(Covid-19) Contact tracing - an amazing graph problem & rabbit hole
In the past couple of days, I have been working with several of my colleagues on a number of projects, all around the world, that are preparing our societies for a post-lockdown strategy that will allow us to keep the Covid-19 pandemic under control, and still regain some of our freedoms. This will be tricky, for sure, but as in so many problems, technology can probably assist.
This has ended up being quite an extensive piece of work. In order to keep it readable, I have cut it up into 4 blogposts, which I will put up all at the same time:
- Part 1: how I go about creating a synthetic dataset, and import that into Neo4j
- Part 2: how I can start running some interesting queries on the dataset, making me understand some of the interesting data points in there and questions that one might ask
- Part 3: how I can use graph data science on this dataset, and understand some of the predictive metrics like pagerank, betweenness and use community detection to direct policies
- Part 4: a number of loose ends that I touched on during my exploration - but surely did not exhaust.
There's so much potential in this dataset, and in this problem domain in general. I feel like I have gone into the rabbit hole and have just resurfaced for some air. But who knows, maybe I will dive back in and do some more digging - after all, this is interesting stuff, and I love working on interesting topics.
Hope this is as interesting for you as it was for me.
All the best
Rik
Note that these demos will require the following environment:
- Neo4j Desktop 1.2.7, Neo4j Enteprise 3.5.17, apoc 3.5.0.9, gds 1.1.0, or
- Neo4j Desktop 1.2.7, Neo4j Enterprise 4.0.3, apoc 4.0.0.6 (NOT later! a bug in apoc.coll.max/apoc.coll.min needs to be resolved)
Labels: bloom, contact tracing, covid-19, graph data science, graph database, graphdb, neo4j, neomap, pandemic, privacy, security, spatial, time

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