Biosurveillance 2.0

2009, AMIA Spring Congress

2009-06-01T09:39:00-07:00

Last week, I presented Evolve; InSTEDD's Global Early Warning and Response System, at the 2009, American Medical Informatics Association (AMIA) Spring Congress. The conference took place at the Walt Disney World Swan, May 28th–30th, in Orlando, Florida, USA. Here is the presentation:

View more OpenOffice presentations from Taha Kass-hout.

Related Links:

Influenza A(H1N1) Media Hype: Mid-March 2009 thru May 19, 2009
Tracking A(H1N1) using Evolve
Low volume Evolve announcements on Twitter
Extremely Affordable Health Innovations
MBDS ICT and Technology Forum
Best Poster Award for Improving Public Health Investigation and Response at the Seventh Annual International Society for Disease Surveillance Conference
Collaborative Analytics and Environment for Linking Early Event Detection to an Effective Response

Influenza A(H1N1) Media Hype: Mid-March 2009 thru May 19, 2009

2009-05-21T21:56:00-07:00

Data source: A(H1N1) Evolve Collaborative Workspace

Note: Tag cloud was created on Many Eyes © IBM

Note: Tag cloud was created on Many Eyes © IBM

Note: Map was created on Many Eyes © IBM

Related Links:

Tracking A(H1N1) using Evolve
Low volume Evolve announcements on Twitter
Extremely Affordable Health Innovations
MBDS ICT and Technology Forum
Best Poster Award for Improving Public Health Investigation and Response at the Seventh Annual International Society for Disease Surveillance Conference
Collaborative Analytics and Environment for Linking Early Event Detection to an Effective Response

Oswego in the Cloud: US CDC Recommends Mesh4x to Synchronize Data using Epi Info™

2009-05-16T14:31:00-07:00

Enrique Nieves Jr (Acting Director, Division of Integrated Surveillance Systems and Services (DISSS), CDC, NCPHI) and Jay Jones are recommending Mesh4x for "...anyone who needs to synchronize data using Epi Info™". You can see their recent publication in the PanAfrican Medical Journal titled: Epi Info™: Now an Open-source application that continues a long and productive “life” through CDC support and funding.

Related Links:

Epi Info™ and Mesh4x Prototype Demonstration with US CDC
Empowering Epidemiologists to Share Information, Anytime, Anywhere: Epi Info™ and Mesh4x
Epi Info™ current website on CDC.gov (available for download Epi Info™ 3.5.1 (last accessed May 16th, 2009))
Epi Info™ Friends Group on Google (Restricted to invited members. To become a member, contact the group owner at andy (dot) dean (at) gmail (dot) com)
InSTEDD Mesh4X

Tracking A(H1N1) using Evolve

2009-05-14T21:44:00-07:00

Last week, InSTEDD's CEO; Dr. Eric Rasmussen, blogged about the Evolve workspace we stood up earlier to further aid experts and responders collaborating around emerging reports related to the 2009 A(H1N1) pandemic influenza. To-date, there's been massive news coverage around the event, but in order to make sense of it all, a group of experts (with a backgrounds in public health, international relations, diplomacy, social work, and emergency response) volunteered their time to collaborate around the various streams of information (listed below).

One by-product of this ongoing effort is that the information is appropriately tagged and geo-located. The A(H1N1) workspace allows you to subscribe to all the information or to a filter of your own. The tags include information beyond just a disease category, symptom, or syndrome, but we also tried to capture other important information; such as policy issues (e.g., vaccination, school closure, travel advisory, etc.). However, our primary goal is not to become another information source, rather to be able to provide a good situational awareness of the event in order to respond effectively. We are trying to address the following problems that are inherent in the current early detection systems:

Classic problem: too much data, not enough information. Why aren’t the key indicators noticed earlier?
Noisy data—low reliability—
Need to keep the human in the loop (a lot of this is still an art).
Sources not always obvious - we saw emerging sources of information, like the citizen reporting over a Google map (in the past much attention was paid to sources like Internet search queries (e.g., Eysenbach, Ginsberg, Polgreen, Hulth, and Cooper), over the counter medications sales (e.g., Wagner/RODS Lab), absenteeism (e.g., Wagner/RODS Lab), as opposed to ER chief complaints or routine disease surveillance hierarchical systems).
Threat profile keeps changing- is not known for something like a SARS/SARI (e.g., Swine Flu or other things like it).
Political and Organizational Boundaries (Note that we weren't able to perfectly communicate the first indications of SARS, or the outbreaks of H5N1 in China, and we saw that happen again with the H1N1 Swine outbreak).
Need to set up the need for shared collaboration spaces and geographic distribution. Not only do we need human experts in the loop, but they need to share the hunches and concerns. Discussion needs to identify communities of interest. Different specialists are involved based on the nature of the threat.

I particularly applaud the effort by HealthMappers (Dr. Brownstein, et al) for quickly putting together an A(H1N1) mashup (or the New England Journal of Medicine HealthMap) which tracks the cases (confirmed, suspect, dead, or ruled out) alongside with the informal sources that HealthMap continuously monitors and moderates. HealthMap and BioCaster also setup moderated tweets on Twitter that are timely, reliable and of high quality.

Information Sources:

The Challenge Ahead:

While all this is important, I'd like to emphasize the fact that the best data is still coming from old-fashioned shoe leather epidemiology. We have to be true to ourselves and remember that, with all the information, new tools, open networks of collaborators, etc., we still missed the early indication(s) of the A(H1N1) outbreak. I remember when I was in the trenches of SARS back in 2003, we didn't have the breadth and depth of the information nor the tools we currently have. This calls for an action to rethink our strategies around early detection especially for emerging infectious diseases...

