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.

Recall that the Ising model (see other posts) is a well-known model in statistical physics, which describes magnetization phenomena. Our previous explorations of the Ising model where based on the Glauber interpretation of the Ising model, but this interpretation is not the only interpretation. While most of the other interpretations are qualitatively similar to the Glauber interpretation (e.g., Metropolis interpretation), this time we will present you interpretation, which is very much different from the Glauber interpretation - Kawasaki interpretation.

In this rather dated video (back from 2011) prof. Rob Axtell (one of the contemporary gurus of agent-based modeling and general interdisciplinary numerical simulation) talks about the possibilities of agent-based models. He also talks about his work in modeling of the economy with huge number of agents.