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ISDS 2012 Conference Abstracts

A Bayesian Approach to Characterize Hong Kong
Influenza Surveillance Systems
Ying Zhang*, Ali Arab and Michael A. Stoto

Georgetown University, Washington, DC, USA

Objective
Our goal is to develop a statistical model for characterizing in-

fluenza surveillance systems that will be helpful in interpreting mul-
tiple streams of influenza surveillance data in future outbreaks.

Introduction
Syndromic surveillance has been widely used in influenza sur-

veillance worldwide. However, despite the potential benefits created
by the large volume of data, biases due to the changes in healthcare
seeking behavior and physicians’ reporting behavior, as well as the
background noise caused by seasonal flu epidemics, contribute to the
complexity of the surveillance system and may limit its utility as a
tool for early detection [1,2]. Since most current analysis methods
are developed for outbreak detection, there are few tools to charac-
terize influenza surveillance data for situational awareness purposes
in a quantitative manner.

Hong Kong Centre for Health Protection (CHP) has a compre-
hensive influenza surveillance system based on healthcare providers,
laboratories, schools, daycare centers and residential care homes for
the elderly. Hong Kong usually experiences a summer peak in July
and August [3], which potentially doubles the data volume and con-
stitutes a natural experiment to assess the effect of school-age chil-
dren in the influenza transmission dynamics. The richness of the
available data and the unique epidemiological characteristics make
Hong Kong an ideal study object to develop and evaluate our model.

Methods
We have constructed a Bayesian statistical model for influenza sur-

veillance data by parameterizing factors that describe disease trans-
mission, behavior patterns in health care seeking and provision, and
biases and errors embedded in the reporting process (Figure 1). The
prior distributions are selected for each of the parameters to reflect
knowledge of influenza epidemiology and the likely biases in each
data system. Using the Markov Chain Monte-Carlo (MCMC) method
in OpenBUGS, a posterior distribution can be generated for every pa-
rameter to characterize each data stream. The ratios of specific pairs
of data streams are assessed in order to identify patterns in the change
of ratios at different stage of the flu season.

Results
Preliminary results, as shown in Figure 2, incorporate confirmed

influenza infection (solid line), influenza-like illness (double solid
line), fever cases (dashed line), and Google search index (round
dashed line). Although most of these data series track together, dif-
ferences among them suggest reporting bias related to public aware-
ness, which will be addressed in the statistical modeling.

Conclusions
The posterior distribution for parameters and ratios between indi-

vidual data streams can be used to characterize influenza surveillance
systems in terms of tendency in peak early or late, or to over or under
represent actual influenza cases. To better interpret syndromic sur-
veillance data for situational awareness purposes, behavioral data re-
lated to healthcare resource utilization, such as the percentage of

intended GP visit among people with ILI, need to be collected together
with the flu activity surveillance.

Conceptual model for influenza surveillance statistical model 
Blue circles: unobservable true value; white boxes: observation; orange
boxes: factors

Hong Kong flu activity in 2009 pH1N1 outbreak

Keywords
situational awareness; modeling; epidemiology; influenza surveil-
lance; Bayesian

Acknowledgments

CDC, Hong Kong University
Center for Health Protection, Hong Kong SAR

References

1.Zhang, Y., May, L., & Stoto, M. A. (2011). Evaluating syndromic sur-
veillance systems at institutions of higher education (IHEs): A retro-
spective analysis of the 2009 H1N1 influenza pandemic at two
universities. BMC Public Health, 11, 591.

2. Stoto MA (2012) The Effectiveness of U.S. Public Health Surveillance
Systems for Situational Awareness during the 2009 H1N1 Pandemic:
A Retrospective Analysis. PLoS ONE 7(8): e40984.

3. Chan, P. K. S., Mok, H. Y., Lee, T. C., Chu, I. M. T., Lam, W., & Sung,
J. J. Y. (2009). Seasonal influenza activity in Hong Kong and its as-
sociation with meteorological variations. Journal of Medical Virol-
ogy, 81(10), 1797-1806.

*Ying Zhang
E-mail: yz62@georgetown.edu

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(1):e172, 2013