Use of Social Media to Monitor and Predict Outbreaks and Public - - PowerPoint PPT Presentation

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Use of Social Media to Monitor and Predict Outbreaks and Public - - PowerPoint PPT Presentation

Nov 15, 2023 97 likes •398 views

Use of Social Media to Monitor and Predict Outbreaks and Public Opinion on Health Topics Alessio Signorini Department of Computer Science University of Iowa December 3rd, 2014 Measurement is the first step that leads to control and

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Use of Social Media to Monitor and Predict Outbreaks and Public Opinion

  • n Health Topics

Alessio Signorini

December 3rd, 2014

Department of Computer Science University of Iowa

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“ Measurement is the first step that leads to control and eventually to improvement. “

  • James Harrington
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  • Nascar / Formula One
  • Sports
  • Insurances
  • Sales / Marketing
  • Online Advertising
  • Logistics

Data Analytics

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in Public Health we have

Disease Surveillance

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Surveillance Systems

  • Vital Statistics & Registries (e.g., births, deaths, defects)
  • Population Surveys (e.g., substance abuse)
  • Disease Reporting (e.g., salmonellosis, measles)
  • Sentinel Surveillance (e.g., Influenza-Like Illnesses)
  • Adverse Events Surveillance (e.g., issues with drugs)
  • Laboratory Data
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surveillance data

should be a byproduct

  • f any healthcare operation
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Syndromic Surveillance

  • Focuses on Early Detection
  • Based on disease signs or symptoms, not diagnosis
  • Novel sources: Emergency Room data, Drugs sales
  • Uses well known Data Mining techniques
  • Reduced delay in results
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aggregate and analyze

Social Media Data

to monitor and predict health trends

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~5B/day ~10M/day ~7M/day ~500M/day

  • nline

~27h/mon

5% 13% 19% 20% 21% 22%

Social Search Content Email/IM Video Shopping

mobile

~34h/mon

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Google Searches Positive Tweets

Comprehensive Exam Alessio Signorini

University of Iowa, May 2010

Monitor Public Opinion

vs.

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The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic Alessio Signorini, Alberto Segre, Philip Polgreen

PLoS ONE – Journal, May 2011

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Using Twitter to Estimate H1N1 Activity Alessio Signorini, Alberto Segre, Philip Polgreen

ISDS 2010 – 9th Annual Conference of International Society for Disease Surveillance

error ~0.28% error ~0.37%

Estimate ILI%

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Inferring Travel from Social Media Alessio Signorini, Alberto Segre, Philip Polgreen

ISDS 2011 – 10th Annual Conference of International Society for Disease Surveillance

National Local

Monitor Travels

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can we use

“Social Travel Models”

to improve local flu trends prediction?

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City-Level Flu Trends

  • CDC’s MMWR - Flu & Pneumonia Deaths for 122 cities
  • Smoothed each week with values of prev/next 2 weeks

Philadelphia, PA - Deaths for 2012 New York City, NY - Deaths for 2012

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Social Travel Data

  • 240 Million geolocated tweets posted by 4 Million users
  • Mapped over MMWR cities, discarded overlapping ones
  • Used Spark cluster of 8 machines to do geo-mapping

Volume of Trips among MMWR cities 2012 TKG COL FAT SPK

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Social Travel Model

  • Final dataset: 78 cities, 124M tweets, 2.2M users
  • Assumed “home” the most common location
  • A “trip” was a post at home followed by one elsewhere
  • Used population to scale volume of trips between cities
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Correlation b/w Cities

San Jose, CA Atlanta, GA Philadelphia, PA

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Predicting Flu Trends

  • Flu Trends of 78 cities generated from MMWR data
  • Used 2011 for training and 2012 for testing
  • Support

Vector Regression with polynomial kernel

  • Target: value of local flu trend for that week
  • Features: value of top 20 correlated cities 2 weeks before
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Measures Compared

  • Distance closest 20 cities
  • Similarity most similar 20 cities on 2011 flu trends
  • Flow top 20 cities by number of visitors
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Prediction Results

Dallas, TX San Jose, CA

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Failure Hypothesis

  • Port-of-entry influenced by international travels
  • Noisy data Watebury, CT had only 43 deaths in 2011
  • Few data Fort Wayne has 1/50th of Las

Vegas’ users

Washington, DC - Flu Deaths 2012

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Conclusions

  • Social Media can be an important source for surveillance
  • Can predict American Idol’s winner ;)
  • Allows to monitor public sentiment about health topics
  • Can effectively be used to monitor ILI% in real time
  • Geolocated posts can be used to create travel models
  • Social Travel Data provides additional predictive power

for flu trends

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Checkins Distributions

0 < 1 mile 1 < 10 miles 10 < 100 miles 100 < 1000 miles 1000 < 10000 miles

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 50% 85% 97% 99% 100% % Trips % Cumulative

10s 30s 1m 2m 5m 10m 15m 30m 1h 2h 6h 12h 1d 2d 1w

0% 2% 4% 6% 8% 10% 12% 14% 16% 0% 1% 4% 8% 15% 21% 24% 31% 38% 46% 59% 69% 81% 89% 97% % Trips % Cumulative

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Denver, CO

Distance Similarity Flow

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Smoothing Methods

5 weeks ahead 1 week around 2 weeks around