Decision Theoretic Analysis of Improving Epidemic Detection

Izadi, M.

Buckeridge, D.

AMIA 2007,Symposium Proceedings 2007

Overview Objective: Improve the accuracy of current

detection methods

Observation: Quantifying the potential costs and effects of intervention can be used to optimize the alarm function

Method: Use Partially Observable Markov Decision Processes on the outbreak detection method

The Surveillance Cycle*

Event Report

s

Individual Event Definitions

Population Pattern

Definitions

Event Detection Algorithm

Pattern Report

Population Under Surveillance

Intervention Decision

Intervention

GuidelinesPublic Health Action

Data Describing Population

Pattern Detection Algorithm

1. Identifying individual cases

2. Detecting population patterns

3. Conveying information for action

(Buckeridge DL & Cadieux G, 2007)

Usual Detection Methods*

Methods are non-specific – they look for anything unusual in the data

Design a baseline.

Define an aberration when some statistics are more than expected values by the baseline.

Detection Method Example*

Define a threshold for the number of Emergency Department visits per day.

Signal an alarm when the number of ED visits per day exceeds the threshold.

Number of ED Visits per Day

0

10

20

30

40

50

1 10 19 28 37 46 55 64 73 82 91 100

Day Number

Number of ED Visits

Sensitivity and Specificity Tradeoff

Sensitivity is the probability of alarm given an outbreak P(A+|O+)

Specificity is the probability of no alarm given no out break P(A-|O-)

Timeliness is time between outbreak and detection

Challenge: Increasing sensitivity and improving timeliness decreases specificity

Approach Overview*

Instead of trying to improve the detection method, ‘post-process’ the signals: Use a standard detection method to

provide signalsFeed this signal to a decision support

model to find the optimal action

Quick Introduction to POMDPs*

States: sS Actions: aA Observations: oO Transition probabilities: Pr(s’|s,a) Observation probabilities: Pr(o|s,a) Rewards: R(s,a) Belief state: b(s)

What goes on: st-1 st

at-1 atWhat we see: ot-1 ot

What we infer: bt-1 bt

Model Components States: - True epidemic state

No Outbreak Day1 ... Day4

Observations: Output from the detection algorithm: Alarm No-Alarm

Model Components (continued)

Actions1. Do nothing

2. More Systematic Studies (e.g. get more patient files from ED)

3. More Investigation (done by human expert)

4. Declare outbreak

Transition and Observation Probabilities Calculated based on expert knowledge

Model Components (continued)*

Costs Investigation (false and true positive) Intervention (false and true positive) Outbreak by day (false negative)

(# deaths* future earnings) + (# hospitalized * cost of hospitalization) + (# outpatient visits * cost of visit)

Model Components (continued)*

Rewards Preventable loss at each day

Outbreak Detection as a POMDP*

No OutbreakOutbreakdetectedD1 D2 D3 D4

Do nothing

Review records

Investigate cases

Declare outbreak

Experimental Design Compare a detection method (moving

average) with and without addition of POMDP

Consider a fixed Specificity of 0.97

The comparison is over 10 years simulation Not exactly clear how the data is generated

Experimental Results*

Small size outbreak

Day of Outbreak

Experimental Results*

Day of Outbreak

Larger size outbreak

Conclusion POMDPs can improve the accuracy of the

current outbreak detection methods

We can use the potential costs and effects of intervention to learn a decision process

P(A-|O-) = 0.97 P(A|O-) = 0.03 In every 100 days , we will have 3 false alarms! Is this acceptable?