Bayesian Biosurveillance Using Multiple Data Streams

2004 University of Pittsburgh

Bayesian Biosurveillance Using Multiple Data Streams

Greg Cooper, Weng-Keen Wong, Denver Dash*, John Levander, John Dowling, Bill Hogan, Mike Wagner

RODS Laboratory, University of Pittsburgh

* Intel Research, Santa Clara


Outline

1. Introduction2. Model3. Inference4. Conclusions


Over-the-Counter (OTC) Data Being Collected by the National Retail Data Monitor (NRDM)

19,000 stores

50% market share

nationally

>70% market share in large cities


ED Chief Complaint Data Being Collected by

RODS

Date / Time Admitted

Age Gender Home Zip Work Zip Chief Complaint

Nov 1, 2004 3:02 20-30 Male 15213 Shortness of breath

Nov 1, 2004 3:09 70-80 Female 15132 15213 Fever

: : : : : :

Chief Complaint ED Records for Allegheny County


Objective

Using the ED and OTC data streams, detect a disease outbreak in a given region as quickly and accurately as possible


Our Approach

• A detection algorithm that models each individual in the population

• Combines ED and OTC data streams• The current prototype focuses on

detecting an outdoor aerosolized release of an anthrax-like agent in Allegheny county

Population-wide ANomaly Detection and Assessment (PANDA)


PANDA

Visit of Person to ED

Location of Anthrax Release

Anthrax Infection of Person

Bayesian Network: A graphical model representing the joint probability distribution of a set of random variables

Uses a causal Bayesian network

Home Location of Person


PANDA

The arrows convey conditional independence relationships among the variables. They also represent causal relationships.

Uses a causal Bayesian network

Visit of Person to ED

Location of Anthrax Release

Anthrax Infection of Person

Home Location of Person


Outline



A Schematic of the Generic PANDA Model for Non-

Contagious Diseases

Population Risk Factors

Population Disease Exposure (PDE)

Person Model

Population-WideEvidence

Person Model Person Model Person Model


A Special Case of the Generic Model

Time of Release

Person Model

Anthrax Release

Location of Release

Person ModelPerson ModelPerson Model

OTC Sales for Region

Each person in the population is represented as a subnetwork in the overall model


Location of Release

Time Of Release

Anthrax Infection

Home Zip

Respiratory from Anthrax

Other ED Disease

Gender

Age Decile

Respiratory CCFrom Other

RespiratoryCC

Respiratory CCWhen Admitted

ED Admitfrom Anthrax

ED Admit from Other

ED Acute Respiratory

Infection

Acute RespiratoryInfection

Daily OTC Purchase

Last 3 Days OTCPurchase

Non-ED AcuteRespiratory Infection

ED Admission

The Person Model



Why Use a Population-Based Approach?

1. Representational power• Spatial, temporal, demographic, and symptom

knowledge of potential diseases can be coherently represented in a single model

• Spatial, temporal, demographic, and symptom evidence can be combined to derive a posterior probability of a disease outbreak

2. Representational flexibilityNew types of knowledge and evidence can be readily incorporated into the model

Hypothesis: A population-based approach will achieve better detection performance than non-population-based approaches.


Location of Release

Time Of Release

Anthrax Infection

Home Zip


Other ED Disease

Gender

Age Decile


RespiratoryCC



ED Admit from Other


Infection


Daily OTC Purchase



ED Admission

The Person Model


Location of Release

Time Of Release

Anthrax Infection

Home Zip


Other ED Disease

Gender

Age Decile


RespiratoryCC



ED Admit from Other

ED AcuteRespiratory

Infection


Daily OTC Purchase



ED Admission

The Person Model

AgeDecile

Gender Home Zip

Respiratory Chief Comp.

DateAdmitted

20-30 Male 15213 Yes Today

Equivalence Class Example:


Outline



Inference

Time of Release

Person Model

Anthrax Release

Location of Release

Person ModelPerson ModelPerson Model

Derive P (Anthrax Release = true | OTC Sales Data & ED Data)


gfc

You need to make the point that each person model has variables that indicate whether the person visited the ED, and if so, then other variables contain information about that visit, such as the time and the chief complaint.

gfc

Say this is the model that is the focus of this talk.


