In the last post we have introduced you to the compartmental voter model [1]. While we have introduced the concept of capacity and allowed for a brief exploration of it, this time we dig slightly deeper into this topic.

It seems that COVID-19 "wind" has blown on the house of cards, which we have been carefully rebuilding since 2008 crisis. Though the markets are not the economy, but the economy is likely to stall as it did after so many crashes before. We invite you to watch an Extra History episode (by Extra Credits), which provides historical retrospective on The 1929 Crash, which was followed by The Great Depression.

What do you think will our economy fall as the financial markets did?

In sociological papers it is quite common to see the analysis of how specific socio-demographic factors influence voting behavior. In opinion dynamics on the other hand we are usually interested only in contagion effects. Though some of the models sometimes are more sophisticated and include things like bounded confidence, explicit or implicit (like in Ishii's trust and suspicion models we have recently discussed) network structure, which effectively segment the society into separate groups. Yet these groups are surely not equivalent to the socio-demographic groups.

Khan Academy is an American non-profit educational organization. This organization produces short educational videos, which are more similar to your every day lecture than your typical popular science video. Due to COVID-19 outbreak this educational website has opened up variety of educational content, including videos related to COVID-19 outbreak itself.

This video discusses basic intuition on how to estimate the actual cases of an epidemic outbreak. In general same methodology works for any epidemic outbreak as long as we are still in the exponential growth phase. The actual numbers are estimated for the ongoing COVID-19 outbreak and are taken from a blog post by Tomes Pueyo. We invite you to watch it.

Let us continue examining UK census 2011 data set (see the previous post). This time the app uses scatter plots, which allow us to see whether some socio-demographic parameters are correlated to other socio-demographic parameters. While some of such findings are just spurious correlations, other times some other explanation could easily be found.

Once again we use UK census 2011 data set, which is freely from NOMIS website). Here we continue using Tables KS201EW, KS209EW, KS301EW, KS402EW and QS607EW. Our geographical resolution being postal areas.