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minor edits bdmm tutorial

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denisekuehnert committed Aug 27, 2018
1 parent cd47b29 commit 958eec120da623d2280b5bb6dc3dfe919869281c
Showing with 7 additions and 4 deletions.
  1. +7 −4 README.md
@@ -266,7 +266,8 @@ Thus it is particularly important for the analysis that we properly set up the p
First, let's talk about the values that need to be set on the `Priors` panel.
The first panel that you see at the top is the tree prior.

`bdmm` is a model that can be used to explain data that is clearly divided into separate partitions, or demes.
`bdmm` is a model that can be used to explain data that is clearly divided into separate partitions, or demes.
(We will use the terms deme, partition and type interchangeably here.)
The demes can be geographical locations, as in our example, but the sequences can also be separated through other means than that, e.g. by a specific drug resistance mutation (strains can develop/lose drug resistance and thus move between demes, but can not transfer between demes otherwise), or location in the body (for example, for localised infections caused by the same agent).
In this dataset we have strains from 2 different locations, New Zealand and Hong Kong, so the `Number of demes` should be set to 2, which also is the default value.
Next, `bdmm` lets you estimate the `Reproduction number per type` and the `BecomeUninfectiousRate per type`.
@@ -326,9 +327,9 @@ This means that while the default setup might work and the runs will eventually
So, let us go through the important parameters and set priors according to the information we have about our dataset.
The first important parameter is R<sub>0</sub>.
In epidemiology, the basic reproduction number, R<sub>0</sub>, of an infection is the number of secondary cases one case generates on average over the course of its infectious period, in an otherwise uninfected population.
Even though we do not have any information on R<sub>0</sub> in the particular outbreak, infections rarely have R<sub>0</sub> > 10, so we can set an upper limit on the sampled values.
To do so, in the line denoted `R0.t:h3n2_2deme` click the button captioned `initial = [2.0, 2.0] [0.0, Infinity]` to get a pop up settings window (see [Figure 12](#fig:R0-prior)), where you can set the upper value.
Other than that, the current prior sets ther median value of the distribution to e<sup>0</sup> = 1, which will fit the endemic case of influenza.
The default prior sets the median value of the distribution to e<sup>0</sup> = 1, which will fit the endemic case of influenza.

<!--- todo: adjust figure (and ideally analysis files) to remove the upper bound of 10, it shouldn't be necessary -->

<figure>
<a id="fig:R0-prior"></a>
@@ -378,6 +379,8 @@ You can see the sampling prior setup in [Figure 15](#fig:samplingProportion-prio
<figcaption>Figure 15: Set the prior for sampling proportion.</figcaption>
</figure>
<br>
<!--- todo: In Figure 15 (sampling proportion prior) muss noch die "Mean in Real Space"-Box einen Haken haben -->


For the purpose of this tutorial and given that we know little about the outbreak in question to set strict priors on the `rateMatrix`, we will leave the other priors on the default values, but feel free to through them yourself and verify their sensibility.

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