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

2. “Classic” Biosurveillance 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

3. Ambient Geographic Information 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”

4. Risk Terrain Modeling 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

5. AGI and RTM Enhancing Biosurveilance 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

6. Data Collection - RTM 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

7. Data Collection - AGI 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

8. The Study 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

9. Data Cleaning - AGI 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

10. Data Metrics – Key Words

11. Data Metrics - Modifiers

12. Data Metrics – by State

13. Data Metrics – by Quartile

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. Conclusions 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

26. Contact Todd Barr tbarr@usgs.gov