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Avar. Health. Sentinel. Public. Dashboard. Health Alert Level. Team Mentor: Avaré Stewart. Monitoring the blogosphere for emerging, health related events, so Health Officials don‘t have to. Event-Based Biosurveillance.

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Avar

Health

Sentinel

Public

Dashboard

Health Alert

Level

Team Mentor: Avaré Stewart

Monitoring the blogosphere for emerging, health related events, so Health Officials don‘t have to


Event-Based Biosurveillance

Monitor time series, textual data to provide early alerts to anomalies ... stimulate

investigation of potential outbreaks

Shmueli 2010


What If ....?

  • Could we have

  • detected the

  • emergence of the

  • 2009 Swine Flu

  • Pandemic from

  • BlogSocial Media ?


Questions to Consider?

  • What approach can be used to create SimulatedOutbreaks for blog text?

    • What outbreak patterns/signatures exist?

    • How can text be generated to simulate a given outbreak pattern?

  • What tools would be useful in creating Simulated Textual and Numerical Outbreak Data?


Feature selection and counts for outbreak data
Feature Selection and Counts for Outbreak Data

Event-Based (Text) Data

  • Select (textual features)

    i.e.: Number documents containing mentions „flu“

  • Get timestamps

  • Create counts from features

  • Time SeriesData

    • Select features:

      • i.e.: hospital visits, death rate

    • Get timestamps

    • Create counts from features


What is the Task?

  • Using existing data, tools and references:

  • Part 1: Design an approach to creating Simulated Data from example data

  • Part 2: Design an approach for adding Noise to the Simulated Data

  • Part 3: List and summarize any additional tools and approaches that would be useful in creating Simulated Data

  • Document your design:

    • Discuss the motivation for your work/results

    • Outline algorithms with PseudoCode

    • Provide several example input and outputs

    • Hightlight strengths and shortcomings


What Will You Learn?

  • Build Simulated Data from Event-Based Biosurveillance

  • How to organize, design, implement, deliver small-scale project results

  • That your contributions are valuable ....


Starting Points

  • Papers :

  • The Nature of Outbreaks and Their Determination (Shmueli, Section 3.2 )

  • Characteristic shapes of outbreak news. (Collier, Figure 5)

  • Model-Specific Generation:

  • Data Simulation Using R

    • http://biostat.mc.vanderbilt.edu/wiki/pub/Main/AngelAn/myslides5.pdf

  • Random Generation:

  • “Generating Random Text with Bigrams“

    • http://nltk.googlecode.com/svn/trunk/doc/book/ch02.html

  • “How to Generate Random Text in Word 2003” http://www.ehow.com/how_2183058_generate-random-text-word.html


Background Reading

  • Statistical Challenges Facing Early Outbreak Detection in Biosurveillance, Galit Shmueli

  • What‘s Unusual in Online Disease Outbreat News, Nigel Collier

  • Detecting Influenza Outbreaks by Analyting Twitter Messages, Aron Culotta


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