In the last few posts we have considered Reed-Hughes mechanism, first from the empirical and then from the theoretical perspectives. In all these posts we considered data at fixed point in time, but how does the temporal evolution look like when the system evolves as described in our prior posts?

Gross domestic product, one of the main indicators of the well-being of a country, is decreasing in many countries around the world due to the ongoing pandemic and associated lockdowns, but financial markets seem to be doing more than fine. To find out some of the possible reasons watch the following video by Two Cents.

In this post we present a theoretical explanation for the power-law distributions observed in the spatial growth of COVID-19. From theoretical point-of-view it is interesting why power-law distribution is observed in the data, as typical epidemic spread is characterized by an exponential distribution (at least the SIR model predicts this). Yet, the explanation is pretty simple and is based on the Reed-Hughes mechanism [1].

I would like to share another fine visual simulation of epidemic spread created by Primer, which was sent to me by a student (thanks for the link!). It is extremely visually appealing, so we invite you to watch it!

I have spent significant part of this summer reading papers on the modeling of COVID-19. It helped a lot that majority of them were quite terrible, those have saved me some time. Though there were also a few more interesting ones. In [1] it was shown that the distribution of deaths and cases over US counties follows a power-law distribution. This finding is quite similar to Zipf's law, but specifically for the epidemic spread.

In this post we will replicate the empirical finding and in the next post we will consider a theoretical model to explain such observation.