Tag Archives: HealthMap

Want a map of the cholera epidemic in Haiti? Here are five …

PAHO: Chlorea outbreak situation map

Image by mediahacker via Flickr

Post by Kim Stephens

Crisis Mapping is an important tool for response organizations because a map can provide a visualization of events, needs of survivors, and also an aggregation of response activities. And a map can be created in near real-time. Mapping epidemics, particularly cholera, is of particular importance because it gives a visualization of how quickly and where the disease is spreading. As the death toll from the tragic cholera outbreak in Haiti surpasses 1000, you can look to maps created by several different organizations, both governmental and non-governmental, to see its grim progression. One issue, however, seems to be the sheer number of maps available and the duplication of effort regarding their creation. As noted by Chris Thompson of Humanity Road:

There are many different types of maps that are being used and different elements mapped to each for various focus areas – safe water sources, versus cholera cases, versus locations of cholera treatment centers.   To some degree there is duplication that we would like to see eliminated, but unfortunately, that is not the case.

Another limitation I observed is that although the map-producing groups indicate that they are collaborating, each one tends to keep its own map on its own website. In addition, the maps do vary regarding  how well their user-interface works.

After reviewing current mapping efforts for the epidemic in Haiti, I have two questions for which I cannot find answers:  (1) Who is using these maps for operations in the field?  And (2) which one(s) are they relying on?  Although Ms. Thompson did offer assurances that both government and humanitarian organizations were using maps created by technology volunteers, she really couldn’t provide me a concrete example (if one exist please comment). In short, while a lot of well-meaning individuals and organizations are gathering and sharing data, who is using it?  We know more about the supply of information than the demand for and use of it.

Here are five examples  of mapping efforts that I found in the past few days:

(1.) Since the first case was confirmed on Sept. 19, according to the U.N.’s Cholera Inter-sector Response Strategy for Haiti, the humanitarian response has been led by the Ministry of Public Health and Populations (MSPP) with support from the  Pan American Health Organization (PAHO) and the World Health Organization. The PAHO interactive map of  the epidemic represents cholera cases with varying shades of red, giving the viewer a quick understanding where cases are concentrated. It is, however, somewhat difficult to decipher anything other than at the macro-level.  From the PAHO website you can also click on a map of  health related resources in Haiti is simply called “Resource Finder”.  At first glance this is an impressive list of health facilities, their availability, capacity, and services. Upon closer inspection, however, under each of those categories  “No information” was posted instead. This google doc. explains that this list is of all facilities before the earthquake.

(2.) The Health Map, which I have written about before, uses color coded pushpins overlaid on a Google Map to represent validated reports of cholera cases as well as information about where to find clean drinking water and health facilities. According to their website, their maps are populated by data combed from “20,000 global news and health Web sites and blogs each hour.”  Reports are sorted by disease, relevance and location. And “it uses text-mining algorithms to aggregate and sort this information.” An “Alerts-Now-Showing” box beneath the map gives a list of each of the reports, sorted by date. Useful overlays include:

  • New Safe Water Installations
  • Water Points
  • Haiti health facilitates
  • Cholera treatment centers
  • Emergency shelters

HealthMap displays a note that they are collaborating with CrisisMappers and Humanity Road and their combied efforts were recently highlighted in a CNN report: Texts, Maps Battle the Haiti Cholera Outbreak. The article states the importance of the effort:

“On the mapping side of things, volunteers around the world are sorting through text messages, tweets and emergency response information to create real-time maps that can be used to push emergency aid to places where it is most urgently needed. Those maps are also available to local people with internet access.”

(3.) Biosurveillance map was created by Dr. James Wilson of the Haiti Epidemic Advisory System (HEAS), find his blog at Biosurveillance. They boldly state: “While the UN OCHA maps and official MSPP reporting tends to focus on Artibonite and points north, there are other areas routinely not included on the OCHA maps that have reported cholera.  We opt on the side of “cholera until proven otherwise” or when political sensitivity is such that full disclosure and transparency allows.” Collaborators for Biosurveillance include: CrisisMappers, HealthMap, InSTEDD, ProMED, Society of Critical Care Medicine, Ushahidi, Vethnography, Wildlife Conservation Society.

I find this map very hard to interpret, however. There is simply a symbol representing cases on a Google map. There is no other information available by clicking on the icon. For better information, skip the map and go instead to their blog which has some useful statistics regarding total cases and fatalities by region.

(4.) Reports culled from tweeted messages are curated by hand and placed on a Google Map by the  Project EPIC team. From their website:

Our team and an ever-growing group of collaborators involved in Crisis Camps have developed, evolved, deployed, and technologically leveraged a hashtag-based syntax to help direct Twitter communications for more efficient data extraction for those communicating about the Haiti earthquake disaster. Use requires modifications of Tweet messages to make information pieces that refer to #location, #status, #needs, #damage and several other elements of emergency communications more machine readable.

