Using Social Media and Risk Terrain Mapping to predict which states are most affected by influenza and where likely outbreaks will occur

1. Ambient Geographic
Information and
Biosurveillance
Capstone Presentation
Todd Barr
March 20, 2013

2. • Reports Only the Cases that are handled by Medical
Professionals
• Data is sent to the Centers for Disease Control and
Prevention
• Data is Aggregated to the State level
• Standard Turn Around time is anywhere from 7 to 10
days depending on the data, and the level of the crisis
“Classic” Biosurveillance

3. • Ambient Geographic Information (AGI) differs from
Volunteered Geographic Information (VGI)
• Most Commonly Captured from Twitter, Facebook and
Four Square
• Can be used to trace vectors through Social Networks
• Can Determine “Hot Spots” of activity via Hashtags, key
words and modifiers
• Starting to be used in Biosurveillance, but still does not
have buy in from “establishment”
Ambient Geographic
Information

4. • Originally Used to Predict Crime
• Core Concept is that Certain activities are related to
Geographic Features (Assaults tend to occur near certain
Liquor Stores, Bars or Entertainment Venue)
• Leads to a Spatial Understanding for Strategic Decision
Making
• Allows Decision Makers to make best use their of
Resources
Risk Terrain Modeling

5. • AGI
• Allowing Real Time Disease Information to be consumed
and Analyzed both Spatially and Text
• No turn around time
• Not Aggregated to a State level
• RTM
• Generation of a RTM Map for Public Health by County
• People in the lesser served areas less likely to seek medical
attention and less likely to have symptoms/aliment reported
AGI and RTM Enhancing
Biosurveilance

6. • Used the Criteria from Publication “County Health
Rankings and Roadmaps: a Healthier Nation County by
County
• 32 influencers on health and health care quality
• Examples
• Number of Medical Doctors in County
• Proximity to Medical Care
• Percentage of Population with Health Insurance
• Divided Counties into Quartiles
• 152 counties had no Data
Data Collection - RTM

7. • Used Python Script To Collect Tweets within the US to
populate spreadsheet
• Collected an average of 40,000 tweets a night
• Roughly 5% of those Tweets had location data
• Used Hashtags, Keywords and Modifiers to determine if
they were talking about the Flu, or getting a Flu shot
Data Collection - AGI

8. • Collection of Flu Related Geo located Tweets within the
United States from the week of January 5 to the week
ending February 2
• Determined how many of those Tweets were in each
Quartile
• Compare the Results to the CDC Data from those same
timeframe
The Study

9. • Total Usable Tweets 25,000
• Geocoding Issues
• Most had City and State
• Some just had State
• Others had full State Names which did not Geocode
• Others had Clinics for Cities and Cities for States
• Used both ESRI Online Geocoding as well as CartoDB
• ESRI Online Geolocated 75% of the total tweets
• CartoDB Geolocated 90% of the total tweets
Data Cleaning - AGI

10. 30000
25000
20000
15000
10000
5000
0
Key Word and Hashtag
flu Influenza h1n1 H3N2 H5N1 Adenovirus
Data Metrics – Key Words

11. 3000
2500
2000
1500
1000
500
0
Tweet Modifiers
Data Metrics - Modifiers

12. Data Metrics – by State
0
500
1000
1500
2000
2500
3000
AK
AL
AR
AZ
MD
FL
MA
NY
CA
DE
GA
VA
TN
MO
NJ
MI
WI
NC
HI
IA
ID
IN
KS
KY
LA
PA
ME
OR
MN
MS
MT
ND
NE
NH
NM
NV
OK
WV
WA
RI
SC
SD
UT
VT
WY

13. Total Tweets By Quartile
Data Metrics – by Quartile
Quartile 1
Quartile 2
Quartile 3
Quartile 4
No Data

14. Maps – All Tweets

15. Map – Tweets January 5th

16. Map – CDC ILI January 5

17. Maps – Tweets January 12

18. Maps – CDC ILI January 12

19. Maps – Tweets January 19

20. Maps – CDC ILI January 19

21. Maps – Tweets January 26

22. Maps – CDC ILI January 26

23. Maps – Tweets February 2

24. Maps – CDC ILI February 2

25. • Social Media can be used as a new tool in the
Biosurveillance Toolkit
• Tweets are nearly evenly disturbed between the Risk
Quartiles
• Social Media shows trends that are reflected in the CDC
Data
Conclusions

26. Contact
Todd Barr
Todd.barr@SpatialCapability.com