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<p>*What is SSS?</p>
<h2>What is SSS?</h2>
<p>Our Spatial Scan Statistics (SSS) system enables the automatic detection of
anomalous spatial clusters. Given a massive set of spatial or space-time count
data (e.g. the number of reported disease cases in each zip code on each day),
SSS searches through the dataset to find spatial regions with higher than
expected counts. This process has two steps: SSS first infers the expected count
for each spatial location using time series analysis, then uses an
expectation-based spatial scan statistic approach to find spatial regions where
the counts are significantly higher than expected. Randomization testing is
performed to compute the statistical significance of each discovered cluster,
enabling us to distinguish true clusters from those due to chance.</p>
<p>*What's special about SSS?</p>
<h2>What's special about SSS?</h2>
<p>Spatial scan statistics are a powerful statistical test for detection of
significant spatial clusters. Unlike many other cluster detection methods, they
can be used both to determine whether any statistically significant clusters
exist and to precisely pinpoint the size and location of clusters. Because the
statistic scans over a huge number of regions of variable shape and size (and
each region can contain between one and many locations), it has high power to
detect clusters regardless of whether they affect a small or large spatial
area. Our statistical test correctly adjusts for the multiplicity of tests
performed, enabling us to ensure a low false positive rate while maintaining
high power to detect any significant clusters that do occur.</p>
<p>Our new implementation of spatial scan statistics has several advantages over
standard spatial scan approaches (e.g. SaTScan). First, we use novel spatial
statistical methods to adjust for spatial and temporal variation in the baseline
counts, allowing us to account correctly for day of week, seasonality, and other
trends. This improves detection power, allowing more timely detection of
emerging clusters with fewer false positives. Second, we have developed a new
computational method, the “fast spatial scan.” This fast multi-resolution search
approach allows us to compute the spatial scan hundreds to thousands of times
faster than the standard approach. Thus we can obtain results in minutes rather
than hours or days, even for massive datasets containing millions of records.
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<p>*What type of problem can be solved by SSS?</p>
<h2>What type of problem can be solved by SSS?</h2>
<p>SSS can find anomalous spatial clusters in spatial or space-time data
sets. In particular, given a large set of spatial locations (e.g. zip codes),
where each location has an associated time series of counts, SSS can detect any
spatial regions where the most recent counts are significantly higher than
expected, given the historical baseline data. For example, if we are given the
number of emergency department visits in each zip code on each day, SSS can find
areas where the recent number of cases is abnormally high, which may be
indicative of an emerging outbreak of disease.</p>
<p>*SSS in action</p>
<h2>SSS in action</h2>
<p><Retrospective analysis of Walkerton outbreak></p>
<p><strong><em>Retrospective analysis of Walkerton outbreak</em></strong></p>
<p>In May 2000, an outbreak of gastroenteritis in Walkerton, Ontario resulted
from contamination of the water supply with <em>E. coli</em> bacteria. Over 2000
individuals were affected by severe gastrointestinal symptoms, including 65
hospitalizations and 6 deaths. We used the SSS software to perform a
retrospective analysis of emergency department visits in Walkerton and the
surrounding Grey-Bruce region of Ontario between 1999 and 2001. At a rate of
only two false positives per year, SSS was able to detect the outbreak on May
19, 2000, two days before the first public health response and one day before
the other surveillance methods tested. </p>
<p><Nationwide monitoring of over-the-counter drug sales></p>
<p><strong><em>Nationwide monitoring of over-the-counter drug
sales</em></strong></p>
<p>We are currently using SSS to perform daily monitoring of over-the-counter
medication sales from the National Retail Data Monitor (NRDM). Our system
receives daily counts of the number of units sold in 18 different product
categories (cough remedies, nasal decongestants, etc.) from over 20,000 retail
stores and pharmacies nationwide. It then uses our new spatial cluster detection
methods to find areas where the sales are significantly higher than expected,
and makes these results available to state and local public health officials via
a web-based graphical interface.</p>
<p>*Links to representative publications</p>
<h2>Representative Publications</h2>
<p>1. D.B. Neill and A.W. Moore. Methods for detecting spatial and
spatio-temporal clusters. In M. Wagner, A. Moore, and R. Aryel, eds., Handbook
of Biosurveillance, 2006.</p>
<p>2. D.B. Neill and A.W. Moore. Efficient scan statistic computations. In A.
Lawson and K. Kleinman, eds., Spatial and Syndromic Surveillance for Public
Health, 2005.</p>
<p>3. D.B. Neill, A.W. Moore, and G.F. Cooper. A Bayesian spatial scan
statistic. In Advances in Neural Information Processing Systems 18, 2006, in
press.</p>
<p>4. D.B. Neill, A.W. Moore, and G.F. Cooper. A Bayesian scan statistic for
spatial cluster detection. Proceedings of the National Syndromic Surveillance
Conference, 2005. Received “Best Research Presentation” award.</p>
<p>5. D.B. Neill, A.W. Moore, M.R. Sabhnani, and K. Daniel. Detection of
emerging space-time clusters. Proceedings of the 11th ACM SIGKDD Conference on
Knowledge Discovery and Data Mining}, 218-227, 2005.</p>
<p>6. D.B. Neill and A.W. Moore. Anomalous spatial cluster
detection. Proceedings of the KDD 2005 Workshop on Data Mining Methods for
Anomaly Detection, 2005.</p>
<p>7. M.R. Sabhnani, D.B. Neill, A.W. Moore, F.-C. Tsui, M.M. Wagner, and J.U.
Espino. Detecting anomalous patterns in pharmacy retail data. Proceedings of the
KDD Workshop on Data Mining Methods for Anomaly Detection, 2005.</p>
<p>8. D.B. Neill, A.W. Moore, F. Pereira, and T. Mitchell. Detecting significant
multidimensional spatial clusters. In L.K. Saul, et al., eds., Advances in
Neural Information Processing Systems 17, 969-976, 2005.</p>
<p>9. D.B. Neill and A.W. Moore. Rapid detection of significant spatial
clusters. Proceedings of the 10th ACM SIGKDD Conference on Knowledge Discovery
and Data Mining, 256-265, 2004.</p>
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