The effective reproduction number R_{eff}
is the average number of people infected by a single infected person.

R_{eff} (EpiEstim, τ=7) | |

R_{eff} (EpiEstim, τ=13) | |

R_{eff} (LSHTM, 50%-CI) | |

R_{eff} (LSHTM, 90%-CI) |

The figure depicts the estimated evoultion of R_{eff} in Austria according to the software EpiEstim using two fixed values for the
parameter τ. (τ=13 is used by AGES who is the main source for estimates of R_{eff} in Austria.)

In addition, the figure displays an enhanced version of EpiEstim developed by epiforecast.io (LSHTM). For the latter estimator we depict credible intervals (50%-CI and 90%-CI).
Based on case numbers reported on *October 24* (last update), the value of R_{eff} on *October 14* is estimated to lie between *1.2* and *1.3* (90%-CI), with a median of *1.3*.

Current trust in the estimator / credible interval:

The available data leaves some uncertainties that cannot be
taken into account quantitatively in the estimates. In the current
situation, it cannot be ruled out that these may significantly distort
the results.

Here we show the evolution of the number of people who tested positive for SARS-CoV-2, based on data of the ECDC.

Reporting date and infection date: In the figure above, the green bars depict the number of new positive tests (cases) reported on that day.
The *dark grey* band gives the estimate of the positive cases assigned to the date at which the infection took place. The light grey area gives the credible interval around it.

Hypothesis test for R_{eff} ≤ 1: In the week from *October 18* to
*October 24*,
*14326* new cases were reported.

Under the assumption that R_{eff} = 1
*(and given the case numbers until **October 17*) we would have expected
*9288* cases for this week (median).

Under the assumption that R

Under the assumption R_{eff} ≤ 1, the observed number of cases is
.

A simple statistical test shows that under the assumption *R*_{eff} = 1 (or smaller) the probablity of observing *14326* or *more* cases is *≤ 5%*.

Warning: if the absolute number of cases is small, reporting delays and the failure to detect positive cases can lead to substantial distortions.

Warning: if the absolute number of cases is small, reporting delays and the failure to detect positive cases can lead to substantial distortions.