Part 1/4: creating and importing a synthetic contact tracing graph
As we are living in these very interesting times, and many countries are still going through a massive operation to slow down the devastating effects of the SARS-CoV-2 virus and its CoViD-19 effects, there is of course also a lot of discussion already going on what we will do after the initial surge of the virus has passed, and when the various countries and regions will start opening up their economies.
A tactic many countries seem to be taking is the implementation of some kind of Contact Tracing. Using the technology on our phones and our pervasive internet connectivity, we could imagine a way to implement "distancing" and isolation of people that are either already victim of, or vulnerable to, CoViD-19. This seems like a logical, and useful tactic, that could help us to open up our economies for business, while still maintaining the basic attitude of wanting to "flatten the curve". Of course there are still many, many issues with this approach, not in the least with regards to patient privacy and political freedoms, but it seems like an interesting track to explore, at least. Many government organisations have therefore started to explore this, and are working with some of the industry giants like Google and Apple to make this a reality.
This evolution started a whole range of discussions inside Neo4j
, especially with regards to the usefulness of a graph database to make sense of some of these contact traceability databases. I remember reading Christakis and Fowler's Connected book
, and understanding that virus outbreaks are one of those cases where our direct contacts don't necessarily matter - or at least not matter alone. Indirect contacts, between our friends' friends' friends, can be just as important. So lots of interesting, graph-oriented questions arise: How could we maximise the effect of our distancing measures, and of any contact tracing applications that we put in place? How could we use the excellent and predictive power of the graph to find out which of a person's connections could be most risky? How can we use graph analytics to better understand the structural power and weakness of our social networks? And many more.
So, being locked down myself (although Belgium clearly has a much software stance than for example France or Italy), I thought I would spend some time exploring this. That's what this blogpost series is going to be about - so let's get right to it.