Understanding the spread of infectious diseases in populations is the key to controlling them. If the UK was facing a flu pandemic, how could we measure where the greatest spreading risk comes from? This information could help inform decisions on whether to impose travel restrictions or close schools.
We would like to offer our metrics to the research community as a better tool to measure behaviour in dynamic networks."—Hyoungshick Kim
Think of the patterns of human that can spread infectious disease; you might be breathed on by a hundred people a day in meetings, on public transport and even in the street. These interactions create a highly dynamic network, in which new nodes ( points), are added to the graph, some existing ones are removed, and in which edges (the lines that join the nodes) come and go too.
These are difficult concepts to grasp and the spread of diseases is just one of the many examples of visualising how networks rapidly spread into a complex mass of interactions.
Most analyses and models have assumed that networks are static, typically represented in graph form as a number of nodes connected by edges. For example, if a local council were to monitor the flow of traffic through a city, the roads would be modelled as a network and capacities would be assigned to the edges, which represent the number of lanes on the roads. Static network models would apply a network flow equation to determine the maximum traffic between any given pair of points.