RIFF - A Social Network and Collaborative Platform For Public Health Disease Surveillance
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The document discusses public health disease surveillance and syndromic surveillance. It describes how public health surveillance involves ongoing collection and analysis of health data to support public health programs and prevention/control efforts. Syndromic surveillance monitors pre-diagnostic health data to identify potential cases/outbreaks requiring a public health response. The document advocates adopting a social and collaborative decision-making approach to facilitate early identification and assessment of potential health threats in order to recommend control measures.
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RIFF - A Social Network and Collaborative Platform For Public Health Disease Surveillance
- 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
Notes de l'éditeur
- Old ideas: Crows recognized for divination in Roman times: A crucial component of the US West Nile Virus control program New technologies must: Bring multiple disciplines together Offer a collaborative and Open source model OUR MODEL: Commercial models rely on competition to drive innovation. Their tools fail at the edge where there is no market to drive success. Non-profits know the “edge” challenges, but lack the resources for technical innovation We recognize our success will be measured by effective adoption at both the edge and the center. And it has to be open-source and free. We’ve decided to rely on environmental forces (rather than a market) to drive innovation. And it works.
- Our track record: HIV pandemic Rift valley fever FMD pandemic West Nile Virus in the US SARS Monkeypox No room for complacency!!!
- Early detection of disease outbreaks is the holy grail of public health, and has now also become a crucial issue for governments facing the threat of bioterrorism. OUR BIG PICTURE: We want to help people detect things early, connect people with each other so they can respond sooner
- It is not necessarily lack of information… we have a lot of information… rather, can we put the information into intelligence (or context) in a timely manner? Multiple streams include the following- say something about why you need to stitch multiple sources together... How do you put an event into context? And, where is the next disease is going to emerge from... that is the holly grail in this business... Dead crows on the streets of NYC Pepto-bismol disappearing from the shelves of grocery stores Phone calls from citizens and the media to the health department about increased absenteeism from schools and businesses Increased Internet search hits on certain terms per week Image Source: Dead Crow: http://www.birds.cornell.edu/crows/images/deadcrow.jpg Empty Shelves: http://farm3.static.flickr.com/2029/2239605500_6ef2fd2295.jpg?v=0 Sidebar: 5/50 rule, in 5 years time, 50% of all content will be user-generated: (Reference: The Podshow by Ron Bloom (http://www.ronbloom.com/?p=11) 60% content has geo-spatial and temporal aspects… Image Sources: Wikipedia: http://www.citris-uc.org/system/files/imce-u10/Wikipedia-logo.png Blogger: http://z.about.com/d/weblogs/1/5/V/-/-/-/BloggerHomePage.PNG OpenMRS: http://ruddzw.files.wordpress.com/2007/05/openmrs_osx.png Remote Sensing: http://www.medscape.com/content/2000/00/41/47/414717/art-e0603.01.fig2.jpg Cell phone/iPhone; http://healthinformaticsblog.files.wordpress.com/2008/03/iphone-denticon-patient-thumb.jpg WhoIsSick.org: http://gmapsmania.googlepages.com/whosickgmm.JPG
- Indicator-based Surveillance: Computation of indicators upon which unusual disease patterns to investigate are detected (number of cases, rates, proportion of strains…) Lack of infrastructure Low level training Gaps in coverage Poor information flow Event-based Surveillance: The detection of public health events based on the capture of ad-hoc unstructured reports issued by formal or informal sources. Abundant cheap/free resource Detailed local information Near real-time reporting Less susceptible to political pressure Novel data sources: Online news, chat rooms, blogs, articles, multimedia Remote sensing: Algal blooms can be used to monitor the threat of cholera (e.g., Southern Baltic Sea)
- Proportion of infection detected… Control confounding effects by: Including more than the demand side (Internet search query) but also the supply side (e.g., information on news websites) Link to Healthmap.org or GPHIN Including longitudinal data on health information supply Including accurate geographic distribution Infodemiology: Develop methodology and real-time measures (indices) to understand patterns and trends for general health information Understand the predictive value of what the community of practice is looking for ( demand ) for early detection of emerging diseases, infectious disease outbreaks, or bioterrorism Identify and quantify gaps between between information supply and demand Discover and and validate predictive metrics Could an X number (threshold) of Internet search hits on fever per week trigger a flu-outbreak?
