Arcadia FluFender - Surescripts Technology Challenge Final Presentation

Arcadia FluFender
Prepared for the SureScripts Technology Challenge

www.arcadiasolutions.com

CONFIDENTIAL and PROPRIETARY. | ©2013 Arcadia Solutions.

1

Viral infectious disease
Spreads via direct transmission, airborne, hand-to-eye/nose/mouth
A healthy adult can be contagious beginning one day before
symptoms develop and up to seven days after becoming sick


2

5-20% of the population get the flu
in a typical year

The flu tends to be seasonal
In the U.S., Oct. – May, peaking in
Jan. or Feb.



3

Between 1976-2006, estimates of flu-associated
deaths in the U.S. range from 3,000 – 49,000 people
Vaccination and early treatment are effective
prevention strategies



4

Why track with prescription antivirals?
Indicates the presence of influenza but also of its intensity and the
involved population

Antiviral prescriptions are a reliable indicator
 Unlike OTC and ILI estimates, ePrescriptions contain verified
data about the patient that reliably inform models

Antiviral prescriptions are a precise indicator
 FDA requires that AVs be prescribed within 48h of symptoms, and
only aggressive (non-complicated) cases

 Symptoms generally commence 24-48h after contagion


5

FluFender Epidemiological Model
FluFender performs short-term forecasting based on known clinician
and patient behavior
1 Patient is infected with the flu virus.

1

1

t=0

2

1-4d

3

1.0
1d
𝜑
2

𝜑
2
3

Rx

𝑅 𝑥 𝑡, 𝑖 = Prescriptions at time 𝑡 in area 𝑖
𝛾 = effect of close proximity
𝜑 = likelihood of transmission
𝛿 𝑖 = spatial separation between 𝑖 and 𝑘
𝑏 = strength of distance effect

4

1.0
1d

Rx

4

𝛾

𝑅𝑥 𝑡 =
𝑖≠𝑘

𝜑
𝑅 𝑥 𝑡 − 2, 𝑖 + 𝑅 𝑥 𝑡 − 3, 𝑖
𝛿𝑖 𝑏 2

𝜑
+
𝑅 𝑥 𝑡 − 2, 𝑖 + 𝑅 𝑥 𝑡 − 3, 𝑖
2

6

2

1

Patient becomes highly symptomatic
1-4 days later.
1

t=0

2

1-4d

2

3

1.0
1d
𝜑
2

𝜑
2
3

Rx


4

1.0
1d

Rx

4

𝛾

𝑅𝑥 𝑡 =
𝑖≠𝑘

𝜑
𝑅 𝑥 𝑡 − 2, 𝑖 + 𝑅 𝑥 𝑡 − 3, 𝑖
𝛿𝑖 𝑏 2

𝜑
+
𝑅 𝑥 𝑡 − 2, 𝑖 + 𝑅 𝑥 𝑡 − 3, 𝑖
2

7

2

1-4 days later.

3

Patient seeks medical attention.

1

1

t=0

2

1-4d

2

3

1.0
1d

3

𝜑
2

𝜑
2
3

Rx


4

1.0
1d

Rx

4

𝛾

𝑅𝑥 𝑡 =
𝑖≠𝑘

𝜑
𝑅 𝑥 𝑡 − 2, 𝑖 + 𝑅 𝑥 𝑡 − 3, 𝑖
𝛿𝑖 𝑏 2

𝜑
+
𝑅 𝑥 𝑡 − 2, 𝑖 + 𝑅 𝑥 𝑡 − 3, 𝑖
2

8

2

1-4 days later.

3


1

4

1

t=0

2

Patient can be prescribed Tamiflu up
to 48 hours following onset of
symptoms.

1-4d

2

3

1.0
1d

3

𝜑
2

𝜑
2
3

Rx


4

1.0
1d

4

Rx

4

𝛾

𝑅𝑥 𝑡 =
𝑖≠𝑘

𝜑
𝑅 𝑥 𝑡 − 2, 𝑖 + 𝑅 𝑥 𝑡 − 3, 𝑖
𝛿𝑖 𝑏 2

𝜑
+
𝑅 𝑥 𝑡 − 2, 𝑖 + 𝑅 𝑥 𝑡 − 3, 𝑖
2

9

5

2

1-4 days later.

3


1

4

5

1

t=0

2

Patient can be prescribed Tamiflu up
to 48 hours following onset of
symptoms.
What no one noticed was that the
patient was already contagious one
day before onset of symptoms.

1-4d

2


1.0
1d

3

4

1.0
1d

𝜑
2

𝜑
2
3

Rx

Therefore, the most likely window for
transmission is 2-3 days prior to the day
a Tamiflu prescription is transmitted.

3

4

Rx

4

𝛾

𝑅𝑥 𝑡 =
𝑖≠𝑘

𝜑
𝑅 𝑥 𝑡 − 2, 𝑖 + 𝑅 𝑥 𝑡 − 3, 𝑖
𝛿𝑖 𝑏 2

𝜑
+
𝑅 𝑥 𝑡 − 2, 𝑖 + 𝑅 𝑥 𝑡 − 3, 𝑖
2

10

Why FluFender?
Arcadia Analytics FluFender offers a platform for users and healthcare
professionals to both track and forecast flu and antiviral requirements



11

FluFender Analysis
Prepared for SureScripts Technology Challenge
Based on dataset 11/13/2013


12

516,032

Number of SCRIPT
10.6 Records



13

516,032

Number of SCRIPT
10.6 Records

16,033

Number of
Antiviral Rx

(Tamiflu, Relenza, etc.)