Related Links:

Low volume Evolve announcements on Twitter
Extremely Affordable Health Innovations
MBDS ICT and Technology Forum
Best Poster Award for Improving Public Health Investigation and Response at the Seventh Annual International Society for Disease Surveillance Conference
Collaborative Analytics and Environment for Linking Early Event Detection to an Effective Response

Extremely Affordable Health Innovations

2009-05-14T20:27:00-07:00

Nico and I presented Evolve at the “Extremely Affordable Health Innovations”; A Grameen Health initiative in collaboration with World Health Care Congress (WHCC), which took place on April 14th-16th, 2009 in Washington D.C. The winners, by category and overall, are listed here. We won the best poster in the Community Monitoring category. The poster will be on display again on May 131h-141h, 2009 at WHCC Europe in Brussels, Belgium.
Evolve: InSTEDD's Global Early Warning and Response SystemView more presentations from Taha Kass-hout.

Related Links:

Low volume Evolve announcements on Twitter
Extremely Affordable Health Innovations
MBDS ICT and Technology Forum
Best Poster Award for Improving Public Health Investigation and Response at the Seventh Annual International Society for Disease Surveillance Conference
Collaborative Analytics and Environment for Linking Early Event Detection to an Effective Response

MBDS ICT and Technology Forum

2009-05-14T12:16:00-07:00

I presented the following at the Mekong Basin Disease Surveillance (MBDS), Information Communication and Technology Forum which took place April 2nd–3rd, 2009 in Mukdahan Province, Thailand.

I. ICT Developments in Mobile Technology for Global Public Health: InSTEDD Collaboration Tools

View more presentations from Taha Kass-hout.

Photo Credit: Dr. Moe Ko Oo MBDS Coordinator

II. Using GeoChat for a Cross-Border Field Simulation Exercise

III. MBDS SE Asia Collaborative Early Warning & Response workspace (using Evolve)

View more presentations from Taha Kass-hout.

At the end of the forum, Ed and I answered questions related to the utility and feasibility of GeoChat and Evolve in the various MBDS member countries.

Photo Credit: Dr. Moe Ko Oo MBDS Coordinator

Official google.org Blog: New resources for non-profit and humanitarian mapping

2009-05-13T09:29:00-07:00

Official google.org Blog: New resources for non-profit and humanitarian mapping

InSTEDD Collaboration Technology for Public Health and Humanitarian Action and Global Development

2009-03-02T13:16:00-08:00

I gave a talk today on InSTEDD collaboration technologies and platform at the CDC Focus On Users: Underserved Populations Conference, March 2-3, 2009. This conference is co-sponsored by CDC's National Center for Health Marketing, the U.S. Department of Health and Human Services, Georgia State University Department of Communication, the Pew Internet & American Life Project, and the National Public Health Information Coalition.

Collaboration Technology for Public Health and Humanitarian Action and Global Development.

View more presentations from Taha Kass-hout. (tags: kass-hout tools)

Open Collaboration Experiment around Emergency Alerts, Disasters, Market Recalls, and More...

2009-03-01T15:43:00-08:00

We set up two open collaboration spaces around up-to-date notifications and alerts on the following:

- Emergency Preparedness & Response alerts, Recent Outbreaks and Incidents, Clinician Outreach and Communication Activity (COCA), and Public Service Announcements for Hurricanes from US CDC

- Recalls, market withdrawals and safety alerts from the the U.S. Food and Drug Administration (FDA)

These spaces offer a suite of collaborative features, including: commenting, tagging (adding your own keywords), mapping (user generated and automated), relating multiple alerts, searching and filtering, specifying a time window, adding attachments, subscribing (currently in the form of a web-friendly format also known as RSS, but in the very near future we will support email subscription), and more. The two spaces are also equipped with an intelligent process (referred to as machine-learning algorithm) that "learns" from inputs provided by the users (e.g., adding a keyword (tag), or correcting the mapping of an item). This intelligent process quickly and accurately learns from users inputs (with 95% confidence based on previous tests) and starts suggesting tags as well as correcting itself and offering even better results over time (by simply accepting or rejecting a suggested tag or an automatically mapped location).

Have you an alert or item you like to share with the community? You can easily contribute that alert by clicking the "Add Item" feature.

We invite you to try it and help us spread the good word by inviting others as well. We look forward to your feedback and comments in order to make this tool as useful as it can possibly be. If you have thoughts or questions you can contact us at info@instedd.org or email me directly at: kasshout@instedd.org.

Atmospheric CO2 Levels: Green, Orange, Red...

2009-02-17T01:25:00-08:00

Since 412,000 BCE, atmospheric CO2 levels ranged; in a cyclical motion, from 182-299 ppmv. CO2 levels started an upward trend after 1604 and rose above the maximum historic threshold of 300 ppmv in 1912. A most recent recording of CO2 level from the Mauna Loa Observatory was at 381.9 ppmv in 2006.

I assembled the data in a Google Doc from the following sources:

Recent CO2 records:
- Mauna Loa Observatory atmospheric CO2 Data [1958 - present]

Historical paleoclimatic CO2 records:
- Law Dome, Antarctica DE08, DE08-2, and DSS ice cores [1006 - 1978]
- Vostok ice core Data in thousands of years BCE [-412,080 - -337]

Internet Search Queries on CO2 Levels (Source Google Insights for Search):

Internet Search Queries on Global Warming (Source Google Insights for Search):

Acknowledgments:
- Rafal Raciborski, Ph.D. candidate, Department of Political Science, Emory University - for scaling the x-axis

Thanks for reading my blog last year!

2009-01-22T19:08:00-08:00

World Health-Related Events

2009-01-08T18:45:00-08:00

Data is aggregated and moderated on routinely basis using InSTEDD's Collaborative Early Event Detection and Response space. Sources of information include the following (in alphabetical order):

- BBC Outbreak News

- BioCaster Open Source Ontology

- CNN Health News

- EpiSpider

- Eurosurveillance

- Google Outbreak News

- HealthMap (Global Disease Alert Map)

- HEDDS Surveillance News

- Moreover Public Health News

- ProMED News (including MBDS)

- Veratect Corporation

- WDIN Disease Map Digest

- WHO Latest news on the avian influenza situation in humans around the world

- WHO Outbreak News

- Y! Health Cold & Flu News

Related Links:

Note: Tag cloud was created on Many Eyes © IBM (click here for a live version)

Best Poster Award for Improving Public Health Investigation and Response at the Seventh Annual ISDS Conference

2008-12-22T21:38:00-08:00

Earlier this month, I presented our vision for an Integrated Global Early Warning and Response System at the Seventh Annual International Society for Disease Surveillance Conference, December 3-5, 2008 at the Raliegh Conference Civic Center. We received the most votes in the "Best Poster" award category for the "Improving Public Health Investigation and Response" track. You can also download the article abstract from here.

