Add links to first mention of glossary entries

And add subsequent links where a reminder of the definition is required.

episodes/model-choices.Rmd

@@ -52,7 +52,7 @@ When deciding whether an existing model can be used, we must consider :
 
 A model may already exist for your study disease, or there may be a model for an infection that has the same transmission pathways and epidemiological features that can be used. 
 
-+ Do we need a deterministic or stochastic model? 
++ Do we need a [deterministic](../learners/reference.md#deterministic) or [stochastic](../learners/reference.md#stochastic) model? 
 
 Model structures differ for whether the disease has pandemic potential or not. When predicted numbers of infection are small, stochastic variation in output can have an effect on whether an outbreak takes off or not. Outbreaks are usually smaller in magnitude than epidemics, so its often appropriate to use a stochastic model to characterise the uncertainty in the early stages of the outbreak. Epidemics are larger in magnitude than outbreaks and so a deterministic model is suitable as we have less interest in the stochastic variation in output. 
 

episodes/simulating-transmission.Rmd

@@ -163,7 +163,7 @@ There is a library of models to choose from in `epidemics`. Models are prefixed
 ### Check model equations
 When using existing model structures always check the model assumptions. Ask questions such as:
 
-- How is transmission modelled? e.g. direct or indirect, airborne or vector-borne
+- How is transmission modelled? e.g. [direct](../learners/reference.md#direct) or [indirect](../learners/reference.md#indirect), [airborne](../learners/reference.md#airborne) or [vector-borne](../learners/reference.md#vectorborne)?
 - What interventions are modelled? 
 - What state variables are there and how do they relate to assumptions about infection?
 
@@ -466,7 +466,7 @@ The choice of increment will depend on the time scale of the parameters, and the
 
 ### Accounting for uncertainty 
 
-As the epidemic model is deterministic, we have one trajectory for our given parameter values. In practice, we have uncertainty in the value of our parameters. To account for this, we must run our model for different parameter combinations. 
+As the epidemic model is [deterministic](../learners/reference.md#deterministic), we have one trajectory for our given parameter values. In practice, we have uncertainty in the value of our parameters. To account for this, we must run our model for different parameter combinations. 
 
 We ran our model with $R_0= 1.5$. However, we believe that $R_0$ follows a normal distribution with mean 1.5 and standard deviation 0.05. To account for uncertainty we will run the model for different values of $R_0$. The steps we will follow to do this are: