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

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

Problem Statement

Long history of epidemics and bio-terrorism attacks

– no good early detection system!

Existing Solutions

Enforced by Department of HealthQuarantine – 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

- do not prevent from new strains

Existing SolutionsEnforced 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 SystemsSystem 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

Gap

Fault: Existing CBSS rely on medical records

– may not be early enough! (anthrax)

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

YES

Proposed FrameworkData

Preprocessing

Smoothed

Model Decomposition

Prediction of each

component

Merge to get

final

prediction

Real-time data > thresholdNO

WARNING! – POSSIBLE BEGINNING OF AN EPIDEMIC

YES

Proposed FrameworkData

Preprocessing

Smoothed

Model Decomposition

Prediction of each

component

Merge to get

final

prediction

Real-time data > thresholdNO

WARNING! – POSSIBLE BEGINNING OF AN EPIDEMIC

Smoothed Model

N

nN

knnxkwky

12

)1)(12(cos)()()(

Nk

kkw

N

N

2,

1,)(

2

1

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:

YES

Proposed FrameworkData

Preprocessing

Smoothed

Model Decomposition

Prediction of each

component

Merge to get

final

prediction

Real-time data > thresholdNO

WARNING! – POSSIBLE BEGINNING OF AN EPIDEMIC

Decomposition – using wavelets

YES

Proposed FrameworkData

Preprocessing

Smoothed

Model Decomposition

Prediction of each

component

Merge to get

final

prediction

Real-time data > thresholdNO

WARNING! – POSSIBLE BEGINNING OF AN EPIDEMIC

PredictionsSince each component is smooth – using linear methods, such as AR,

for predictions of each component

YES

Proposed FrameworkData

Preprocessing

Smoothed

Model Decomposition

Prediction of each

component

Merge to get

final

prediction

Real-time data > thresholdNO

WARNING! – POSSIBLE BEGINNING OF AN EPIDEMIC

Comparison step

Data falls under the threshold -> declare normal flow.

No flag is raised. Note: in reality – no outbreak at that time

Proposed FrameworkData

Preprocessing

Smoothed

Model Decomposition

Prediction of each

component

Merge to get

final

prediction

Real-time data > thresholdNO

Why so many steps?Smoothing:

original data is too hard to predictlittle 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!

ResultsRan the system according to the framework with different thresholds (as in the legend)

0

1

2

3

4

5

6

1/30/00

2/14/00

2/29/00

3/15/00

3/30/00

4/14/00

4/29/00

5/14/00

5/29/00

6/13/00

6/28/00

7/13/00

7/28/00

8/12/00

8/27/00

9/11/00

9/26/00

10/11/00

10/26/00

11/10/00

11/25/00

12/10/00

12/26/00

day number

nu

mb

er

of

ala

rms

2%

2.5%

3%

4%

5%

Eit

her

12

day

s ea

rly

or

fals

e

Eit

her

8 d

ays

earl

y o

r fa

lse

2 d

ay

s e

arl

y -

tr

ue

ala

rm

fals

e al

arm

-

po

ssib

ly b

efo

re E

aste

r

fals

e al

arm

Detected strong epidemic 8 days early,

weak one – 2 days early

had one false alarm with threshold set as 4% above prediction

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.

Future Work

Analyze the lower bound on accuracy of the predictionIncorporate expert knowledge into the process, for e.g. remove known periodicitiesPredict based on a selection of products, not just one categorySet threshold to be the function of cost when acted upon a false alarm

Questions?