A ZScore Based Multilevel Spatial Clustering Algorithm for the Detection of Disease Outbreaks

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A Z-Score Based Multi-level Spatial Clustering
Algorithm for the Detection of Disease Outbreaks
Department of Biomedical Informatics University
of Pittsburgh School of Medicine http//www.dbmi.
pitt.edu

• Jialan Que, Fu-Chiang Tsui, PhD and Jeremy
  Espino, MD

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Outline

• Introduction
• Temporal algorithms
• Spatial algorithms
• Methods
• Z-Score Based Multi-level Clustering Algorithm
  (ZMSC)
• Evaluation
• Results
• Discussion
• Future work

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Temporal Detection Algorithms

• Cumulative Sum (CuSUM)
• Recursive least squares
• Bayesian change-point detector
• Wavelet anomaly detector (WAD)

• Cumulative Sum (CuSUM)
• Recursive least squares
• Bayesian change-point detector
• Wavelet anomaly detector (WAD)

Siegrist D, Pavlin JA. BioALIRT biosurveillance
testbed evaluation. In Syndromic
surveillance reports from a national conference,
New York, NY. MMWR 2004 53(Suppl.) 152-8.
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Spatial Detection Algorithms

• Kulldorffs spatial scan statistic (KSSS)
• Bayesian spatial scan statistic (BSSS)

• Kulldorffs spatial scan statistic (KSSS)
• Bayesian spatial scan statistic (BSSS)

Martin K, A spatial scan statistic.
Communications in Statictics theory and
methods. 199726(6)1481-96. Neill DB, Moore
AW, Cooper, GF. A Bayesian spatial scan
statistic. Advances in neural information
processing systems. 2005181003-10.
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Kulldorffs Spatial Scan Statistic
scanning window
p0.001
Having an outbreak in a cluster (H1) vs. no
outbreaks (H0)

• Scan study region with circular or elliptic
  windows
• Compute likelihood ratios
• Locate a cluster with maximum likelihood ratio
• Compute p-value using randomization test

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Kulldorffs Spatial Scan Statistic

• Advantages
• Close to complete search
• Disadvantages and limitations
• Computationally intensive (O(n3),n is of unit
  areas)
• Only find clusters with circular/elliptic shapes

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Bayesian Spatial Scan Statistic
scanning window
p0.03
p0.2

• Divide a study region into an mm grid
• Scan the study region with rectangular windows
• Compute posterior probabilities, P(Having
  outbreak in cluster Data)
• Locate a cluster with highest posterior
  probability

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Bayesian Spatial Scan Statistic

• Advantages
• No significance testing needed
• Disadvantages and limitations
• Clusters with rectangular shapes
• Still nearly exhaustive search, time consuming
• (O(m4), mgrid size)

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MethodsZ-Score Based Multi-level Spatial
Clustering

• Only look at the subsets of the areas having high
  risks of outbreak occurrence
• Risk rate z-score

• Compute the z-score for each cluster by combining
  all the normalized time series of its inclusive
  areas.

• Compute the p-values of the cluster z-scores

• Output the most significant cluster

Top 20
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Evaluation

• Data
• Over-the-counter anti-diarrhea medication sales
  received from Pennsylvania (Jan.1,2004-Aug.31,2007
  )
• Semi-synthetic outbreaks (size K strength )
• Evaluation metrics
• ROC
• AMOC
• Running time
• Cluster positive predictive value (ppv)
• of the detected outbreak ZIP codes / output
  cluster size
• Cluster sensitivity
• of the detected outbreak ZIP codes /
  outbreak size

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ROC Curves
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AMOC Curves
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Areas under ROC and AMOC
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Running Time
The KSSS method was executed using SaTScan
(implemented in C), ZMSC, KSSS and BSSS were
implemented in JAVA and executed under JRE-1.5.
We configured the analysis of KSSS in SaTScan
as prospective, and the time window as 1-day
(time precision was on a daily basis), which
analyzes only the most current day to make it
comparable to ZMSC. The number of Monte-Carlo
replications was set to be 999.
For BSSS, we calculated the expected values
using a 28-day moving average. The grid was
defined as 32 by 32.
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Cluster Sensitivities
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Cluster Sensitivities
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Cluster PPVs
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Discussion

• Advantages
• ZMSC can be applied to larger scale of study
  areas because it runs fast
• ZMSC tends to identify more compact clusters
  which may help field epidemiologists
• ZMSC is not limited to any artificial cluster
  shapes
• Disadvantages
• ZMSC detected clusters later than BSSS at low
  false alarm rates

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Limitations

• Adjacency threshold ( ) was set to 0
• Injected outbreaks were artificial and simplified

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Future Work

• Different outbreak models
• BARD outbreaks
• Other outbreak models
• Other data types
• ED data
• Water quality data, etc..

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Acknowledgments

• NSFIIS-0325581
• CDC-5R01PH00026-02
• NLM-5R21LM008278-03
• PADOH-ME-01737
• AFRL-F30602-01-2-0550

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Cluster Z-Score
Normalize and average
WAD