14

516,032

Number of SCRIPT
10.6 Records

16,033

Number of
Antiviral Rx

2


Number of
Temporal Clusters



15

516,032

Number of SCRIPT
10.6 Records

16,033

Number of
Antiviral Rx


2

Number of
Temporal Clusters

15/4

Number of Spatial
Clusters



16

516,032

Number of SCRIPT
10.6 Records

16,033

Number of
Antiviral Rx


2

Number of
Temporal Clusters

15/4

Number of Spatial
Clusters

Age/Sex

Geo-temporally
Relevant Flu
Cases



17

Starting In North Carolina



18

Moving to South Carolina



19

Growth and Peak in South Carolina



20

Peaks in Florida



21

Outbreak A – Florida
FluFender detected an outbreak with narrow geospatial movement in
Orlando, FL with an outbreak radius of 51mi
Outbreak Status : Ongoing
 Date Range – 12/07/2013 – 12/30/2013
Demographics
 Male (50%), Female (50%)
 2-15yo
- 25%
 65+yo
-70%
 Population Status: Vulnerable
 Large percentage elderly (65+) and young (2-15)

Predictions (3 Day Forecast)
 Likely continuation: 23 Rx in existing area
 Likely addition: 10 Rx in new areas (see report for
new Rx)



22

What can FluFender tell us?
Forecasts antiviral requirements over a short timespan, assisting with
potential drug distribution

 15 clusters in November
– Upper New England
• 274 prescriptions in November
• Almost exclusively 45 years of age and older, mostly in 65+

– Anchorage, Alaska
• 231 prescriptions in November
• Almost exclusively ages 2-15, with large percentage in 0-2

 4 Clusters in December
– Southeast United States (centered in Florida)
• Large percentage elderly (65+) and young (2-15)



23

WHY US?
“Dreaming, after all, is a form of planning.” –Gloria Steinem



24

APPENDIX


25

Future Potential for FluFender

Public Health

Technology &
Security

User Features

•
•
•
•

Integrate Flu Vaccine provider locations
User Auto-Location
Local flu outbreak email updates
Add “Did You Feel It?” style survey that
relates directly to health outcomes

• Share flu tracking over social networks
• Enable user-embedded FluFender maps
on blogs and websites
• Timescale sliders for easier user
experience

• Enhanced cell-blocking on low Rx counts • Improved predictive algorithm through
for improved Rx privacy
additional “training” datasets from
• Improved predictive geocoding
previous outbreaks
• Improve graph options and layouts
• Integration with Health Information
• Use of Rx ICD-9 codes to identify
Exchanges for improved population
vulnerable populations
management
• E-mail prescription alerts to vulnerable
populations
• Additional Rx and OTR medications in
predictive model


• Alignment with existing public health
data sources for improved forecasting
• Standardized public health reporting
modules for local authorities and Federal
agencies (e.g. HHS, CDC)

26

Future Potential for FluFender

Public Health

Technology &
Security

User Features

•
•
•
•

Integrate Flu Vaccine provider locations
User Auto-Location
Local flu outbreak email updates
Add “Did You Feel It?” style survey that
relates directly to health outcomes

• Share flu tracking over social networks
• Enable user-embedded FluFender maps
on blogs and websites
• Timescale sliders for easier user
experience

• Enhanced cell-blocking on low Rx counts • Improved predictive algorithm through
for improved Rx privacy
additional “training” datasets from
• Improved predictive geocoding
previous outbreaks
• Improve graph options and layouts
• Integration with Health Information
• Use of Rx ICD-9 codes to identify
Exchanges for improved population
vulnerable populations
management
• E-mail prescription alerts to vulnerable
populations
• Additional Rx and OTR medications in
predictive model


• Alignment with existing public health
data sources for improved forecasting
• Standardized public health reporting
modules for local authorities and Federal
agencies (e.g. HHS, CDC,

27

FluFender Enhancements
 Distinguish between palliative and prophylactic use
 Distinguish between vulnerable and regular populations
– Determined based on ages and genders
– Increased accuracy if ICD-9 codes in Rx data were reliable

 A small, intense outbreak could look the same as a large,
relatively mild outbreak

 Currently uses a simple predictive model
– Requires larger data sets and additional testing to determine
whether the model extends from one outbreak to another,
between years, and between populations.
– Need to build interaction between user-generated data and
predictions


28

Developing the model


We considered the factors surrounding an individual getting a Rx for Tamiflu:
–
–
–
–
–



Prophylactic: Vulnerable (0-2yo, 65+yo, pregnant ♀, ↓immune)
Palliative: Intense symptoms, non-complicated (e.g. no comorbidities)
Patient can only get Rx for Tamiflu within 48h of onset of symptoms
Since contagion starts about 1d before symptoms, that 1d represents a window for transmission of high-intensity flu;
because of the FDA regs, that 1 day is almost exactly 3d before the Rx is written
Also, transmission should be most likely very close to existing cases, and for children and high-density populations (that
have greater group contact)

These facts and assumptions directly form the model for occurrence and transmission used here.

Testing the model


We tested the three-day conclusion on SureScripts test data
–
–
–



The outbreak intensity auto-correlated at 2-3 days, supporting the contagion window model
The results rolled off for smaller outbreaks
Geospatial and learning models contributed very little in initial testing, although the number of cases available were
too small to make any strong conclusions.

The model currently primarily uses Rx data for forecasting; other prediction models have been tested but are
not as reliable as the Rx data.

Improving the model


The following could help improve the forecasting model: More training data; more information about the
prescriptions; more patient information in the prescription (e.g. ICD-9 codes); incorporation of information
about populations into the model; reliable historical context



29


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30

Arcadia FluFender - Surescripts Technology Challenge Final Presentation

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