What is R_{t}?
The R_{t}, also known as the reproduction rate, describes how many
people an infected person ends up infecting.
Together with the number of new infections, the reproduction rate R_{t} is
an important figure to follow to understand how fast the disease spreads.
We can distinguish three different scenarios:
 R_{t}>1: the number of new infections increases
 R_{t}=1: the number of new infections remains the same
 R_{t}<1: the number of new infections decreases
For more information, you can take a look at
this page to begin with.
Where do the data come from?

Belgium: The daily data comes from the
EPISTAT COVID19 dataset
, of "Siensano", the national public health institute of Belgium.
The Belgian data loader was contributed by Lode Nachtergaele.

Germany: We collect positive tests numbers thanks to the official
dashboard of the RKI, which is based on the underlying ArcGIS system.
We also collect daily tests results by region. They are kindly
provided to us by the RKI. A summary of these figures is also
published by the RKI in its
weekly report.
The number of tests carried out is difficult to analyze in the
current reporting system. Thus, we currently use the data compiled
by the ARS team of the Robert Koch Institute. Their figures are
based on figures provided voluntarily by test laboratories. Today,
for small federal states in particular, the data may therefore
not be representative of the real number of tests carried out.
The German data loader was contributed by Laura Helleckes and Michael Osthege.

France: The daily data come from the French government
opendata repository.
Note that data by regions only contain tests for
which residence regions of tested people could be known. Hence,
countrywide data contain more tests than the sum of all regions.
Moreover, data transmission can sometimes excess 9 days. Indicators
are updated daily on test results reception.
The French data loader was contributed by Alexandre Andorra.

Italy: The data is sourced from the
data repository (CCBY4.0).
of the Dipartimento della Protezione Civile (the department of civil protection).
The Italian data loader was contributed by Davide Ferrero.

United States: All the data come from
covidtracking.org.
The US data loader originates from the original project.

All other countries: Data come from
ourworldindata.org.
Visit
github.com/rtcovidlive/rtliveglobal
for all the data sources and a detailed explanation of how the model is programmed.
What do the superscript and subscript numbers mean?
The large number describes the median value, while the small numbers indicate the
range in which our analysis suspects the true value is, with a 90% probability.
X = 0.94
_{0.78}
^{1.11}
can therefore be read as "Our analysis has shown that there is a 90% probability that X lies between 0.78 and 1.11.".
How are the colors of the risk indicators chosen?
For the reproduction number, we choose the color based on the probability that R > 1.
Color 
Probability range 
green 
025 % 
gray 
2550 % 
orange 
5075 % 
red 
75100 % 
What are the weaknesses of the method?
The model requires the number of daily performed COVID19 tests, which are only
available within a delay for all countries. We therefore use
Facebook's Prophet
library to predict the number of tests that will be performed.
Our forecast is shown in the detailed views, but, in a nutshell, the older the
latest test numbers are, the less reliable this forecast becomes.
No model can exactly describe reality and the more we learn about the pandemic,
the more "flaws" in the model we get to discover. The model also makes assumptions
that are informed by literature sources, but do not necessarily apply exactly to every
countries we model here. In addition, these literature sources are only snapshots
that apply to a greater or lesser extent in the course of the pandemic.
Importantly, two probability distributions are key to the model:
 The time lag between an infection and an observed positive test.
 The time lag between the infection of a person A and the infection of another
person B by this person A.
Who is behind this page?
We,
Laura Helleckes
and
Michael Osthege,
are doctoral students at the Institute for
Biotechnology (IBG1) at the Jülich Research Center. We have adapted the original
opensource model for
crosscountry compatibility. But we're not alone here! As you'll see at
github.com/rtcovidlive/rtliveglobal,
there are many other people, who made a contribution to this project. If
you have any questions or comments, please contact us by
email.