Related Links:

Epi Info™ and Mesh4x Prototype Demonstration with US CDC

2008-12-22T17:46:00-08:00

On Thursday, December 18th, 2008, we gave a joint theater-style demonstration of synchronizing Epi Info™ Data using Mesh4x with the Division of Integrated Surveillance Systems and Services (NCPHI/DISSS) at US CDC.

[The Epi Info™ team: David Nitschke (lead) (left), Roger Mir (middle) and Mark Berndt (right). According to our imaginary scenario (where we extended the sample Oswego outbreak from 1940), David is the NY State epidemiologist, Roger is the Oneida county Medical Officer, and Mark is the CDC epidemiologist]

Here is a presentation and script scenario which walks you through the scenario step-by-step. You can download the latest Mesh4x tool from here, which also includes the sample data. [If you do not already have a copy of Epi Info™, you can download it from here]

Epi Info™ ─ Mesh4x Synchronization Prototype

View SlideShare presentation or Upload your own. (tags: technology david)

Oswego in the Cloud: Scenario Script

View more documents from Taha Kass-Hout.

During this proof-of-concept, we showed the utility of a Mesh4x tool for synchronizing Epi Info™ data over the cloud (web) and SMS (Please see my previous blog in which I introduced this effort: "Empowering Epidemiologists to Share Information, Anytime, Anywhere: Epi Info™ and Mesh4x"). Epi Info™ is now available as an Open Source project (Please see Official US CDC MMWR release notice and the most recent Government Health IT article: CDC takes its epidemiological software open source).

[The Epi Info™ ─ Mesh4x Synchronization Tool]

[Synchronization over over the Amazon EC2/S3 cloud (State’s available online data in the scenario)]

The Synchronization over SMS using cell phones provides great potential for sharing data among field epidemiologists conducting investigations in areas with limited resources and infrastructure, especially in austere conditions (e.g., during or after disasters).

[Synchronization over SMS]

As part of the scenario, we showed how to share maps across various investigators (in the scenario Oneida county and the neighboring counties). This is especially true as the epidemiologic investigation in underway, that data is shared in aggregate forms; such as a map with a few pins, before further collaboration. During the demonstration, we used Google Earth as the viualization tool to show the various cases (Ill) and no cases are distributed across space (accurately geocodesd) and time (through a time slider). We integrated Google geocoder with the Mesh4x tool to automatically geocode the sample physical addresses and we provided means in the Mesh4x tool to automatically generate and synchronize the maps across the various counties.

Here I show the maps before synchronization of data (one map for Oneida county (highlighted in pink) on the left and the rest of the counties on the right). After the synchronization was completed, both maps were identical as the counties now have similar data.

After the demonstration, we identified with US CDC a high priority list for next steps, including:

Preview/Accept/Reject or "Undo" & Conflict Resolution
Schema/view update and propagation
Mesh-based authentication & authorization
Specify multiple tables to sync
SMS-to-cloud and back
Client to define mesh, feeds, mappings
Privacy & Signatures

[Brainstorming priorities with the Epi Info™ team (from left to right): Roger, Mark and David. Ed facilitated the discussion as you see him going through the projected list]

DISSS is currently seeking a project to continue development of this functionality pending the availability of resources. I'll keep you posted!

We are very grateful for the time and expertise US CDC offered us during this exercise and we wish to further enhance our tools and platform as a result of this effort. I want to personally thank the Epi Info™ team: David Nitschke (lead), Roger Mir and Mark Berndt, and US CDC National Center for Public Health Informatics (or NCPHI) leadership team: Dr. Leslie (Les) Lenert (Director) and Enrique Nieves (DISSS Division Director (Acting)).

[The NCPHI leadership team: Les (left) and Enrique (right)]

I also want to acknowledge my colleagues at InSTEDD who worked really hard over the past six weeks to put this together and to see it succeed, including work done at very early odd morning hours: Juan Marcelo Tondato, Daniel Cazzulino, Pablo M. Cibraro, and Eduardo (Ed) Jezierski.

Finally, on Behalf of InSTEDDers, I want to wish you Happy Holidays and a Happy New Year!

Useful Links:

Oswego in the Cloud: Scenario Script
US CDC Recommends Mesh4x to Synchronize Data using Epi Info™
Empowering Epidemiologists to Share Information, Anytime, Anywhere: Epi Info™ and Mesh4x
Epi Info™ current website on CDC.gov (available for download Epi Info™ 3.5.1 (last accessed May 16th, 2009))
Epi Info™ User Forum
Epi Info™ Community Edition (CE)
Epi Info™ Friends Group on Google (Restricted to invited members. To become a member, contact the group owner at andy (dot) dean (at) gmail (dot) com)
Epi Info™ in Italy
Epi Info™ in Brazil (Portuguese)
Epi Info™ in Spain
InSTEDD Mesh4X
InSTEDD Mesh4x Discussion Group
Juan Marcelo Tondato's Blog on Mesh4x
Eduardo Jezierski's Blog on Mesh4x
Taha Kass-Hout's Blog on Mesh4x
Daniel Cazzulino's Blog on Mesh Architecture
Pablo M. Cibraro's Blog on Mesh Architecture
Public Health Grid (PHGrid) - Research & Development

Life expectancy in USA and the 1918 Pandemic Influenza

2008-12-20T21:06:00-08:00

The 1918 dip was caused by 1918 Influenza Pandemic

I created a Google Doc here (Data source is from the National Vital Statistics, Vol 50, No. 6.)

