What it is all about
While sitting in self-isolation at home and following my daughter's recent class work in maths, I allowed myself to link the daily John Hopkins COVID-19 data and display in a "Gamma" vs. "Beta" chart the current state in the COVID-19 development.
My intention, given the perceivably irrational COVID-19 hype and lacking reliable information, is to use the simplest possible model in order to indepenently assess the evolving dynamics of the epidemy, in the hope to see a substantial improvement of the situation caused by the radical isolation measures taken so far.

What are "Beta" and "Gamma"
The simple "susceptible-infectious-recovered" (SIR) model is well described in the corresponding Wikipedia article with "Gamma" and "Beta" being its parameters (among others).
The "Beta" coefficient stays for the infection growth rate, and "Gamma" governs the immunization rate (i.e. recovery or death) under the assumption that recovered people get immune against recurrent infections (still to be proven for COVID-19).

What do I see
In the presented chart, "Gamma" is shown on the X-axix and "Beta" is shown on the Y-axis, both simultaneously calculated from the daily data over the given reference time interval, with a bigger reference interval inducing some smoothing and some time delay in the "Beta"/"Gamma" definition.
The whole avalable time series for the selected country is displayed, with more distant points being more transparent. Note that I exclude some points (inconsistent data and/or outliers) so that some days' points may be omitted from the time series.

How to interpret what I see
The SIR model shows two possible dynamics regimes: either the epidemy worsens until a substantial part of population is infected or it reaches its maximum infection rate and then declines by itself.

Flatly spoken: if the daily points gradually reach and stay within the area shaded green then there is at least a good sign that the epidemy gets under control.

Enjoy what you see (with no warranty and no claim to be a professional epidemiologist) and stay good and sound!