Understanding Pandemic Outbreaks through Data Visualisation - An Assessment of Current Tools and Techniques

1.1 Pandemic Outbreak Visualisation There has been a significant amount of work in the area of epidemic and pandemic disease visualisation, particularly as this domain may be crucial to the survival of the human species. A pandemic disease is one that spreads to be prevalent over a whole country or the world, as opposed to an epidemic disease which is more local on a city scale or smaller. Naturally there is much overlap in understanding these two types of disease spread, however this paper will focus more on the larger scale pandemic spreads. Examples of notable pandemic diseases throughout history include: the bubonic plague in 14th-15th century Europe, killing approximately 25 million people[1]; tuberculosis, which currently infects one third of the world’s current population with new infections occurring at a rate of approximately one person per second killing in 50% of cases[2]; and malaria, infecting approximately 350-500 million people worldwide[3]. These diseases present an immense hurdle for humanity, infecting and killing large numbers of the global population, however all of these diseases started with a single infection. Understanding how this single infection can spread to become a worldwide pandemic will help us to understand not only how to aid in reducing numbers of current pandemic infections, but also to prevent future pandemic outbreaks. The question then becomes what is the best way to study and understand the data we have available on pandemic diseases in order to make meaningful conclusions from it? One of the most intuitive ways to do this is through information visualisation. Visualisation of the data allows us to much more easily recognise patterns in it and pick out key areas of interest for further study. Once these patterns have been identified and further analysis has been carried out, the information gained may be used to help form health policy to aid in the prevention of future pandemic outbreaks. In line with this emphasis placed on understanding pandemic outbreaks, the VAST Challenge (a major annual information visualisation competition) has included several challenges involving visualising epidemic and pandemic diseases, in particular characterising where they originate and how they spread. The focus of this paper is part of the VAST Challenge from 2010, which involved analysing synthetic data consisting of hospitalisation and death records in order to characterise the spread of a fictitious outbreak of drafa fever[4].