Empowering Epidemiologists to Share Information, Anytime, Anywhere: Epi Info™ and Mesh4x

2008-12-11T22:38:00-08:00

I’m leading a collaborative project with US CDC to establish a proof of concept demonstrating the potential to synchronize data in disparate Epi Info™ installations over the cloud and Short Message Service (SMS) text messages using the tools and libraries of the Mesh4X project. The Epi Info™ team includes: David Nitschke (lead), Roger Mir and Mark Berndt. The InSTEDD team includes: Juan Marcelo Tondato, Daniel Cazzulino, Pablo M. Cibraro, and Eduardo (Ed) Jezierski.

[The Epi Info™ team: Roger Mir (left) and David Nitschke (lead) (right)]

US CDC Epi Info™ is a suite of tools for use by public health professionals in conducting outbreak investigations, managing databases for public health surveillance, and general database and statistics applications. With Epi Info™, physicians, nurses, epidemiologists, and other public health and medical workers can rapidly develop a questionnaire, customize the data entry and validation process, enter and analyze data. Epi Info™ offers adaptability to changing requirements, growing demands, and innovative and scalable public health solutions. Its language and localization features make it portable for national and international missions and events. And, it’s FREE, so developing countries with limited resources can also employ its power. Epi Info™ is now available as an Open Source project [Please see Official US CDC MMWR release notice].

While Epi Info™ is widely used around the world, its implementations have been limited to discrete stand alone applications with no collaborative, peer-to-peer exchange of data or internet connectivity. Data are exported and sent as discrete packets (databases or spreadsheets) to collaborating centers where the data are merged and analyzed. This is a time consuming process and presents a significant limitation especially during an outbreak investigation. This was an opportunity for us to work collaboratively with the US CDC National Center for Public Health Informatics (or NCPHI), directed by Dr. Leslie (Les) Lenert, and demonstrate the value of Mesh4x to meet this challenge.

Mesh4X is a light-weight synchronization platform developed by InSTEDD which provides libraries, tools and applications to simplify interoperability of different applications and services. Ed just posted a blog on the progress of Mesh4x and its various and interesting properties. During this prototype, we developed an adapter for Epi Info™ enabling near real-time data synchronization using the fastest available technology (Internet, wireless Internet, satellite communication, SMS, or flash drive/pen drive) that we believe to be of significant value to the public health community at large.

An example scenario where Mesh4x would be useful is in an outbreak investigation. Many epidemiologists are familiar with the food borne outbreak in Oswego, New York, U.S.A. on April 18^th, 1940. In this outbreak, 75 of the 80 people known to have been present at the pot-luck church supper. A survey was created and interviews were conducted with participants to determine the source of the contamination. While the Oswego study focused on a single region, the significant value of data synchronization can be seen by expanding this scenario to where interviews and data entry are conducted in different localities. Therefore, we recreated the outbreak as if the Oswego church supper was attended by residents of the Oswego county and four other neighboring counties: Jefferson, Lewis, Oneida, and Wayne. In this hypothesized scenario, we imagined two epidemiologists are investigating this outbreak; one investigating the outbreak in Oneida county and the other investigating the other counties. Prior to synchronization, Oneida county had inconclusive results on the cause of the outbreak (baked ham and Vanilla ice cream). After data synchronization, both investigators had a clear picture of the spread of the illness over space and time and concluded the actual source of the outbreak to be from the Vanilla Ice Cream prepared the night before the church supper on April the 18^th. During this scenario, we also demonstrated synchronizing Google Earth maps between localities. We will be demonstrating the solution at the US CDC offices in Atlanta, GA next Thursday December 18^th, I’ll keep you posted!

[Roger and David working on the User Interface]

Related Links:

US CDC Recommends Mesh4x to Synchronize Data using Epi Info™
CDC/InSTEDD Collaboration blog
Epi Info™ current website on CDC.gov (available for download Epi Info™ 3.5.1 (last accessed May 16th, 2009))
Epi Info™ User Forum
Epi Info™ Community Edition (CE)
Epi Info™ Friends Group on Google (Restricted to invited members. To become a member, contact the group owner at andy.dean@gmail.com)
Epi Info™ in Italy
Epi Info™ in Brazil (Portuguese)
Epi Info™ in Spain
InSTEDD Mesh4X
InSTEDD Mesh4x Discussion Group
Juan Marcelo Tondato's Blog on Mesh4x
Eduardo Jezierski's Blog on Mesh4x
Taha Kass-Hout's Blog on Mesh4x
Daniel Cazzulino's Blog on Mesh Architecture
Pablo M. Cibraro's Blog on Mesh Architecture
Public Health Grid (PHGrid) - Research & Development

2nd International Conference on Global Health Applications of Handheld Computing Devices Conference

2008-12-09T15:04:00-08:00

On November 24th, Eric and I presented at the 2nd International Conference on Global Health Applications of Handheld Computing Devices, at US CDC in Atlanta, GA.

Several opening talks reflected on our presence and our tools, including:

1) Leslie (Les) Lenert, Director, National Center for Public Health Informatics, US CDC:

2) Sally Stansfield, Executive Director, Health Metrics Network, WHO, Geneva:

3) Mark Landry, Health Informatics, Office of Global AIDS Coordinator, Department of State, who described InSTEDD as “the best out of the box thinking”

Related Links:

1st PHI Workshop at Mahidol: Collaborations in Public Health Informatics among MBDS and SEAMEO Countries

2008-10-26T13:46:00-07:00

Nico and I were invited to present at the 1st Workshop on Public Health Informatics organized by Center for Excellence for Biomedical and Public Health Informatics (BIOPHICS), Faculty of Tropical Medicine at Mahidol University in Bangkok, Thailand (Sep 29-Oct 10, 2008). The workshop was co-organized by University of Washington and the Rockefeller Foundation. This two-week workshop introduced the discipline of public health informatics to an elite group of public health and information technology experts from the MBDS and SEAMEO countries (Vietnam, Yunnan, Cambodia, Lao PDR, Myanmar, Indonesia, Philippines, Thailand, etc.) The course included topics on the application of latest advancements in information science and technology in supporting public health practice, education and research.

