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.

Another video on epidemiology by Youtube creator I really like watching. In this video by 3Blue1Brown you will see a few variations of epidemiological agent-based model each of which explore various disease containment strategies. Also, this video is another chance to understand that disease tend to spread (exponentially) fast.

At least for me key take away is the effect of a small fraction of people who do not follow social distancing. Those who treat quarantine as a windfall holiday or as an infringement of their personal freedoms. While the curve will still be reasonably flattened, time to fully eradicate the disease grows much longer. This means that society as a whole will have to suffer even longer quarantine, which could result in various social, economical and other health-related side effects.

Don't be an idiot. Stay home.

While we haven't told you the previous post on Kawasaki dynamics is actually meant as a context towards upcoming series of posts. While it covered theoretical aspect of the upcoming series, as the model we will talk about is build on the same premise as Kawasaki interpretation of the Ising model, this post will cover empirical aspect of the upcoming series.

Namely, here we introduce you to the rank-size distributions and illustrate the concept using UK census 2011 data. Note that the data is freely available from NOMIS website). Here in this post we will use Tables KS201EW, KS209EW, KS301EW, KS402EW and QS607EW. Our geographical resolution being postal areas.