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Facebook: An Innovative Influenza Pandemic Early Warning System


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Presentation given at Medicine 2.0 in Toronto September 2009.

Published in: Education, Health & Medicine
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Facebook: An Innovative Influenza Pandemic Early Warning System

  1. 1. Facebook: An Innovative Influenza Pandemic Early Warning System Presented by Chen Luo (MBCHB 4) Supervisors: Dr Chris Paton and Dr Robyn Whittaker
  2. 2. Background <ul><li>No introduction needed. Influenza is a serious disease </li></ul><ul><ul><li>250,000 – 500 000 deaths worldwide/yr among the vulnerable (WHO 2003) </li></ul></ul><ul><ul><li>Lost productivity: $71-167 billion (USA) </li></ul></ul><ul><ul><li>Here to stay. Rapidly evolving virus – swine flu </li></ul></ul><ul><li>Current surveillance – clinical and virology based on network of primary health doctors </li></ul><ul><li>Disadvantages: 1-2 week reporting lag and health infrastructure </li></ul><ul><li>Facebook 150 million worldwide user base </li></ul>
  3. 3. Literature Review <ul><li>Databases searched: MEDLINE, EMBASE, SCOPUS, ISI Web of Knowledge, INSPEC </li></ul><ul><li>Latest news: Google blog search and Google </li></ul><ul><li>Hand searching of all relevant articles </li></ul><ul><li>2 prong standardized search strategy used </li></ul><ul><ul><li>1) Social Networks AND influenza </li></ul></ul><ul><ul><li>2) Web Surveillance AND influenza </li></ul></ul><ul><li>305 articles </li></ul><ul><ul><li>1) 0 articles </li></ul></ul><ul><ul><li>2) 6 articles </li></ul></ul>
  4. 4. MEDLINE Search <ul><li>1 (OSN or (social network* adj2 (site* or service or provider* or website* or online)) or facebook or myspace or orkut or bebo or friendster or hi5) (295) </li></ul><ul><li>2 Disease Outbreaks/ (50100) </li></ul><ul><li>3 Influenza, Human/ (20693) </li></ul><ul><li>4 (flu or ILI or influenza like illness or influenza-like-illness or influenza-like illness) (7406) </li></ul><ul><li>5 2 or 3 or 4 (71592) </li></ul><ul><li>6 1 and 5 (0) </li></ul><ul><li>7 ((Web or internet or net or online) adj3 (Data Collection or survey or questionnaire* or research or feedback or surveillance or experiment)) (3519) </li></ul><ul><li>8 5 and 7 (43) </li></ul>
  5. 5. Different Approaches <ul><li>1) Syndromic Surveillance: indirect measure of influenza </li></ul><ul><ul><li>Google Flu Trends: health seeking search terms </li></ul></ul><ul><ul><li>Global Public Health Intelligence Network: automated algorithm for news reports </li></ul></ul><ul><li>2) Primary Doctor (GP) web-based reporting – REALFLU sentinel GP electronic ILI report </li></ul><ul><li>3) Web-based self-reporting </li></ul><ul><ul><li>Pioneer: Great Influenza Survey (GIS) – Netherlands </li></ul></ul><ul><ul><ul><li>2003-2004 Flu season, 13 300 active participants </li></ul></ul></ul><ul><ul><ul><li>Initial demographic questionnaire </li></ul></ul></ul><ul><ul><ul><li>Weekly ILI symptoms questionnaire via email </li></ul></ul></ul><ul><ul><ul><li>Average correlation: GIS vs Sentinel GP </li></ul></ul></ul>
  6. 6. Gripenet <ul><li>Superseded GIS </li></ul><ul><ul><li>2006-2007 Flu season </li></ul></ul><ul><ul><li>Netherlands (19,623), Belgium (7,025), Portugal (3118) participants </li></ul></ul>
  7. 7. Influenza Tracking Project <ul><li>Inspired by Citizen Science movement </li></ul><ul><li>Pilot study – summer studentship </li></ul><ul><li>Improve on previous research by </li></ul><ul><ul><li>1) Simplification: lay person, easy to use </li></ul></ul><ul><ul><li>2) Using social networks to increase participation </li></ul></ul><ul><ul><li>3) Collecting location (IP data) to track influenza globally </li></ul></ul><ul><li>Similar model used: initial demographic questionnaire and weekly symptom questionnaire but a twist – Facebook application </li></ul>