Color-coded icons represent different categories of data, typical of maps with overlays of information on one platform. For example, orange icons represent cholera treatment centers and by selecting one icon the user sees a pop-up window containing the following information: the author of the report, time of tweet, report type, geo-location including lat./long if possible, and the actual tweet. The following is an actual tweet from the field after it has been “tweaked”:

#haiti #cholera #loc Hospital Grande Riviere du Nord  (latitude and longitude are given) #need all cholera supplies & nurses. (Note: the tweets read a bit strangely because they have been put into a machine-readable format).

The user can also mouse over to a list all of the individual tweets in spreadsheet format. I’m not really sure who is using this map in-country, that information is not listed and the UN also has a map of treatment facilities. However, the level of detail and the fact that someone in the hospital “tweeted” their needs leaves the reader with hope that someone with the ability to help is viewing all of this aggregated information. One critic of this type of crowdsourcing even asked the question directly “Is crowdsourcing raising expectations that cannot be met?”

(5.) Noula.ht is an organization in Haiti created after the earthquake. They also have a map. However, since it is in Creole its difficult for most westerners to discern. From the CNN piece, the founder of Noula states: “We wanted to provide a platform and have it available to reinforce our local capacity to face disasters.” Green circles with numbers inside them represent the location and concentration of cholera cases.

I think I could have found 10 more maps but decided to stop at five.  I wish I could report on the impact these efforts are having on the ground, but that information is not yet available. When I find it, I will happily pass it along.  While I do not expect anyone to achieve a quantitative measure of success, a qualitative measure would be interesting.

An important document that addresses some of these concerns, especially regarding developing a common operating picture, is from the ICT for peace foundation.

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HealthMap, an example of information curation

Sophia B. Liu, a crisis informatics researcher from the University of Colorado, gave a talk at the International Conference of Crisis Mappers in Boston describing the concept of curation. She explained how in this day of information overload it is important to find trends or “the signal in the noise”. How do we decide what information is important versus what information is redundant or unreliable? She described how the process of crisis information curation is similar to the work a curator of an art exhibit, or a newspaper editor:  one or more people decide what is important, what stories are told. The curator also provides context by relating the story to time and place.

[picapp align=”left” wrap=”true” link=”term=Health+map&iid=6873483″ src=”http://view4.picapp.com/pictures.photo/image/6873483/special-school-helps-teen/special-school-helps-teen.jpg?size=500&imageId=6873483″ width=”380″ height=”251″ /]Of course this relates to crisis mapping since, as one person in the audience tweeted, “If it’s not on the map, did it happen?” This comment simply points out how responders and governments need information in a digested format in order to make decisions, to see the trends. The Health Map is a wonderful example of information curation but with a specific focus on diesease outbreaks. From their website:

HealthMap brings together disparate data sources to achieve a unified and comprehensive view of the current global state of infectious diseases and their effect on human and animal health. This freely available Web site integrates outbreak data of varying reliability, ranging from news sources (such as Google News) to curated personal accounts (such as ProMED) to validated official alerts (such as World Health Organization). Through an automated text processing system, the data is aggregated by disease and displayed by location for user-friendly access to the original alert. HealthMap provides a jumping-off point for real-time information on emerging infectious diseases and has particular interest for public health officials and international travelers.

Health Map was co-founded by Dr. John Brownstein of Harvard Medical School and Ph.d candidate, Clark Freifeld, a research software developer at the Children’s Hospital Informatics Program.  I had the pleasure of meeting both of them at the ICCM conference. Their team consist of 13 other equally impressive people with backgrounds ranging from biomedical engineering to epidemiology and biostatistics.

To learn more about what information is provided on their system see their HealthMap 3.0 Tutorial. In general, when you view the map you see either pins which represent precisely placed alerts or dots which represent country, state or province-wide alerts.  The dots are color coded based on a mathematical computation that takes into account time-frame, number of alerts and number of sources. You can customize the map to provide specific information that you are interested in, for example, by location or disease.

Users are able to add to information to the map with the “outbreak missing feature”–which can even be done on-the-go with a mobile app. People are encouraged, however, to add links to news articles which helps the team verify the information. And in fact, all reports are reviewed prior to being displayed. This user-generated information has its own category “HM Community News Reports” so that other users can understand the source of the content.

This model of curation is laudable, however, I wonder if it would work for information aggregation of real-time disaster data. The verification system mostly depends on reports from sources such as newspaper articles or Government or UN released reports; but during a fast moving crisis, by the time information is released by a news organization it could already have become overcome by events. There is no doubt, however, that this system is one of the very best for understanding disease outbreaks.