On the morning of Thursday October 9, 2008, Nico and I gave 3 lectures on the application of machine learning for early detection and response, Open Source movement and licensing, and discussed various Open Source applications for public health. We introduced biosurveillance, its current challenges and limitations, and proposed a practical “collaborative approach” for effective early disease detection and response from local, national, and global perspectives.

We then had a lunch break with Assoc. Prof. Pratap Singhasivanon, Dean of Faculty of Tropical Medicine who also supervises BIOPHICS. We discussed with Dr. Pratap InSTEDD’s role in the region and the MBDS, the various projects and technology tools currently underway, the innovation lab model, and our gratitude for extending his invitation to us to present at the 1st PHI course.

We spent the majority of the afternoon in the computer lab where we offered hands-on training on various Open Source tools and projects, their implication to public health, and their role in the MBDS region. We also used this opportunity to introduce InSTEDD’s tools including: Mesh4X, Geochat (Overview, Details and source), Riff and RNA.

At the end of day, we had a quick tour of BIOPHICS and its various state-of-the art projects. We were briefed of BIOPHICS multi-clinical trials center (several million patients), observational studies and disease registry, and had a tour of the BIOPHICS data center. The clinical trials ranged from PK study, Phase I to Phase III studies, and Phase IV monitoring adverse drug reaction (ADR). We were also offered a demonstration of a versatile and mobile patient tracking system. Other services include computerized system development for disease surveillance and medical/laboratory data. BIOPHICS has experts in basic science, health science, public health, medical and information sciences and they offer various consulting services related to applications of genetics statistics, bioinformatics and clinical informatics.

On Friday October 10, 2008, each country had a representative speaker from their group who presented on their country's current challenges in disease surveillance and response, what they learned from the workshop, and how best to apply that to address the current system's limitations and challenges.

We were pleased to see how InSTEDD's tools and “collaborative” approach was mentioned in various discussions and considered to be part of the solution (Picture: The Cambodian team presented on their future solution and suggested using various InSTEDD tools and technologies as part of this solution.)

Afterwards, Nico and I sat on a final panel discussion that addressed participants’ questions with regards to the material presented during the workshop. We received various questions on the role of Geochat and SMS in enhancing and augmenting current surveillance and response activities in the MBDS and SEAMEO countries. Given the fact that text-messaging solutions can be cost-prohibitive for many localities, we discussed how Geochat’s “Gateway” offers an affordable alternative—requiring coordination between mobile providers and government agencies—and more features than regular text-messaging provided by local mobile companies.

We then concluded the session with graduation ceremony where participants received their certificate in PHI, the first for the region! Congrats!

After graduation, Dr. Moe Ko Oo, (Regional Coordinator for the MBDS project), Dr. Yin Myo Aye (Data Analyst & Manager for the MBDS project) and I had a meeting to further discuss the project and InSTEDD's contribution to the region. During the workshop we introduced various open source efforts projects; in particular we discussed two efforts which aggregate news media and ProMed reports, HealthMap and BioCaster. Dr. Moe and I discussed the importance of providing situation awareness capacity for the MBDS region by extending these efforts from detection into response. We discussed in details; as I mentioned in a previous blog, how the majority of current systems have been geared towards specific data sources and detection algorithms but much less effort has been focused on how these systems will "interact" with humans. Dr. Moe was very keen towards our “collaborative” approach, and I quote: “HealthMap, the data they are presenting in their web is good for us to know, but we would like to see more on it” and that efforts as such should offer a “…better analytical way for future planning and advocacy purpose.” We also discussed how our approach could further refine and enhance classification of health events especially at the earliest stages of an infectious disease outbreak. I had a follow-up discussion with Dr. John Browntein and his team is working with ProMed (recent grant from Google (healthMap/ProMed) through the Google Predict and Prevent initiative) on extending the current HealthMap platform to incorporate collaborative tools.

Using GeoChat during an Avian Influenza Simulation Exercise, Stung Treng Province, Cambodia

2008-10-16T10:58:00-07:00

On October 13-15, 2008, over 65 people gathered from around Cambodia to participate in a Cambodian CDC-led Avian Influenza (AI) pandemic training and simulation exercise in the Stung Treng province, Cambodia. Cambodia CDC provided the public health training and InSTEDD introduced and tested GeoChat for the first time demonstrating the utility of mobile phones to help with cross-disciplinary and cross-regional pandemic preparedness and response.

Here my After Action Trip Report (a summary is also provided on the InSTEDD site).

AI Simulation Exercise – After Action Report Stung Treng Province, Cambodia, October 13-15, 2008, InSTEDD (Dec.8,2008)

View more documents from Taha Kass-hout.

Related Links:

Lecture at Mahidol Part 2: Using Open-Source Software for Public Health. By Kass-Hout and di Tada

2008-10-11T18:55:00-07:00

Using Open Source Software For Public Health Kass-Hout Di Tada

From: kasshout, 10 hours ago

Using Open Source Software For Public Health Kass-Hout Di Tada

View SlideShare presentation or Upload your own. (tags: health biosurveillance)

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Lecture at Mahidol Part 1: Application of Machine Learning in Biosurveillance--A Collaborative Approach by Kass-Hout and di Tada

2008-10-11T08:57:00-07:00

Machine Learning And Disease Surveillance/Biosurveilance Kass Hout Di Tada

From: kasshout, 15 minutes ago

Machine Learning And Disease Surveillance/Biosurveilance Kass Hout Di Tada

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The majority of the designs, analyses and evaluations of early detection (or biosurveillance) systems have been geared towards specific data sources and detection algorithms. Much less effort has been focused on how these systems will "interact" with humans. For example, consider multiple domain experts working at different levels across different organizations in an environment where numerous biosurveillance algorithms may provide contradictory interpretations of ongoing events. We present a framework that consists of a collection of autonomous, machine learning-enabled analytic processes, services and tools that; for the first time, will seamlessly integrate surveillance and response systems with human experts.

