One of the most interesting ideas I’ve seen about the spread of the corona virus this week is discussion about the role that superspreaders play. I thought the topic could be made accessible to kids following a plan similar to what we did last week with Christopher Wolfram’s virus spread model:
I also want to be clear that the code we are playing around with here is from Wolfrman’s project which you can find in the link below. Other than really minor modifications for this project, none of the code is mine and this project wouldn’t have been possible without Wolfram’s work:
Today’s project didn’t go nearly as well as I hoped, though, But even with things no going so well I wanted to share the project.
My idea was to show the boys the distributions of outcomes when a virus spreads through a network. So, unlike last week when we just looked at one simulation for each network, today we looked at 1,000 simulations per network. Then, as a really simplified way to look at the idea of superspreaders, we’d look at how the infection spread through the network when the starting point had different numbers of initial infections.
So, we started by looking at one of the networks from last week and talking about the ideas we learned from that project:
Since simulating 1,000 different runs through a network takes a long time I prepared several graphs ahead of times so that we could just talk about the results. Fortunately I prepared two different visuals for each simulation because the first graph I made ended up being extremely confusing for the boys:
We spent a lot of time in the last video making sure that we understood the visualization of the simulations I was running. It turned out that the histogram was the easiest one for the boys to understand.
With the boys hopefully understanding what the histograms meant now, we looked at how the spread of a virus through a network changes as the interaction between nodes of the network changes. What we looked at specifically was how the spread changes from almost nothing to spreading through the entire network quite suddenly.
Having looked at the change in spread based on the average number of interactions in the last two videos, here we changed to looking at how the spread changes based on the number of initial infections. By changing the number of initial infections from 5 to 10 to 15 to 25 to 100 (out of 1,000 nodes) we saw very different spreading patterns in the network.
This way of looking at spread through a network was my guess for an easy way for kids to see / understand the role of superspreaders.
Definitely not my best executed idea ever, but still hopefully something that helped the boys get a bit more understanding of some of the important ideas in virus models.