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

Use of Control Bar Matrix for Outbreak Detection in
Syndromic Surveillance System
Tao Tao1, Qi Zhao1, Huijian Cheng2, Lars Palm3, Xin Lu4, 5, Hui Yuan2, Xiaoxiao Song1 and
Biao Xu*1

1School of Public Health, Fudan University, Shanghai, China; 2Jiangxi Provincial Center for Disease Control and Prevention,
Nanchang, China; 3Future Position X, Gävle, Sweden; 4Division of Global Health (IHCAR), Department of Public Health Sciences,
Karolinska Institutet, Stockholm, Sweden; 5College of Information Systems and Management, National University of Defense
Technology, Changsha, China

Objective
To develop and test the method of incorporating different control

bars for outbreak detection in syndromic surveillance system.

Introduction
Aberration detection methods are essential for analyzing and in-

terpreting large quantity of nonspecific real-time data collected in
syndromic surveillance system. However, the challenge lies in dis-
tinguishing true outbreak signals from a large amount of false alarm
(1). The joint use of surveillance algorithms might be helpful to guide
the decision making towards uncertain warning signals.

Methods
A syndromic surveillance project (ISSC) has been implemented in

rural Jiangxi Province of China since August 2011. Doctors in the
healthcare surveillance units of ISSC used an internet-based elec-
tronic system to collect information of daily outpatients, which in-
cluded 10 infectious related symptoms. From ISSC database, we
extracted data of fever patients reported from one township hospital
in GZ town between August 1st and December 31st, 2011 to conduct
an exploratory study. Six different control bar algorithms, which in-
cluded Shewart, Moving Average (MA), Exponentially Weighted
Moving Average (EWMA) and EARS’ C1, C2, C3, were prospec-
tively run among historical time series of daily fever count to simu-
late a real-time outbreak detection. Each control bar used 7 days’
moving baseline with a lag of 2 days [the baseline for predicting
Day(t) starts from Day(t-9) to Day(t-3), C1 method used a lag of zero
day]. We set the threshold of µ+2! for Shewart and MA, and 2.1 for
EWMA C1, C2 and C3. An alarm was triggered when the observed
data exceeded threshold, and the detailed information of each patient
were checked for signal verification. Microsoft Excel 2007 was used
to calculate the simulation results.

Results
During the 5 months, GZ township hospital reported 514 outpa-

tients with fever symptom, with an average of 3.4 per day. All con-
trol bars were simultaneously operated among daily counts of fever
cases. Of the 153 days on surveillance, 29 triggered alarms by at least
one of the control bars. Nine days triggered alarms from >= 3 control
bars while on one day (12/30) all 6 algorithms raised alarms. Figure
1 shows the date, fever count, algorithm and warning level (color) of
each alarm, which we called a control bar matrix. It can be seen that
C3 and EWMA present a higher sensitiveness towards tiny data
change whereas C1, C2 and MA focus on large increase of data. C3
also had a memory effect on recent alarms. No infectious disease epi-
demic or outbreak event was confirmed within the signals. Most fever
patients on the nine high-warning days (red and purple) were diag-
nosed as upper level respiratory infection. However, we discovered
that the sharp increase of fever cases on 12/30 was attributed to 5 du-
plicate records mistakenly input by the staff in GZ hospital.

Conclusions
By combining control bars with different characteristics, the ma-

trix has potential ability to improve the specificity of detection while
maintaining a certain degree of sensitivity. With alarms categorized
into hierarchical warning levels, public health staffs can decide which
alarm to investigate according to the required sensitivity of surveil-
lance system and their own capacity of signal verification. Though we
did not find any outbreak event in the study, the possibility of local-
ized influenza epidemic on high-warning days cannot be wiped out,
and the matrix’s ability to detect abnormal data change was appar-
ent. The proper combination, baseline and threshold of control bars
will be further explored in the real-time surveillance situation of
ISSC.

Figure 1: Detailed information of alarm signals generated by control bar ma-
trix (No-alarm days were omitted).

Keywords
Syndromic surveillance; matrix; control bar; signal

Acknowledgments

This study was funded by [European Union’s] [European Atomic Energy
Community’s] Seventh Framework Programme ([FP7/2007-2013]
[FP7/2007-2011]) under grant agreement no. [241900].

References

1. Fearnley L: Signals come and go: syndromic surveillance and styles
of biosecurity. Environment and Planning A 2008, 40(7):1615-1632.

*Biao Xu
E-mail: bxu@shmu.edu.cn

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(1):e111, 2013