As Ebola wreaks havoc across the globe in the largest outbreak of the disease ever recorded, countries are scrambling to figure out how to best detect those who are contagious early on, and prevent the spread of disease.
Cases in Liberia are doubling every 15 to 20 days and in countries like Sierra Leone and Guinea they are doubling every 30 to 40 days. With the growing strain of available health care professionals, not only in Liberia but also here in the U.S., the role of technology and social media will gain wider attention.
Big data, the latest buzz word in many sectors, especially health care, is gaining rapid adoption. Big data enables aggregation of massive volumes filtered across many sources to look for relevant trends. Facebook undoubtedly is sitting on the largest network of connected individuals in the world, and perhaps, the largest trove of data on what people are doing in their day-to-day lives.
Often in disaster zones, analysis of such huge volumes of data can help identify trends that were not previously recognized or understood. As an example, earlier this year, Science Magazine revealed the findings of Google Flu Trends — a project they embarked on in 2009, aimed at looking at flu outbreaks based on individual search terms. In fact, Google claims that it was able to track the spread of flu faster than the Centers for Disease Control and Prevention (CDC) without the need for any medical information.
There is, however, risk in using big data as Science Magazine also found that Google had overestimated the number of flu cases for four years when compared to the data collected from channeled medical sources.
Most recently, Harvard’s HealthMap made headlines for being able to monitor early mentions of the Ebola outbreak on March 14. That was nine days before the World Health Organization (WHO) formally announced the epidemic.
HealthMap claims that its warning came from using massive computer power to sift out early indicators from millions of social media posts. It turns out that the first health care workers to see Ebola in Guinea were blogging about their work, and writing about the treatment of patients with Ebola-like symptoms. It is interesting to note, however, that traditional media sources in Guinea did publish information about the outbreak, but most of the communications were in French, and much of this news was lost in translation for other parts of the world to be aware.
As Facebook is embarking on a potential role in building health communities among individuals as an additional means of expanding its network, it will have an enormous responsibility that will have both positive and negative effects.
Facebook communities of health care professionals and other individuals on the front line, who are able to accurately report on the outbreak of new cases globally, and perhaps using a defined lexicon for communication, could have a powerful impact in hastening the time for diagnosis and prevention. But an uncontrolled network of communications around flu-like symptoms carries a great risk for creating mass hysteria among those who are uneducated about the disease.
The use of big data combined with Facebook’s expansive network can be a powerful compliment to the measures being taken by public health officials and others in enforcing standards for screening and patient care.
Since Facebook is the one means of communication that is used even in many remote parts of the world as a common medium, it is possible that, within a controlled environment, a framework of communication and data collection could be created that can help us tackle this crisis.
This will be an interesting experiment for Marc Zuckerberg on how Facebook can truly be used in remote areas to collect and disseminate information that can be responsibly analyzed by data scientists to effectively combat an emerging epidemic.
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