diff --git a/config.yaml b/config.yaml
index c9b07d54..e5722d19 100644
--- a/config.yaml
+++ b/config.yaml
@@ -27,7 +27,7 @@ life_cycle: 'pre-alpha'
 license: 'CC-BY 4.0'
 
 # Link to the source repository for this lesson
-source: 'https://github.com/epiverse-trace/tutorials'
+source: 'https://github.com/epiverse-trace/tutorials-middle'
 
 # Default branch of your lesson
 branch: 'main'
diff --git a/episodes/quantify-transmissibility.Rmd b/episodes/quantify-transmissibility.Rmd
index 1ecf1b9a..e931c460 100644
--- a/episodes/quantify-transmissibility.Rmd
+++ b/episodes/quantify-transmissibility.Rmd
@@ -12,8 +12,8 @@ withr::local_options(list(mc.cores = 4))
 
 :::::::::::::::::::::::::::::::::::::: questions 
 
-- How can I estimate key transmission metrics from a time series of case data?
-- How can I quantify geographical heterogeneity in these metrics? 
+- How can I estimate the time-varying reproduction number ($Rt$) and growth rate from a time series of case data?
+- How can I quantify geographical heterogeneity in these transmission metrics? 
 
 
 ::::::::::::::::::::::::::::::::::::::::::::::::
@@ -68,6 +68,14 @@ In Bayesian inference, we use prior knowledge (prior distributions) with data (i
 
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
+:::::::::::::::::::::::::::::::::::::::::::::::: instructor
+
+We refer to the Prior probability distribution and the [Posterior probability](https://en.wikipedia.org/wiki/Posterior_probability) distribution.
+
+Lines below, in the "`Expected change in daily cases`" callout, by "the posterior probability that $R_t < 1$", we refer specifically to the [area under the posterior probability distribution curve](https://www.nature.com/articles/nmeth.3368/figures/1). 
+
+::::::::::::::::::::::::::::::::::::::::::::::::
+
 
 The first step is to load the `{EpiNow2}` package :
 
@@ -149,7 +157,7 @@ The number of delays and type of delay is a flexible input that depends on the d
 
 The distribution of incubation period can usually be obtained from the literature. The package `{epiparameter}` contains a library of epidemiological parameters for different diseases obtained from the literature. 
 
-We specify a (fixed) gamma distribution with mean $\mu = 4$ and standard deviation $\sigma^2= 2$ (shape = $4$, scale = $1$) using the function `dist_spec()` as follows:
+We specify a (fixed) gamma distribution with mean $\mu = 4$ and standard deviation $\sigma= 2$ (shape = $4$, scale = $1$) using the function `dist_spec()` as follows:
 
 ```{r}
 incubation_period_fixed <- dist_spec(
@@ -176,15 +184,15 @@ For all types of delay, we will need to use distributions for positive values on
 
 ####  Including distribution uncertainty
 
-To specify a **variable** distribution, we include uncertainty around the mean $\mu$ and standard deviation $\sigma^2$ of our gamma distribution. If our incubation period distribution has a mean $\mu$ and standard deviation $\sigma^2$, then we assume the mean ($\mu$) follows a Normal distribution with standard deviation $\sigma_{\mu}^2$:
+To specify a **variable** distribution, we include uncertainty around the mean $\mu$ and standard deviation $\sigma$ of our gamma distribution. If our incubation period distribution has a mean $\mu$ and standard deviation $\sigma$, then we assume the mean ($\mu$) follows a Normal distribution with standard deviation $\sigma_{\mu}$:
 
 $$\mbox{Normal}(\mu,\sigma_{\mu}^2)$$
 
-and a standard deviation ($\sigma^2$) follows a Normal distribution with standard deviation $\sigma_{\sigma^2}^2$:
+and a standard deviation ($\sigma$) follows a Normal distribution with standard deviation $\sigma_{\sigma}$:
 
-$$\mbox{Normal}(\sigma^2,\sigma_{\sigma^2}^2).$$
+$$\mbox{Normal}(\sigma,\sigma_{\sigma}^2).$$
 
-We specify this using `dist_spec` with the additional arguments `mean_sd` ($\sigma_{\mu}^2$) and `sd_sd` ($\sigma_{\sigma^2}^2$).
+We specify this using `dist_spec` with the additional arguments `mean_sd` ($\sigma_{\mu}$) and `sd_sd` ($\sigma_{\sigma}$).
 
 ```{r}
 incubation_period_variable <- dist_spec(