SlideShare Link

Collaborative Analytics and Environment for Linking Early Event Detection to an Effective Response

2008-09-07T00:41:00-07:00

A system for early detection, situational awareness and coordinated response is essential to effectively mitigate the threat (morbidity and mortality) of a health-related event and to improve health. The progress made to-date in biosurveillance worldwide is significant and should be evolved to meet existing and emerging needs. There are existing processes, relationships, technologies, policies, infrastructures, and advances in science and technology that provide a solid foundation to a truly integrated biosurveillance solution. Of paramount importance is the need to strengthen the capacity and enable data-driven decision-making of public health services from the local to the district, national and global levels—both strategically and tactically.

In looking at the current landscape; however, we found the majority of the designs, analyses and evaluations of early detection (or biosurveillance) systems have been geared towards specific data sources and detection algorithms. Much less effort has been focused on how these systems will "interact" with humans. For example, consider multiple domain experts working at different levels across different organizations in an environment where numerous biosurveillance algorithms may provide contradictory interpretations of ongoing events.

Nico and I have been working on project codenamed RNA (or Event Evolution) to provide the public health, disaster and humanitarian communities with 1) a "collaborative” virtual environment supporting the entire life cycle of an event, and 2) a ubiquitous biosurveillance capability. Our objective is to connect early event indications to a coordinated and timely response, therefore reducing the response cycle. Through a hybrid (event-based and indicator-based) surveillance approach, we aim to provide processes, methods, and technology tools for streamlining collaboration between domain experts and machine learning algorithms. By synthesizing a wide variety of health-related event indications into a consolidated picture, RNA is anticipated to help the user community to:

Rapidly identify, characterize, localize, and track health-related events
Maintain a global awareness of the situation
Integrate and analyze data relating to human health, animal, plant, food, and environment
Disseminate alerts

The humans are essential part at every step of the life cycle of an event. Humans understand the meanings of information, languages, images, etc. better than machines alone and can make contextually relevant collections of information. Each collection can build the power of the knowledge network in order to corroborate or refute different hypotheses especially at the early and sketchy stages of an event.

RNA will consist of several high-level modules, including:

Data processing
Automatic feature extraction, data classification and tagging
Human input, hypotheses generation and review
Predictions and alerts output
Field confirmation and feedback

The data processing module allows users to assimilate, broker, and/or collect information from several sources (SMS messages (e.g., Geo-Chat microformat), RSS feeds, email list (e.g., ProMED or ProMED MBDS), documents, web pages, scholarly publications, electronic medical records, animal disease data, environmental feed, remote sensing, VoIP, alerts, etc.). A number of ontologies (geographical and disease) will be supported in multiple languages, including: English, Spanish, French, Japanese, Vietnamese, Thai, Korean, Chinese, Arabic, Russian, and Khmer. Disease ontologies will include: Medical Subject Headings (or MeSH), Systematized Nomenclature of Medicine--Clinical Terms (SNOMED-CT), Logical Observation Identifiers Names and Codes (LOINC®), and The International Classification of Diseases (ICD9/10.)

The automatic feature extraction, data classification and tagging module is extensible allowing the introduction of machine learning algorithms (e.g., Bayesian). The components of this module can extract and augment features (or metadata) from multiple data streams; such as: 1) at the earliest stages of a disease outbreak it extracts source and target geo-location, time, route of transmission (e.g., person-to-person, waterborne), 2) at the later stages of a disease outbreak it provides detection and suggestion of tags for new sources based on the evidence on other sources using Support Vector Machines (or SVM) (as will be discussed below). Additional feature extraction include "data decorators"; for example, extracting features, such as: soil moisture, temperature, and mosquitoes density from a reference NASA remote sensing database during a suspected Dengue fever outbreak investigation following heavy rainfall and flooding associated with the landfalls of a hurricane in the Lower Rio Grande Delta. In addition, these components help detect relationships between extracted features within a collaborative space or across different collaborative spaces (e.g., Riff, the ProMED MBDS network.) We plan on using a smaller but more comprehensive set of event classification as follows:

Large aerosol release
Building/vessel contamination
Small release or contamination
Continuous or intermittent release of an agent
Contagious person-to-person
Commercially distributed products
Waterborne
Vector/host –borne
Sexual or parenteral transmission

This high-dimensionality of threat space classification can be further reduced to:

Single or focus event
Continuous/sustained event
Distributing/disseminating event

With human input, this module can help suggest possible classes (or a combination of classes) depending on time, space, and where we are in the life cycle of an event. Possible classifications include the following (Note: All tags/classifications can follow a hierarchical construct):

Syndromes (e.g., dermatological, gastrointestinal, musculoskeletal, neurological, respiratory)
Symptoms (e.g., fever, cough, sore throat, diarrhea)
Routes of transmission (e.g., person-to-person, waterborne, foodborne, aerosol)
Diseases (e.g., TB, HIV, Influenza, Influenza/Avian Influenza)
Hosts (e.g., human, animal, plant, multiple)
Time and geo-location (spatio-temporal features)

For example, at the earliest stages of an infectious disease outbreak, classification of an event could indicate that “there is an unknown respiratory event, transmitted person-to-person, detected in location X, and is spreading with a Y spatio-temporal pattern, across regions Z1, Z2, and Zn.”

The human input and review module is exposed as a set of features that allow users to comment, tag, and rank the elements (positive, neutral, or negative). Additionally, users (or groups, such as communities of interests) can generate and test multiple hypotheses in parallel, further collect and rank sets of related items (evidence), and model against baseline information (for cyclical or known events).

