Transcript Spatiotemporal Cluster Detection in ESSENCE Biosurveillance Systems

Spatiotemporal Cluster Detection
in ESSENCE Biosurveillance
Systems
Panelist: Howard Burkom
National Security Technology Department,
John Hopkins University Applied Physics Laboratory
DIMACS Working Group
Workshop on Analytical Methods for
Surveillance of Multidimensional Data Streams
Rutgers University, Piscataway NJ
February 19, 2004
Problem/Data Context of
ESSENCE Surveillance Systems
Physician
Office Visits
Hosp ER
Admissions
Absentee
Rates
Sales of OTC
Remedies
Normalization
Analysis
Fusion
Counts/Clusters of
Statistical Significance
Epidemiological Significance
Who?
What?
Where?
When?
Applying Statistical Process
Control to Multiple Data Streams
• Multiplicity from intertwined effects: multiple
data sources, regions, strata (syndrome
groups, product groups)
• Multiple univariate methods
– Critical issue: use individual detector outputs
without getting overwhelmed by multiple testing
– Low power for anomalies spread over inputs
• Multivariate methods
– Critical issue: need modifications to reduce
alerts due to irrelevant changes in data
relationships
– Need to retain power in individual source data
Significance Assessment: Multiple
Univariate Alerting Algorithms
• Bonferroni bound: replace a by a/N
– Alert based on individual outputs (conservative)
• Edgington’s “consensus” method (1972)
– Combined prob from alg. comb .of N individual p-values
– Z-score approximation:
( mean(p-values) – 0.5 ) / ( 0.2887 / √N )
• Bayes Belief Net
– Originated effort to add sensor data, intelligence info,…
– Recently applied to separate algorithm outputs
– Can weight each type of information based on training
data and/or intuition
– Configurable to soften thresholds for evidence accrual
Multivariate Alerting Strategies
•
Variants of Hotelling’s T2
–
•
MEWMA (Lowry), MCUSUM (Crosier, Pignatiello/Runger)
–
–
•
m = vector mean est. from current baseline
S = est. of covariance matrix calc. from baseline
X = multivariate (filtered?) data from test interval
T2 statistic: (X- m) S-1(X- m) (Ye et al, 2002)
“Neighbor-regression” preconditioning strategy of Hawkins;
removal of covariance effects
Numerous strategies, adaptations to Poisson data
But which is appropriate for multivariate syndromic data
streams?
Can EWMA/Shewhart (or CUSUM/Shewhart) encompass
both point-source “bioweapon” epicurve and seasonal
endemic=>epidemic outbreak?
Detection Challenge:
faint rise in all 3 data sets
Detection Challenge:
faint rise in all 3 data sets
Lowry’s MEWMA:
Day 4 alert at each FA rate
Scan Statistics for Biosurveillance
Scarlet Fever Outbreak Study
Analysis of
Claims Data
in National
Capital Area
ICD9 codes
for scarlet
fever:
034
034.1
10 cases, 5 days
p = 0.013
15 cases, 12 days
p = 0.002
11 cases, 7 days
p < 0.001
Surveillance combining outpatient visits,
OTC anti-flu sales, school absenteeism
Practical Issues in Spatiotemporal
Monitoring and Evaluation
• Control needed for mismatched scales & variances
among data sources
• To retain power in indiv. sources, gain combined sensitivity
• Difficult to assess delays, relative scales of effects
among separate sources, in both background & signal
• Simulation much harder to validate
• If distance matrix is used, it should reflect proximity
according to the epidemiological case definition:
• Modifications to reflect plausible demographic behaviors
• The importance of significance testing grows with the
number of sources, especially for subregions where
expected counts are low
• More sources => more small spurious clusters
Finding Clusters with
Multiple Data Sources
• For candidate cluster J1, the Kulldorff likelihood ratio is:
LR(J1) ≡ (O1/E1)O1 * ((N-O1) / (N-E1)) (N-O1)
where O1 = number of cases inside J1,
E1 = number of cases outside J1,
N = total case count
• Extension by treating multiple sources as covariates:
O1 = SO1k, E1 = SE1k, N = SNk, for sources k=1,…,K
– “adjusted method”: problem of adding sources with
mismatched scales, variances
• Alternate multisource approach: “stratified” scan statistic
S log( LR(J1k) ), k=1,…,K
– reduces chances for a noisy source to overwhelm others
– can cost power to detect faint signal spread over sources
Prob. Signal-Based Significant Cluster
FROC Performance Assessment
Adjusted vs Stratified Multisource
Scan Statistics
Prob. Random Background Significant Cluster