This summer one of my colleagues asked me if I know what is the recovery rate from COVID-19. While I have read a lot of papers, some of them claiming to use "realistic" parameters, I still had no clue (as those "realistic" parameters were in some cases wildly different). Yet as we have enough data available, we can estimate recovery rate ourselves.
European Centre for Living Technology is an international and interdisciplinary research network involving 17 research institutions from Europe and other continents. Researchers in this network study life-like properties observed in variety of complex systems. Our university is represented by dr. Vygintas Gontis, who few weeks ago has made a presentation on his most recent research (see [1] for more details). We invite you to watch a video recording of his talk.
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].
Physics of risk, complexity and socio-economic systems.