analyzing over-the-counter medication purchases for early detection of epidemics and bio-terrorism...
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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?
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