Tom Hilbert. Influenza Detection from Twitter

Transcription

1 Tom Hilbert Influenza Detection from Twitter

2 Influenza Detection from Twitter Addressed Problem Contributions Technical Approach Model Data Results/Findings Page 2

3 Influenza Detection from Twitter Addressed Problem Contributions Technical Approach Model Data Results/Findings Page 3

4 Addressed Problem WHO Key Facts: Seasonal influenza is an acute viral infection that spreads easily from person to person. Seasonal influenza viruses circulate worldwide and can affect anybody in any age group. Seasonal influenza viruses cause annual epidemics that peak during winter in temperate regions. Influenza vaccination is the most effective way to prevent infection. Statistics 10-15% of people get influenza Worldwide: 50 million illnesses 500,000 deaths Page 4

5 Influenza Detection from Twitter Addressed Problem Contributions Technical Approach Model Data Results/Findings Page 5

6 Influenza Detection from Twitter Addressed Problem Contributions Technical Approach Model Data Results/Findings Page 6

7 Contributions Early Stage flu detection on twitter Capture flu outbreaks more promptly and accurate compared with baselines Modelling spatial and temporal distribution Creation of a real-time flu-detection better flu prediction than other signals (e.g. query-click on search engine) Page 7

8 Influenza Detection from Twitter Addressed Problem Contributions Technical Approach Model Data Results/Findings Page 8

9 Influenza Detection from Twitter Addressed Problem Contributions Technical Approach Model Data Results/Findings Page 9

10 Technical Approach Page 10

11 Technical Approach Different Phases: Non-epidemic phase (NE) Rising epidemic phase (RE) Stationary epidemic phase (SE) Declining epidemic phase Page 11

12 Influenza Detection from Twitter Addressed Problem Contributions Technical Approach Model Data Results/Findings Page 12

13 Influenza Detection from Twitter Addressed Problem Contributions Technical Approach Model Data Results/Findings Page 13

14 Technical Approach Model Daily Effect: Peoples inclination to post flu related tweets on each day within the week Model as a Gaussian distribution, providing mean and variance depending on the day of the week Spatial and Temporal dependency Dependency on Neighboring states in the US and dependency of the previous phase Modeled as unified Markov Network Epidemic Dynamics Non-epidemic phase and stationary epidemic phase only affected by daily effect Rising epidemic phase and declining epidemic model with autoregressive process combined with daily effect Page 14

15 Technical Approach Model Page 15

16 Influenza Detection from Twitter Addressed Problem Contributions Technical Approach Model Data Results/Findings Page 16

17 Influenza Detection from Twitter Addressed Problem Contributions Technical Approach Model Data Results/Findings Page 17

18 Technical Approach - Data Collecting Fetched tweets containing indicator words (flu, influenza, H5N1, H5N9, swine flu, bird flu) June 2008 June million tweets 0.9 million users Classifying Data Starting to get worried about swine flu. [Awareness]. Swine flu vaccine is not only for pupils. [Related News]. The bird flu last year is scary. [Out of date]. My son got flu, really worried. [Referring Another person]. Do you think swine flu will come to America? [Question]. Doctors urged people at risk of contracting swine flu to get vaccinated. [Warning]. Filtering Retweets and Duplicates Page 18

19 Technical Approach Page 19

20 Influenza Detection from Twitter Addressed Problem Contributions Technical Approach Model Data Results/Findings Page 20

21 Influenza Detection from Twitter Addressed Problem Contributions Technical Approach Model Data Results/Findings Page 21

22 Results/Findings Page 22

23 Results/Findings Real Time Data Page 23

24 Results/Findings - Model Comparison Page 24

25 Results/Findings Predict Number of Tweets Page 25

26 Results Findings Predict Visits Page 26