polish model choice episode.

Author: Degoot-AM

episodes/model-choices.Rmd

@@ -29,7 +29,7 @@ set.seed(33)
 
 ::::::::::::::::::::::::::::::::::::: prereq
 
-+ Complete tutorial [Simulating transmission](../episodes/simulating-transmission.md)
++ Complete tutorial [simulating transmission](../episodes/simulating-transmission.md)
 :::::::::::::::::::::::::::::::::
 
 
@@ -206,7 +206,7 @@ The key parameters affecting the transition between states are:
 + $\rho^E$, the mean preinfectious period,
 + $p_{hosp}$ the probability of being transferred to the hospitalised compartment. 
 
-**Note: the functional relationship between the preinfectious  period ($\rho^E$) and the transition rate between exposed and infectious ($\gamma^E$) is $\rho^E = k^E/\gamma^E$ where $k^E$ is the shape of the Erlang distribution. Similarly for the infectious period $\rho^I = k^I/\gamma^I$. For more detail on the stochastic model formulation refer to the section on [Discrete-time Ebola virus disease model](https://epiverse-trace.github.io/epidemics/articles/model_ebola.html#details-discrete-time-ebola-virus-disease-model) in the "Modelling responses to a stochastic Ebola virus epidemic" vignette. ** 
+**Note:** the functional relationship between the preinfectious  period ($\rho^E$) and the transition rate between exposed and infectious ($\gamma^E$) is $\rho^E = k^E/\gamma^E$ where $k^E$ is the shape of the Erlang distribution. Similarly for the infectious period $\rho^I = k^I/\gamma^I$. For more detail on the stochastic model formulation refer to the section on [Discrete-time Ebola virus disease model](https://epiverse-trace.github.io/epidemics/articles/model_ebola.html#details-discrete-time-ebola-virus-disease-model) in the "Modelling responses to a stochastic Ebola virus epidemic" vignette. 
 
 ```{r, echo = FALSE, message = FALSE}
 DiagrammeR::grViz("digraph {