This website is unfortunately in use much longer than anticipated. By now, the media have bought some competent chart data. Also counting the days since the initial outbreak does not make much sense any more. Therefore, I aligned the charts by date for those who are here to get the statistics computed in javascript by their browser. You can find the original version here.

The following charts are based on live data from https://github.com/pomber/covid19, which in turn sources jhu.edu.

Countries to compare

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Contents

#

Here, confirmed refers to a case being **reported** in contrast
to a
case
being **infected**.
Therefore, fluctuations are introduced by working days (e.g. less tests performed on weekends &
holidays).

Spreading rate measures

# The infection rate can be visualised intuitively as the *doubling time*,
i.e. the time it takes for the confirmed count to double.

# Yet another way to represent the same data is the effective reproduction number R.

# Yet another way to represent the same data is the 7-Day incidence.

However, this representation is numerically unstable as it diverges to infinity as the infection rate approaches to 1.0.

# Yet another way to represent the same data is the effective reproduction number R.

It is very similar the infection rate. However, instead of comparing the
the absolute number of *confirmed* from day to day, the change in *confirmed* over a period of time is
used.
Therefore, R becomes 0.0 if there are no new infections.

The number R determines which proportion of the population must be vaccinated to stop the disease.
Generally speaking: the higher R, the higher the proportion.

There is a variety of models to
estimate R.
I went with a rather naive approach (see code), so the actual numbers may vary from official reports.

# Yet another way to represent the same data is the 7-Day incidence.

This measures the number of infected per 100.000 over the last 7-Days. This too, measures the effective reproduction of the virus. But the different unit makes this more understandable I guess.

The population count to compute the incidence is based on data from
wikipedia

#
However, *confirmed* does not mean the same between different countries and even in the same country
at
different time points of the epidemic. This is due to the sampling bias induced by the limited amount of
corona test kits.
In the early days of the epidemic there are enough kits to test everyone, so many cases that do not yet show
symptoms are tested.
With ongoing spread, we hit limits on test-kit and health-system capacities and the focus shifts to testing
severe cases only. This in turn pushes the fatality rate.

A significant increase of the fatality rate indicates that

- the confirmed count is being under-estimated.
- the health-system capacites being exhausted.

Conversely, a decrase of the fatality rate indicates that

- the confirmed count was previously under-estimated. (e.g. by the exhaustion of the health system and focusing on severe cases)

We are only talking about an *estimate* of fatality rate here, as we cannot measure the true
fatality
rate while the epidemic is ongoing. For us the relative rate between countries is sufficient. Therefore
we simpliy chose the optimisitic deaths by confirmed ratio here.

#
In contrast to the fatality rate, the mortality rate below is shown in dead per million inhabitants.
This makes it independant of whether the confirmed count is estimated correctly. In most cases both will be
correlated.

However, the mortality rate is a better indicator of the influence of the pandepic on a countries society and economy - especially when the health-system is exhausted.

The population count to compute the mortality is based on data from
wikipedia

Source-code: https://github.com/paroj/arewedeadyet

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