Analyzing over-the-counter medication purchases for early detection of epidemics and bio-terrorism

1. Analyzing over-the-counter medication purchases for early detection of epidemics and bio-terrorism by Anna Goldenberg Advisor: Rich Caruana Note: Sponsored by CDC Grant

2. Problem Statement Long history of epidemics and bio-terrorism attacks – no good early detection system!

3. Existing Solutions • Enforced by Department of Health • Quarantine – there has to be enough evidenceof mass sickness • Sanitation – always helps but what if it’s an intentional release of bio–agent? • Immunity • Vaccination • Computer Surveillance Systems - do not prevent from new strains - do not prevent from new strains

4. Existing Solutions • Enforced by Department of Health • Quarantine – there has to be enough evidenceof mass sickness • Sanitation – always helps but what if it’s an intentional release of bio–agent? • Immunity • Vaccination • Computer Surveillance Systems • System for clinicians to report suspicious trends of possible bio- terrorist events • assessing the current capacity of hospitals and health systems to respond to a bio-terrorist attack • evaluating and improving linkages between the medical care, public health, and emergency preparedness systems to improve detection of and response to a bio-terrorist event - do not prevent from new strains

5. Gap • Fault: Existing CBSS rely on medical records – may not be early enough! (anthrax)

6. Gap • Fault: Existing CBSS rely on medical records – may not be early enough! (anthrax) • Solution: Create a system based on non-specific syndrome data, for e.g. over-the-counter medications

7. Proposed Framework Data Preprocessing Merge to get final prediction Smoothed Model Decomposition Prediction of each component Real-time data > threshold NO YES WARNING! – POSSIBLE BEGINNING OF AN EPIDEMIC

8. Proposed Framework Data Preprocessing Merge to get final prediction Smoothed Model Decomposition Prediction of each component Real-time data > threshold NO YES WARNING! – POSSIBLE BEGINNING OF AN EPIDEMIC

9. Smoothed Model Smooth original data by using DCT and removing small coefficients that correspond to noise DCT: rms=0.0798 k=1,..,N, N – length of data vector TOO SMOOTH! rms = 0.1055

10. Proposed Framework Data Preprocessing Merge to get final prediction Smoothed Model Decomposition Prediction of each component Real-time data > threshold NO YES WARNING! – POSSIBLE BEGINNING OF AN EPIDEMIC

11. Decomposition –using wavelets

12. Proposed Framework Data Preprocessing Merge to get final prediction Smoothed Model Decomposition Prediction of each component Real-time data > threshold NO YES WARNING! – POSSIBLE BEGINNING OF AN EPIDEMIC

13. Predictions Since each component is smooth – using linear methods, such as AR, for predictions of each component

14. Proposed Framework Data Preprocessing Merge to get final prediction Smoothed Model Decomposition Prediction of each component Real-time data > threshold NO YES WARNING! – POSSIBLE BEGINNING OF AN EPIDEMIC

15. Comparison step Data falls under the threshold -> declare normal flow. No flag is raised. Note: in reality – no outbreak at that time

16. Proposed Framework Data Preprocessing Merge to get final prediction Smoothed Model Decomposition Prediction of each component Real-time data > threshold NO

17. Why so many steps? • Smoothing: original data is too hard to predict little confidence in prediction • Decomposition: even after smoothing – too complicated for regular TSA tools to predict Main Reason: need as much confidence in our model as possible – lives may depend on this!

18. Results • Ran the system according to the framework with different thresholds (as in the legend) Detected strong epidemic 8 days early, weak one – 2 days early had one false alarm with threshold set as 4% above prediction

19. Complications • Hard to make predictions around big holidays. It is possible that people stock up at that time • Lack of detailed data concerning real outbreaks • Difficulty in distinguishing between very early prediction and false alarms So far, need to consult an expert on the issues above.

20. Future Work • Analyze the lower bound on accuracy of the prediction • Incorporate expert knowledge into the process, for e.g. remove known periodicities • Predict based on a selection of products, not just one category • Set threshold to be the function of cost when acted upon a false alarm

21. Questions?