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 Reports Individual Event Definitions Population Pattern Definitions Event Detection Algorithm Pattern Report Population Under Surveillance Intervention Decision Intervention Guidelines Public 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.

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 signals  Feed this signal to a decision support model to find the optimal action

Quick Introduction to POMDPs * States:s  S Actions: a  A Observations: o  O Transition probabilities: Pr(s’|s,a) Observation probabilities: Pr(o|s,a) Rewards: R(s,a) Belief state: b(s)

Model Components States: - True epidemic state No Outbreak Day1... Day4 Observations: Output from the detection algorithm: Alarm No-Alarm

Model Components (continued) Actions 1. 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 *

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?