This document discusses how new technologies like crowdsourcing, social networks, and citizen science can enhance biosurveillance and public health through big data. It provides examples like Ushahidi and Google Flu Trends that track diseases. Mobile games and heat maps using machine learning can gather health data. Platforms like InSTEDD and data journalism sites are proliferating. Future challenges include scaling coordinated data sharing for public good while ensuring accuracy, privacy, and governance over this new "data commons". Organizing dynamic health surveillance will require more flexible collaboration beyond traditional control models.

1. Biosurveillance 2.0: Big Data, Social
Networks, Citizen Science and
Collaboration for Public Health
Jody Ranck, DrPH
Sr. Digital Health Advisor, PwC
February 25, 2013

2. Crowdsourcing: Ushahidi

3. Google Flu Trends Flubreaks

4. Dengue: Pakistan

5. Tracking Platforms proliferating

8. Mappy Health

10. Citizen Science

11. Citizen Science/Bio Art

12. Mobile: Game dynamics, data,
crowdsourcing, engagement

13. Heat Maps: Twitter Sentiment Analysis
using Machine Learning

14. InSTEDD

15. Big Data/Social Business

16. Data Journalism: Platforms

17. Data Journalism

18. Data Challenges

19. Open Source Data Analytics

20. BigML (predictive analytics)

21. UN Global Pulse:

22. Fukushima: Citizen science turning
point

23. Fukushima: implications
• Maps triggered engagement, created
networks
• Changed information landscape
• Co-production of expertise—no more one-way
streets
• Online maps act like web applications—
participatory culture of science and tech
• Data bricolage, remix—messiness and debates

25. Future
• How to scale and coordinate data for public
good
• Algorithms and improving accuracy, timeliness
• PPP model: Global Data Alliance
• New data markets and possibilities for new
products/services derived from a data
commons
• New publics and new politics of data and
health

26. Organizational Challenges
• Managing Transboundary Crises (Ansell et al)
& system of systems
• Data triaging, management, integration,
governance
• Beyond traditional resource capacity to more
dynamic, combinatorial, flexible ways of
organizing
• Resilient systems, trust, bottom-up—beyond
command and control epidemiology