A hybrid (event-based and indicator-based) platform designed to streamline the collaboration between domain experts and machine learning algorithms for detection, prediction and response to health-related events (such as disease outbreaks or pandemics). The platform helps synthesize health-related event indicators from a wide variety of information sources (structured and unstructured) into a consolidated picture for analysis, maintenance of “community-wide coherence”, and collaboration processes. The platform offers features to detect anomalies, visualize clusters of potential events, predict the rate and spread of a disease outbreak and provide decision makers with tools, methodologies and processes to investigate the event.

1. Photo credit: IRMA (Integrated Risk Management for Africa)

2. Taha Kass-Hout and Nicolas di Tada, Summer 2008, Washington, DC, USA.

9. Reduce Morbidity and Mortality and Improve Health Adopted from WHO

13. Lab Confirmation Detection/ Reporting First Case Opportunity for control Adopted from WHO Response DAY CASES

14. First Case Detection/ Reporting Confirmation Investigation Opportunity for control Response DAY CASES Adopted from WHO

15. Nov 2002 Mar 2003 Progression of outbreak Electronic Surveillance Adopted from Brownstein, et al. Cases of atypical pneumonia Foshan Nov 16th Infected Chinese Doctor Hong Kong hotel Feb 21st 305 Cases of acute resp Guangdong Province Feb 11th Pharma report Guangdong Province November 27 Media reports Guangdong Province Feb 10 Astute physician on ProMED Feb 10 Initial WHO Report Feb 25 Official WHO Report March 10

17. News articles Alerts Disease reports

18. 9/20, 15213, cough/cold, … 9/21, 15207, antifever, … 9/22, 15213, CC = cough, ... 1,000,000 more records… Huge mass of data Detection algorithm Too many alerts Duplicative and uni-directional channels Uncoordinated response

22. 9/20, 15213, cough/cold, … 9/21, 15207, antifever, … 9/22, 15213, CC = cough, ... 1,000,000 more records… Huge mass of data Feedback loop Fewer and more actionable alerts Effective and coordinated response Multi-directional communication

23. Feature extraction (including geo-location) Tags Comments Location Flags/Alerts/Bookmarks Environment Factors Animal Health Factors Remote Sensing Event Classification and Detection Previous Event Training Data Previous Event Control Data Metadata extraction Other reference information Machine learning Show event characterizations Social network Other inferred information … Professional network feedback Professional feedback Anomaly detection Multiple data streams (multi-lingual) User-Generated and Machine Learning Metadata Existing Social Network (e.g., Comm. of interest) Riff Bot

25. Kass-Hout and di Tada: Best Poster Award for Improving Public Health Investigation and Response at the Seventh Annual ISDS Conference, December 3-5, 2008 at the Raliegh Conference Civic Center. http://kasshout.blogspot.com/2008/12/best-poster-award-for-improving-public.html and http://www.isdsjournal.org/article/viewArticle/3308

26. Search: _____ {tag Cloud} Terms tagged by human collaborators or source {Event Tag cloud} X Diarreha X Cholera X Influenza X Respiratory lllness X Fever [Show me unusual distributions]

29. Filters Item (e.g., disease report, news article, alert) summary and location (s) Tag cloud Subscriptions SMS alerts Ratings, comments, alerts, flags Tags (automatic + humans classification) Thread (related Items)

36. Tracking the Avian Influenza Outbreak in Egypt (reports started to appear late January 2009).

45. Taha Kass-Hout, MD, MS http://kasshout.blogspot.com Nicolás di Tada [email_address] Riff http://riff.instedd.org [Software: http://code.google.com/p/riff-evolve Code license: GNU General Public License v3, Content license: Creative Commons 3.0 BY-SA] Cambodia, Photo taken by Taha Kass-Hout, October 2008 “ this pic says it all- our kids are all the same- they deserve the same ”, Comment by Robert Gregg on Facebook, October 2008

47. Kass-Hout and di Tada: Best Poster Award for Improving Public Health Investigation and Response at the Seventh Annual ISDS Conference, December 3-5, 2008 at the Raliegh Conference Civic Center. http://kasshout.blogspot.com/2008/12/best-poster-award-for-improving-public.html and http://www.isdsjournal.org/article/viewArticle/3308

66. Cough [13 of 130] If Item has: Runny Nose [20 of 130] Fever [23 of 130] Then tag it with: Flu [10 of 130] Admin configures a new inference: User sees a suggestion for a new item: System will analyze the existing tagged Items and find out the probability of an item been a flu given that it has cough, runny nose and fever. Flu [85% confidence because of cough, runny nose and fever] Influenza [55% confidence because of cough and headace] Tags inferred

67. Cough Longitude Latitude Fever 3 items clustered because of its proximity and similar symptoms Note: This is actually done in a n-dimensional space, n being the number of tags available, plus the number of relevant words detected, plus a possible spatio-temporal dimension Time

70. P(malaria) = 22% P(influenza) = 13% P(other ILI) = 33%

72. cold fever

76. Φ : x -> φ ( x ) Map to higher-dimension space

77. Classifier Document 1 Document 2 Document 3 Positives Negatives Training Document Training Document

81. Probability of disease A (flu) once symptom B (fever) is observed Probability of fever once flu is confirmed Probability of flu (prior or marginal) Probability of fever (prior or marginal)

83. Hidden Layer Output Layer Input Layer […] […] {I 0 ,I 1 ,……I n } {O 0 ,O 1 ,……O n } Weight

84. Event?

87. Fitness = 1/Area

89. (4, 5 ,6, 3 ,5) (4,3,6,2,5) (5,3,4,6,2) (2,4,6,3,5) (4,3,6,5,2) (2,3,4,6,5) (3,4,5,2,6) (3,5,4,6,2) (4,5,3,6,2) (5,4,2,3,6) (4,6,3,2,5) (3,4,2,6,5) (3,6,5,1,4) ( 5,3 , 2,6,5 ) ( 3,4 , 4,6,2 ) ( 5,3 , 2,6,5 ) ( 3,4 , 4,6,2 )

98. Internet search for allergies and ragweed search terms increase in the spring , and allergy and pollen search terms increase significantly in the fall . It would also appear that Texas and Oklahoma are leading locales for ragweed. Source: Mostashari F. Can Internet searches provide useful data for public health surveillance?. Advances in Disease Surveillance 2007;2:209

99. A search for the term “leptospirosis” in the United States finds dramatically higher search rates from Honolulu, Hawaii, consistent with the epidemiology of the illness in the United States (more than half of all national cases are reported from Hawaii). Source: Mostashari F. Can Internet searches provide useful data for public health surveillance?. Advances in Disease Surveillance 2007;2:209

100. Internet search for “contact lens” increased in Singapore in February 2006, prior to the notification from CDC of the first US cases of contact lens-associated Fusarium keratitis in March 2006, and prior to widespread news coverage in April 2006. Source: Mostashari F. Can Internet searches provide useful data for public health surveillance?. Advances in Disease Surveillance 2007;2:209

101. Following large anti-war protests on the Mall in Washington DC in late September 2005, multiple environmental sensors watching for bioterror events detected the presence of Francisella tularensis . Interestingly, queries appear to have increased prior to discovery of the sensor findings by public health officials on September 30 th . Source: Mostashari F. Can Internet searches provide useful data for public health surveillance?. Advances in Disease Surveillance 2007;2:209

102. While uncommon words like “croup” readily reveal the expected seasonal pattern of Internet search, more common words like “cough” or “throat” require logical modifiers to rule out more common search phrases. Source: Mostashari F. Can Internet searches provide useful data for public health surveillance?. Advances in Disease Surveillance 2007;2:209