Why would that kind of technology be applied to Ebola? Diseases, like ideas, spread when people get in contact. You can actually learn the basics of virology through network visualization. If you are reading this blog post, you probably already know that graphs are the best way to represent and study the connections between different entities. That is key to understand the diffusion of a virus.
To fight a virus like Ebola, it is important to understand how it spreads. The goal is to contain it as much as possible. A graph model can be used to represent the different persons infected by the virus and the places, persons, activities and everything else that connect them. Instead of looking for the hidden network that connects terrorist, the scientists and doctors fighting the virus need to understand how it moves to stop it.
That task can be tricky. According to Noah Robischon of FastC@mpany, “In the case of a disease like Ebola, data used to track the spread of the disease can come from any number of sources, starting with tissue samples and medical reports taken in the field. Factor in information from medical labs, NGOs, public research, and private institutions and you have a pretty hefty mess of data that comes in any number of different formats, if it’s even structured at all”. Working with massive and unstructured data is always a challenge. This is exactly the problem that graph databases solve.