Layout 1


ISDS Annual Conference Proceedings 2012. 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 2012 Conference Abstracts

The Surveillance Window – Contextualizing Data
Streams
Kirsten McCabe*, Lauren Castro, Mac Brown, William Daniel, Eric Nick Generous, Kristen
Margevicius and Alina Deshpande

Los Alamos National Laboratory, Los Alamos, NM, USA

Objective
The goal of this project is the evaluation of data stream utility in

integrated, global disease surveillance. This effort is part of a larger
project with the goal of developing tools to provide decision-makers
with timely information to predict, prepare for, and mitigate the
spread of disease.

Introduction
Los Alamos National Laboratory has been funded by the Defense

Threat Reduction Agency to determine the relevance of data streams
for an integrated global biosurveillance system. We used a novel
method of evaluating the effectiveness of data streams called the “sur-
veillance window”. The concept of the surveillance window is de-
fined as the brief period of time when information gathered can be
used to assist decision makers in effectively responding to an im-
pending outbreak. We used a stepwise approach to defining disease
specific surveillance windows;

1. Timeline generation through historical perspectives and epi-
demiological simulations. 

2. Identifying the surveillance windows between changes in “epi-
demiological state” of an outbreak. 

3. Data streams that are used or could have been used due to their
availability during the generated timeline are identified. If these data
streams fall within a surveillance window, and provide both action-
able and non-actionable information, they are deemed to have utility.

Methods
Figure 1 shows the overall approach to using this method for eval-

uating data stream types. Our first step was identifying a list of pri-
ority diseases to build surveillance windows for and our primary
sources were our SME panel, CDC priorities, as well as DOD prior-
ities. We also conducted a literature review to support our selection
of diseases. We ensured that there was representation of human, an-
imal and plant diseases and there was enough data available for se-
lected outbreaks to facilitate evaluation of all data stream types
identified. We then selected representative outbreaks for diseases to
generate a timeline for defining surveillance windows. Surveillance
windows were then defined (based on four specific biosurveillance
goals developed by LANL) and information for applicable data
streams was collected for the duration of the outbreak. A data stream
was deemed useful if it was determined to be available within the de-
fined surveillance window. In addition, evaluation of the ideal use
case of the data streams was performed. In essence, if used more ef-
fectively could this data stream provide greater support to under-
standing, detection, warning or management of disease outbreaks or
event situations?

Results
Results presented in this abstract are from retrospective analyses

of historical outbreaks selected as being representative of FMD,

Ebola, Influenza and E.coli. Graphs indicating case counts and geo-
graphical spread were combined and a timeline was created to deter-
mine the length of time between changes in “epidemiological state”
that defined various surveillance windows. This timeline was then
populated with durations when data streams were used during the
outbreak. Results showed varying surveillance windows times are
dependent on disease characteristics. In turn, epidemiology of the dis-
ease affected the occurrence of data streams on the timeline.

Conclusions
Surveillance window based evaluation of data streams during dis-

ease outbreaks helped identify data streams that are of significance for
developing an effective biosurveillance system. Some data streams
were identified to have high utility for early detection and early warn-
ing regardless of disease, while others were more disease and opera-
tions specific. This work also identified data streams currently not in
use that could be exploited for faster outbreak detection. Key useful
data streams that are underlying to all disease categories and thus im-
portant for integration into global biosurveillance programs will be
presented here.

Figure 1: Overall approach to surveillance window based evaluation

Keywords
Surveilliance Windows; Data streams; Biosurveilliance

Acknowledgments

This project is supported by the Chemical and Biological Technologies
Directorate Joint Science and Technology Office (JSTO), Defense Threat
Reduction Agency (DTRA).

*Kirsten McCabe
E-mail: kjmccab@lanl.gov

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(1):e113, 2013