Objective

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) [1]. 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

decisions.

Introduction

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 [2]. 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

agencies.

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.

Methods

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 [3] and the WHO’s Influenza A/B Influenza Data

(Flunet) [4]. 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 [5]. 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

Detection [6].

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

signals.

Conclusions

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.

Keywords

BSVE; Biosurveillance; R/Shiny; Clustering; Alarming

Acknowledgments

This work was supported by the Defense Threat Reduction Agency under

contract CB10082 with Pacific Northwest National Laboratory

References

1. Dasey, Timothy, et al. “Biosurveillance Ecosystem (BSVE) Workflow

Analysis.” Online journal of public health informatics 5.1 (2013).

2. http://www.defense.gov/News/Article/Article/681832/dtra-scientistsdevelop-

cloud-based-biosurveillance-ecosystem. Accessed 9/6/2016.

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.