In the previous post we have estimated mean recovery time in COVID-19 Lithuanian data set. We have tried to generate fake recovery time series assuming that recovery times are exponentially distributed. We have failed. This time we will assume that recovery times are Weibull distributed.
Optimizing is easy, if the problem is small and functions involved are smooth with out local minima or maxima. But what happens when the problem is bigger or not so smooth? In these cases it might be impossible to find the best solution, so we have to be happy about finding "good enough" solution. How do we do it? By using the most metal algorithm in computer science.
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?
Physics of risk, complexity and socio-economic systems.