  8. 8. 1. Simplification <ul><li>Problem: 13 ILI symptoms asked in GIS/Gripenet </li></ul><ul><ul><li>Runny nose? Sore throat? Muscle pain? Abdominal pain? Nausea? Sudden Fever (No/Yes/Don’t Know), How high was the fever? Cough? Headache? Chest pain? Diarrhea? Cold Shivers? Irritated Eyes? </li></ul></ul><ul><li>Complicated case definition; by “experts” </li></ul><ul><ul><li>Definition of ILI: Fever > 38C started suddenly plus headache or muscle pain, plus at least one respiratory symptom </li></ul></ul><ul><li>No current consensus on case definitions </li></ul><ul><li>Solution: Thursky (2003): Symptom Triad – Fever (subjective), cough and fatigue </li></ul><ul><ul><li>Effective: more sensitive and specific than CDC. S ensitivity (43.5-75.1%) , specificity (46.6-80.3%) and PPV ( 23.3-59.7%) . </li></ul></ul><ul><ul><li>Simple </li></ul></ul>
  9. 9. 2. Organic Growth <ul><li>GIS/Gripenet used: TV, radio interviews, information posted to Universities/schools and even competitions (!) </li></ul><ul><li>Participant growth dependant on expensive media campaign </li></ul><ul><li>Student = poor </li></ul><ul><li>But! Facebook applications engineered to spread.. Like.. </li></ul>
  10. 10. (Annoying) Fortune cookie <ul><li>Programmed application to be “viral” </li></ul><ul><ul><li>Via “newsfeeds” </li></ul></ul><ul><ul><li>“ Invitations” </li></ul></ul><ul><ul><li>“ Profile badges” </li></ul></ul><ul><li>Demonstrated later </li></ul>
  11. 11. 3. IP Data <ul><li>Information which tells you city/country where internet accessed </li></ul><ul><li>Worldwide real time surveillance </li></ul>
  12. 12. How we build the program <ul><li>Programming was relatively straight forward </li></ul><ul><ul><li>API (Application Program Interface): access to Facebook functions with just a line of code </li></ul></ul><ul><ul><li>Official resources like the Developer Centre provides documentation and examples </li></ul></ul><ul><ul><li>Open source: PHP and MYSQLI </li></ul></ul><ul><ul><li>(Inelegantly) program the application based on freely availiable PHP or MYSQL tutorials on the net </li></ul></ul><ul><ul><li>Design is based on copying and pasting Facebook CSS </li></ul></ul><ul><li>Google = best friend </li></ul>
  13. 13. The website <ul><li>Website for explanation </li></ul><ul><ul><li> </li></ul></ul><ul><li>Mass media exposure to get “critical mass” for viral spread like a nuclear reaction </li></ul>
  14. 23. IP address automatically stored
  15. 24. Invitations for “viral” spread
  16. 25. Profile Badges for “viral” spread
  17. 26. Newsfeeds (like a newspaper/stalker report of what your friends in your social network are doing) for “viral” spread
  18. 27. Weekly email notification <ul><li>Referred by mass media/blogs </li></ul>
  19. 28. Summary Slide Media Exposure Sign up Flu Symptoms Questionnaire Weekly email reminders “ Viral” Spread mechanisms
  20. 29. Beta Results <ul><li>Beta-testing started in 28/06/09 </li></ul><ul><li>Beta testers - 73 participants </li></ul><ul><li>52 participants filled more than 1 questionnaire </li></ul><ul><li>71% conversion rate </li></ul><ul><li>Demographics: </li></ul><ul><ul><li>Age: 30 yrs old (mean) </li></ul></ul><ul><ul><li>Gender: 37 M 36 F </li></ul></ul><ul><ul><li>Education: 83% of users had tertiary or postgraduate degree </li></ul></ul><ul><ul><li>Vaccination: 37% vaccination rate </li></ul></ul><ul><li>26 application “fans” </li></ul>
  21. 30. Beta Results <ul><li>Median days between questionnaire: 8.0 </li></ul><ul><li>Future calculation: </li></ul><ul><ul><li>No of ILI/no. of total participants in a city/country </li></ul></ul><ul><ul><li>World map </li></ul></ul><ul><li>This presentation marks the end of beta-testing and the start of the PR drive public </li></ul>
  22. 31. Plans for the Future <ul><li>Mass PR drive </li></ul><ul><ul><li>Please join! </li></ul></ul><ul><ul><li>Please do blog/spread the word! </li></ul></ul><ul><li>Research partnerships: </li></ul><ul><ul><li>Epidemiologist </li></ul></ul><ul><ul><li>Biostatistician </li></ul></ul><ul><ul><li>If you can help in any way </li></ul></ul><ul><ul><li>[email_address] </li></ul></ul>
  23. 32. Thank you for listening! <ul><li>Any questions? </li></ul>