With that information together, the field confirmation and feedback module helps maintain and record history of a list of ongoing possible threats. Components in this module allow domain experts to focus their field investigation and information gathering in order to confirm or refute hypotheses under consideration. Feedback information is then fed into the virtual collaborative network to update (increase or decrease) the reliability of the sources and credibility of the users in light of their inferences or decisions. By analyzing the factors contributing to the identification of various events, it will be possible to help future epidemiologic investigation through play-back or retrospective analysis. Possible information we anticipate recording include:

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 indications are the most effective in identifying a health event?
What factors help to minimize or aggravate a health event?
Which elements of the biosurveillance life cycle require the most time and/or collaboration?

The network history can provide a common point of evaluation (for the overall situation awareness and the individual processes of the biosurveillance network) for a variety of surveillance and response techniques.

Over the summer, the Humanitarian FOSS (HFOSS) Project Summer Institute 2008 (May' 08 - July' 08) carried out an internship project mentored by InSTEDD and a number of HFOSS faculty. During this internship, Juan Pablo Mendoza and Qianqian Lin developed ALPACA Light Parsing And Classifying Application (ALPACA) to:

Transform raw unstructured documents (e.g., news reports, ProMED mail, etc.) into machine readable and analyzable data using a text parsing module
Categorize documents using a SVM classifier using libSVM for: a) Classification into a predetermined (user-defined) list of categories as described above (syndromes, symptoms, routes of transmission, diseases, etc.), and b) Suggesting additional tags and/or topics using a Naive Bayes classifier given existing topics and monitoring human input and review. This is especially helpful with new (emerging) threats or those threats that we know about but we experience them at a much bigger scale than usual (e.g., far more virulent flu virus than we’ve experienced over the past few years)

We tested ALPACA against two widely accepted early sources of information in the public health community; Reuters news and ProMED mail. Results are shown here:

ALPACA is extensible through a plug-in functionality that provides a simple way to add additional parsers and classifiers to the application. We are continuously adding and testing additional algorithms and we welcome your contribution to help us better calibrate existing classifiers and parsers as well as introduce additional ones (you can visit our collaborative space here.)

With RNA we hope to provide the user community with a ubiquitous capability that enables detection, prediction and response to health-related events through a collaborative environment that combines data exploration, integration, search and inference—providing more complex analysis and deeper insight. We've demonstrated RNA's initial capabilities (feature extraction, classification, and tagging and item clustering with a spatio-temporal context) as part of Riff and leveraging mesh4x during a demonstration for the MBDS in SE Asia last week. In the future we also plan to offer RNA as a service that can be integrated with other platforms and networks.

The analytical and collaborative support does not end at the early detection of an event; we envision RNA to provide a rich and flexible functionality during and after an event for maintaining situational awareness, effective response planning, and evaluation. We plan on providing RNA's libraries, tools and applications in the Google Code soon. In the meantime, we look forward to your feedback and contribution.

Some Definitions

Public health: "is the study and practice of managing threats to the health of a community. The field pays special attention to the social context of disease and health, and focuses on improving health through society-wide measures like vaccinations, the fluoridation of drinking water, or through policies like seatbelt and non-smoking laws. The goal of public health is to improve lives through the prevention and treatment of disease. The United Nations' World Health Organization defines health as "a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity." In 1920, C.E.A. Winslow defined public health as "the science and art of preventing disease, prolonging life and promoting health through the organized efforts and informed choices of society, organizations, public and private, communities and individuals." The public-health approach can be applied to a population of just a handful of people or to the whole human population. Public health is typically divided into epidemiology, biostatistics and health services. Environmental, social, behavioral, and occupational health are also important subfields." [Source: http://en.wikipedia.org/wiki/Public_health]
Epidemiology: "is the study of factors affecting the health and illness of populations, and serves as the foundation and logic of interventions made in the interest of public health and preventive medicine. It is considered a cornerstone methodology of public health research, and is highly regarded in evidence-based medicine for identifying risk factors for disease and determining optimal treatment approaches to clinical practice. In the work of communicable and non-communicable diseases, the work of epidemiologists range from outbreak investigation to study design, data collection and analysis including the development of statistical models to test hypotheses and the documentation of results for submission to peer-reviewed journals. Epidemiologists may draw on a number of other scientific disciplines such as biology in understanding disease processes and social science disciplines including sociology and philosophy in order to better understand proximate and distal risk factors." [Source: http://en.wikipedia.org/wiki/Epidemiology]
Outbreak: "is a classification used in epidemiology to describe a small, localized group of people or organisms infected with a disease. Such groups are often confined to a village or a small area. Two linked cases of an infectious disease are usually sufficient to constitute an outbreak. Outbreaks may also refer to epidemics, which affect a region in a country or a group of countries, or pandemics, which describe global disease outbreaks." [Source: http://en.wikipedia.org/wiki/Outbreak]
EID: "Emerging infectious diseases (EIDs) are caused by pathogens that have increased in incidence, geographic or host range, have changed pathogenesis, or are newly-evolved or newly-recognized. Over three-quarters of emerging infectious diseases are a result of zoonotic pathogens. Evidence suggests that emerging diseases are driven largely by anthropogenic environmental changes and/or changes in human demographics and behavior. In certain areas, these factors act on a background of high pathogen biodiversity and will alter host-parasite dynamics driving the emergence of known and unknown pathogens." [Source:http://www.conservationmedicine.org/eid_overview.htm]
Biosurveillance

A roadmap toward a European healthgrid

2008-05-20T20:53:00-07:00

This is an exciting work towards building an environment of sharing of resources across heterogeneous and dispersed health data:

molecular data (e.g. genomics, proteomics)
cellular data (e.g. pathways)
tissue data (e.g. cancer types, wound healing)
personal data (e.g. PHR, EHR)
population data (e.g. epidemiology)

Also, an application which can be accessed by all users as a tailored information system according to their level of authorization and without loss of information. Collaboration is the heart of this especially across multiple disciplines. In addition, many standards have not yet been realized (e.g., HL7 (Health Level Seven), the US based Standards Development Organization that currently offers asynchronous messaging), this makes a grid approach far more powerful where organizations join the grid and make their information available for querying, processing, analysis, etc.

