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ISDS 2012 Conference Abstracts

A Framework for Detecting and Classifying Outbreaks of
Gastrointestinal Disease
Kathryn Morrison*, Katia Charland, Anya Okhmatovskaia and David Buckeridge

Epidemiology & Biostatistics, McGill University, Montreal, QC, Canada

Objective
To develop a methodological framework for detecting and classi-

fying outbreaks of gastrointestinal disease on the island of Montreal,
with the goal of improving early outbreak detection using simulated
surveillance data.

Introduction
Outbreaks of waterborne gastrointestinal disease occur routinely in

North America, resulting in considerable morbidity, mortality, and
cost (Hrudey, Payment et al. 2003). Outbreak detection methods gen-
erally attempt to identify anomalies in time, but do not identify the
type or source of an outbreak. We seek to develop a framework for
both detection and classification of outbreaks using information in
both space and time. Outbreak detection can be improved by using
simulated outbreak data to build, validate, and evaluate models that
aim to improve accuracy and timeliness of outbreak detection.

Methods
To generate outbreak data, we used a previously validated mi-

crosimulation model depicting waterborne outbreaks of gastroin-
testinal disease (Okhmatovskaia et al. 2010). The model is
parameterized based on outbreak characteristics such as concentration
and duration of contamination, and calibrated to produce realistic out-
break data (e.g., emergency department visits from GI-illness, labo-
ratory reporting to public health) in space and time. We are interested
in identifying unique space-time signatures in the data that would
allow not only detection, but also classification based on outbreak
type. For example, to be able to detect and classify an outbreak as
due to a water plant failure versus an food-borne illness based on
unique space-time patterns, even though symptoms and temporal out-
break patterns may be similar. For the detection step, we use a hidden
Markov model (HMM) that accounts for spatial information through
a spatially correlated random effect with an exponential decay.
HMMs have been used previously in disease mapping (Green 2002)
but not widely in space-time disease outbreak detection. For the clas-
sification step, we use a supervised clustering algorithm to classify
the outbreak by source (e.g., water plant location) and type (e.g., dis-
ease).

Results
Preliminary results for the detection step show that the HMM can

distinguish accurately between regions in an outbreak state versus
those in a normal state at each time period. Ongoing work for the de-

tection step includes further evaluation of the HMM accuracy as a
function of outbreak characteristics. For the classification step, we
are evaluating the suitability of different supervised clustering algo-
rithms for identifying the type of outbreak from the HMM results.

Conclusions
If outbreaks are detected rapidly, interventions, such as boil-water

advisories, are available to quickly and effectively limit the human
and economic impacts. Traditional public health surveillance sys-
tems, however, frequently fail to detect waterborne disease outbreaks.
Every disease outbreak has unique characteristics; simulation is the
best method to estimate the capacity of syndromic surveillance to
more efficiently detect different types of enteric disease outbreaks
based on a variety of parameters. Outbreak detection can be improved
with advances in data availability, such as syndromic surveillance
data that will increase timeliness of detection, and space-time infor-
mation to allow for simultaneous detection and classification of out-
breaks by important characteristics (type of outbreak, source of
outbreak).

Keywords
Syndromic surveillance; Disease outbreak detection; Waterborne dis-
ease

Acknowledgments

This research is supported by the Canadian Institutes of Health Research
(CIHR).

References

Green PJ. (2002). “Hidden Markov Models and Disease Mapping.” Jour-
nal of the American Statistical Association 97(460): 1055-1070.

Hrudey S., P. Payment, et al. (2003). “A fatal waterborne disease epidemic
in Walkerton, Ontario: comparison with other waterborne outbreaks
in the developed world.” Water science & technology 47(3): 7-14.

Okhmatovskaia A, Verma AD, Barbeau B, Carriere A, Pasquet R, Buck-
eridge DL. (2010). A Simulation Model of Waterborne Gastro-In-
testinal Disease Outbreaks: Description and Initial Evaluation. AMIA
Annual Symposium.

*Kathryn Morrison
E-mail: kt.morrison@mail.mcgill.ca

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 5(1):e115, 2013