in the data source, spatial scale, and disease types making it useful to
a wide range of analysts
Soda Pop within the BSVE.
BSVE; Biosurveillance; R/Shiny; Clustering; Alarming
This work was supported by the Defense Threat Reduction Agency under
contract CB10082 with Pacific Northwest National Laboratory
1. Dasey, Timothy, et al. “Biosurveillance Ecosystem (BSVE) Workflow
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3. Centers for Disease Control and Prevention. “National Notifiable
Diseases Surveillance System (NNDSS).”
4. World Health Organization. “FluNet.” Global Influenza Surveillance
and Response System (GISRS).
5. R Core Team (2016). R: A language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna, Austria.
6. Salmon, Maëlle, et al. “Monitoring Count Time Series in R: Aberration
Detection in Public Health Surveillance.” Journal of Statistical
Software [Online], 70.10 (2016): 1 - 35.
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