Ambient Geographic Information and Biosurveillance

Capstone Presentation

Todd Barr

March 20, 2013

“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

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”

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

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

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

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

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

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

Data Metrics – Key Words

flu Influenza h1n1 H3N2 H5N1 Adenovirus0

5000

10000

15000

20000

25000

30000

Key Word and Hashtag

Data Metrics - Modifiers

sick

have

shot

sch

ild

Health

I hop

eain

tha

s

docto

rI f

eel

son dr

I thi

nkDon

t

Nurse

meds

isn't

vacc

inati

onwor

se

diag

nose

d

Well

ness

suffe

ring

teen

Phys

ician

dayc

are

reduc

e

prov

ider

0

500

1000

1500

2000

2500

3000

Tweet Modifiers

Data Metrics – by State

AK ARM

DM

A CA GA TN NJW

I HI ID KS LAM

EM

NM

T NENM OK

WA SC UT

WY

0

500

1000

1500

2000

2500

3000

Data Metrics – by Quartile

Total Tweets By Quartile

Quartile 1

Quartile 2

Quartile 3

Quartile 4

No Data

Maps – All Tweets

Map – Tweets January 5th

Map – CDC ILI January 5

Maps – Tweets January 12

Maps – CDC ILI January 12

Maps – Tweets January 19

Maps – CDC ILI January 19

Maps – Tweets January 26

Maps – CDC ILI January 26

Maps – Tweets February 2

Maps – CDC ILI February 2

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

Contact

Todd Barr

tbarr@usgs.gov