Few challenges remain, such as:

How do we secure and maintain high performance of such distributed structure of data integration and computing?
How do we close the gap between grid standards and health-related standards [some nice work's been done here by Power, et al]?
How do we go about next-generation open source ontologies for medical informatics?
How do we close the gap between hospital policies, public health policies, etc. and the grid approach?
How do we go about consumerism and patient ownership of her or his data?

If anything, I hope this raises more awareness of the grid application in health and public health - much still remains to be answered.

Successes are already underway in the health community, for example:

US CDC [presentation on the Public Health Grid by Ken Hall (BearingPoint, Inc) and Dr. Tom Savel (US CDC) at the recent HimSS February 2008 meeting]

But also there are lots of lessons which we can learn from the innovative thinking of efforts like the Google Cloud (click here to learn more about cloud computing). In addition, InSTEDD’s Mesh4x allows for asynchronous integration of data between many different sources regardless of network connection, systems or services. Network connectivity is not a constant requirement, as Mesh4x can collect and distribute updates between two users over SMS, Internet (HTTP exchange), or through other available means. InSTEDD recently partnered with the United States Centers for Disease Control and Prevention (CDC) and developed a synchronization adapter for Epi Info™, CDC's application for field collection of disease outbreak information (you can read more about this project in 2 of my previous blogs: Empowering Epidemiologists to Share Information, Anytime, Anywhere: Epi Info™ and Mesh4x, and our final demonstration in December 2008: Epi Info™ and Mesh4x Prototype Demonstration with US CDC).

I'd be very interested to hear your thoughts on this...

Related Links:

US CDC Recommends Mesh4x to Synchronize Data using Epi Info™
CDC/InSTEDD Collaboration blog
Empowering Epidemiologists to Share Information, Anytime, Anywhere: Epi Info™ and Mesh4x
Epi Info™ current website on CDC.gov (available for download Epi Info™ 3.5.1 (last accessed May 16th, 2009))
Epi Info™ User Forum
Epi Info™ Community Edition (CE)
Epi Info™ Friends Group on Google (Restricted to invited members. To become a member, contact the group owner at andy.dean@gmail.com)
Epi Info™ in Italy
Epi Info™ in Brazil (Portuguese)
Epi Info™ in Spain
InSTEDD Mesh4X
InSTEDD Mesh4x Discussion Group
Juan Marcelo Tondato's Blog on Mesh4x
Eduardo Jezierski's Blog on Mesh4x
Taha Kass-Hout's Blog on Mesh4x
Daniel Cazzulino's Blog on Mesh Architecture
Pablo M. Cibraro's Blog on Mesh Architecture
Public Health Grid (PHGrid) - Research & Development

Medicine 2.0™ Congress: Social Networking and Web 2.0 Applications in Medicine and Health

2008-04-30T18:17:00-07:00

I am speaking at the Medicine 2.0™ conference on adapting and adopting social networking methods for biosurveillance. The conference is taking place in Ontario, CA on Sept4-5, 2008. "Medicine 2.0™ is an international conference on Web 2.0 applications in health and medicine, organized and co-sponsored by the Journal of Medical Internet Research, the International Medical Informatics Association, the Centre for Global eHealth Innovation, CHIRAD, and a number of other sponsoring organizations." The congress was organized by Dr. Gunther Eysenbach.

Here is a list of the final accepted abstracts and the Medicine 2.0™ Blog site...

I am very interested in hearing your thoughts and any input you can provide to help me better present the topic. Definitely if you know of existing or related work that I can reference will be much appreciated...

And as my friend Susanne Jul would say: Gunalchéesh [thank you, Tlingit]

What is Biosurveillance? What is InSTEDD's Role?

2008-01-25T15:21:00-08:00

Dating back to Hippocrates, biosurveillance, the detection and observation of disease outbreaks, is not an entirely new concept. In recent history, the geographical isolation between plants and animals has been gradually broken by the intentional or natural transport of organisms caused by human travel, tourism or trade. Today, the rate at which species are moving between different bio-geographic regions is unprecedented, resulting in adverse ecological, economic, and human health consequences. Additionally, global environmental changes have continued to grow rapidly throughout the past five years. These changes for example to climate, transport networks, disease pathogens and their vectors do not respect administrative boundaries and their influences and impacts are best addressed at the global scale. These factors have contributed to an environment where a new disease threat can spread globally within hours and days.

Over the last decade, automated biosurveillance systems have been developed to provide more timely detection of disease outbreaks by monitoring clinical systems and other sources. To date, the bulk of these systems have been regionally deployed, limited to areas where clinical data is readily available, and internet connectivity and high bandwidth are widespread. Unfortunately, these systems do not support regions of low bandwidth, limited connectivity, and sparse use of clinical information systems-the regions where globe-threatening outbreaks typically originate and where timely interventions are most needed. What is needed now is a global information infrastructure and scientific methods to support timely detection and monitoring of events world-wide, as close to real time as possible.

InSTEDD is working with its partners on addressing these problems and promise of timely biosurveillance. InSTEDD's suite of technologies and informatics approach will have a global reach that will revolutionize retrieval and integration of information across multiple disciplines, and will have a high degree of penetration into regions were enhanced biosurveillance is sorely needed. InSTEDD is taking the ancient art of biosurveillance to the global level, focusing on key biosurveillance targets such as Infectious and Respiratory Diseases (Influenza, Malaria, West Nile Virus, Lyme Disease (in animals is indicative of global warming), Rift Valley Fever, Schistosomiasis (schis·to·so·mi·a·sis (shĭs'tə-sə-mī'ə-sĭs)), Asthma)), Extreme Weather Events (heat waves and floods), and Natural and Managed Systems (forests, agriculture, marine ecosystems, and water).

Finally, I leave you with this historic map - John Snow used statistics to argue that "something was in the water" -- although no one had yet identified cholera.

1854 Broad Street cholera outbreak