- Timeliness… We could potentially observe the progression of a disease outbreak within a population at multiple touch points (data) Some of these data may be collected before visits to the physician or hospital have actually happened Patients might search the Internet on symptoms they’re experiencing Patients might adjust their diet when they feel ill (such as drinking more water, juice, and have more rest) If the symptoms become more severe, patients might seek over-the counter (OTC) medicine, and miss classes or work In many cases, patients might go to work late or leave for home early Patients might also experience subtle change of their behavior at work When the symptoms continue, patients might seek help from physicians (e.g., schedule appointments, present with chief complaints, lab tests ordered, medicines prescribed) Similar models can also be established for pollution, non-infectious diseases, chronic diseases, injury, and natural disasters
- There is currently NO turnkey solution to this problem… You have to involve humans and provide a collaborative environment for these people to work together… and we’re adopting a web 2.0/3.0 approach to pull everything together: In the Pepto Bismol example, the most interesting aspects of this event was that the majority of the victims did not seek medical attention at first. The Milwaukee Health Department in 1993 became aware of widespread gastrointestinal illness in the community through phone calls from citizens and the media. There was increased absenteeism from schools and businesses, and groceries and pharmacies reported depletion of anti-diarrheal medications. In an event like this, a human expert could associate certain indications and arrive at a conclusion or a few hypotheses to corroborate or refute an event: There have been unusually heavy rains for the last few weeks The Water authority has received several complaints about cloudy water from customers Now we have all these calls and concerns from the community So perhaps I should lean towards a waterborne hypothesis vs. something else… the human eye can also quickly detect a cluster of pins on a map over time and space and make certain assumptions… As we’re faced with a cross-disciplinary problem (human, animal, environment, organisms, etc.) it becomes more clear that we need to offer a collaborative space for experts from multiple fields to work together on solving the problem Back when I was in the trenches of SARS, we found out very quickly the importance of crowdsourcing and the need to share certain types of data quickly
- Social distance can be more important than the geographic distance Networks can be incrementally developed and don’t need defined a priori Contradictory assumptions can be investigated in parallel (alternate hypotheses for causes, case definitions, etc) Items can be merged if duplication is discovered, or split if needed Each change to an element may trigger notification to users, and business logic Workflow assumes that actions be taken within specific time windows or else additional actions will be triggered Practically every item can be “tagged” by users with notes and supplementary data Users will communicate and collaborate through existing communication channels as much as possible Auditing of each step allows users to “back up” characterizations of health events through their history as well as a wide set of potential metrics for evaluating the processes involved in biosurveillance
- Social distance can be more important than the geographic distance Networks can be incrementally developed and don’t need defined a priori Contradictory assumptions can be investigated in parallel (alternate hypotheses for causes, case definitions, etc) Items can be merged if duplication is discovered, or split if needed Each change to an element may trigger notification to users, and business logic Workflow assumes that actions be taken within specific time windows or else additional actions will be triggered Practically every item can be “tagged” by users with notes and supplementary data Users will communicate and collaborate through existing communication channels as much as possible Auditing of each step allows users to “back up” characterizations of health events through their history as well as a wide set of potential metrics for evaluating the processes involved in biosurveillance
- Health Information Service (HIS) Metadata definitions Augment data with additional attributes (e.g., location, date, key words, related terms, video, images) Provide a markup language: GHML (Google Health Markup Language) based on national and international standards which describes the data and extends its meaning Provide a set of APIs and metadata that can support the following features: Search Visualization Collaboration Situational awareness Analysis Alerts Enhance accuracy, reliability, validity and utility by allowing the community of practice to augment the data Allow users to tag data of interest to further refine its meaning Allow users to link and share data that can be used by others (collaboration) Provide publish-and-subscribe functionality (RSS, GeoRSS, SSE, REST…) Allow users to invoke "health agents“
- 1- Information gets collected from different sources 2-Information gets decorated with different layers of data, like remote sensing information about temperature, humidity or terrain. 3-Machine learning modules classify the articles in the system, determining location, name of diseases, symptoms or syndromes, extracting structured data like epidemiological numbers of suspected or confirm cases. 4-Experts from different disciplines collaborate around the information, creating comments, tagging, relating articles and correcting or training machine-learning algorithms. 5-Experts can use different visualizations and filtering tools, to explore the body of evidence as the event unfolds over time and space and create hypothesis of events that they can discuss or refine with their team members and decide whether they think that a field investigation is needed. 6-Field staff can collect and report information that gets incorporated back to the system.
- 1- Information gets collected from different sources 2-Information gets decorated with different layers of data, like remote sensing information about temperature, humidity or terrain. 3-Machine learning modules classify the articles in the system, determining location, name of diseases, symptoms or syndromes, extracting structured data like epidemiological numbers of suspected or confirm cases. 4-Experts from different disciplines collaborate around the information, creating comments, tagging, relating articles and correcting or training machine-learning algorithms. 5-Experts can use different visualizations and filtering tools, to explore the body of evidence as the event unfolds over time and space and create hypothesis of events that they can discuss or refine with their team members and decide whether they think that a field investigation is needed. 6-Field staff can collect and report information that gets incorporated back to the system.
- Saved filters with subscriptions List, Grid or Map views -Tags -Related items Publish and share information through RSS feeds
- And of course, you can combine filters by tags, with filters by region or any other property that the article has in the system.
- Hurdles to be overcome Diagnostics – limited availability Data collection – limited capacity Partial coverage – the black holes are getting larger Inconsistent definitions and quality of data Incompatible reporting systems and stove piping of information Political filters Technical: Collaboration: Commenting Capability Notification via a “publish and subscribe” capability Shared group definitions and calendars Shared access to key artifacts Support for Mobile devices (e.g., SMS) and VOIP Organizational – China might not want to share information, others might not want to..lots of policy, etc. required… Evaluation Framework: Overall measures (situation awareness and shared mental model) Individual processes measures Network parameters: Which automated systems generated the most reliable alerts, and for what types of conditions? Which human users where the most effective in identifying conditions? Which indicators are the most effective in identifying a health event? Which elements of the biosurveillance lifecycle require the most time and/or collaboration? The network history will provide a common point of evaluation for a variety of surveillance and response techniques System Evaluation: System description Purpose (detection- and information-based) Stakeholders Operations Health-related event detection Timeliness Validity Validation approach Statistical assessment of validity Data quality System experience System usefulness Flexibility Acceptability Portability Stability Costs Sustainability
- To recap, The human experts interacting with automated systems The collaborative decision making environment We are sure one day soon we will have an EID (Emerging Infectious Disease) impact assessment... just like there is an environmental impact assessment…
- E. coli Norwalk-like virus Salmonellosis Dengue fever Herpes Cholera Gastroenteritis Pertussis Rift Valley fever C. difficile Staphylococcal disease Diarrhea Legionellosis Tuberculosis Malaria Chickenpox Measles …