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.

A follow up on our recent post on SIR model. Hopefully this video explains what happens during social isolation (quarantine) in terms of SIR model using simpler terms than our recent post. Enjoy!

As I am writing this, it is the first day of quarantine in Lithuania. So far the restrictions are relatively mild: people are advised to stay home, many public sector workers (including those employed in research institutions, such as myself) by default work from home, while those employed in private sector are advised to work from home. There are some who think that even these mild measures are too much and have doubts that there was a need for at this time quarantine (just 9 cases at the time of the decision; all of them coming back from abroad). There also many optimistic people who believe that timely quarantine can decrease the number of infected (and thus the number of deaths) by almost 40%! While this is not necessarily a lie, the number itself is more than likely to be invented (edit: quick online search reveals that this number is given in this article, which is an excellent article in many regards). One needs to make certain assumptions about the spread of disease and the efficiency of quarantine to get any reasonable estimate.

So this time we will talk about a classical model in epidemiology known as Susceptible-Infected-Recovered model or SIR model for short.