InferenceAR = Anthrax Release ED = ED Data

PDE = Population Disease Exposure

OTC = OTC Counts

P ( OTC, ED | PDE ) =

P ( OTC | ED, PDE ) P ( ED | PDE )

Contribution of ED DataContribution of OTC Counts

Key Term in Deriving P ( AR | OTC, ED ) :

Details in: Cooper GF, Dash DH, Levander J, Wong W-K, Hogan W, Wagner M. Bayesian Biosurveillance of Disease Outbreaks. In: Proceedings of the

Conference on Uncertainty in Artificial Intelligence, 2004.


InferenceAR = Anthrax Release ED = ED Data

PDE = Population Disease Exposure

OTC = OTC Counts



The focus of the remainder of this talk

Key Term in Deriving P ( AR | OTC, ED ) :


Location of Release

Time Of Release

Anthrax Infection

Home Zip


Other ED Disease

Gender

Age Decile


RespiratoryCC



ED Admit from Other


Infection


Daily OTC Purchase



ED Admission

The Person Model



Incorporating the Counts of OTC Purchases

Eq Class1 Zip1OTC count

Zip1OTC count

Eq Classs2 Zip1OTC count

Person1 Zip1OTC count




Approximate binomial

distribution with a normal distribution


The PANDA OTC Model

P (OTC sales = X | ED, PDE ) ),;(Normal 2i

ii

iE

EE

EX

Recall that:




ExampleAgeDecile

Gender

Home Zip


DateAdmitted

50-60 Male 15213

Yes Today

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0 50 100 150 200 250 300 350

Equivalence Class 1 ~ Normal(100,100)


ExampleAgeDecile

Gender

Home Zip


DateAdmitted

50-60 Male 15213

Yes Today


AgeDecile

Gender

Home Zip


DateAdmitted

50-60 Female 15213

Yes Today


0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0 50 100 150 200 250 300 350


0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0 50 100 150 200 250 300 350

ExampleAgeDecile

Gender

Home Zip


DateAdmitted

50-60 Male 15213

Yes Today


AgeDecile

Gender

Home Zip


DateAdmitted

50-60 Female 15213

Yes Today


If these were the only 2 Equivalence Classes in the County then

County Cough & Cold OTC ~ Normal(100+150,100+225)


0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0 50 100 150 200 250 300 350

ExampleNow suppose 260 units are sold in the county

P( OTC Sales = 260 | ED Data, PDE ) =

Normal( 260; 250, 325 ) = 0.001231

260


Inference TimingMachine: P4 3 Gigahertz, 2 GB RAM

Initialization Time (seconds)

Each hour of data (seconds)

ED model 55 5

ED and OTC model

229 5


A Current Limitation

• Problem: Currently we assume unrealistically that a person only makes OTC purchases in his or her home zip code

• Approach 1: Aggregate OTC-counts (e.g., at the county level)

• Approach 2: For each home zip code, model the distribution of zip codes where OTC purchases are made


Outline



Challenges in Population-Wide Modeling Include …

• Obtaining good parameter estimates to use in modeling (e.g., the probability of an OTC cough medication purchase given an acute respiratory illness)

• Modeling time and space in a way that is both useful and computationally tractable

• Modeling contagious diseases


Conclusions• PANDA is a multivariate algorithm that

can combine multiple data streams• Modeling each individual in the

population is computationally feasible (so far)

• An evaluation of the PANDA approach to modeling multiple data streams is in progress using semi-synthetic test data

gfc

We don't really show this experimentaly here. Hopefully by the time you give this talk, we will have such data.


Thank you

Current funding:National Science Foundation

Department of Homeland Security

Earlier funding:DARPA

http://www.cbmi.pitt.edu/panda/

[email protected]



The PANDA OTC ModelModel the OTC purchases for each Equivalence Class Ei as a binomial Distribution.

Ei ~ Binomial(NEi ,PEi

)



Ei ~ Binomial(NEi ,PEi

)

Number of people in Equivalence Class Ei

Probability of an OTC cough medication purchase during the previous 3 days by each person in Equivalence Class Ei



Approximate the binomial distribution as a normal distribution.

Ei ~ Binominal(NEi ,PEi

)

Normal(Ei ,2

Ei)



Approximate the binomial distribution as a normal distribution.

Ei ~ Binominal(NEi ,PEi

)

Normal(Ei ,2

Ei)Ei

= NEi × PEi

2Ei = NEi

× PEi× (1 - PEi

)


Computational Cost of a Population-Wide Approach?

~1.4 million people in Allegheny County, Pennsylvania


Equivalence Classes

The ~1.4M people in the modeled population can be partitioned into approximately 24,240 equivalence classes

Documents

Bayesian Biosurveillance Using Multiple Data Streams