2. 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

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

4. Existing Solutions Enforced by Department of Health Quarantine – there has to be enough evidence of 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

5. Existing Solutions Enforced by Department of Health Quarantine – there has to be enough evidence of 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

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

7. 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

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

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

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

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

12. Decomposition – using wavelets

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

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

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

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

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

18. 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!

19. 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

20. 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.

21. 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

22. Questions?