keep inline code-link at the end of sentence to facilitate translations

Author: avallecam

episodes/compare-interventions.Rmd

@@ -121,7 +121,7 @@ We must also define our *outcome of interest* to make comparisons between interv
 - Health impact measures (e.g., Quality-Adjusted Life Years [QALYs] or Disability-Adjusted Life Years [DALYs])
 - Economic measures (e.g., healthcare costs, productivity losses)
 
-In this tutorial, we will learn how to use the `{epidemics}` package to compare the effect of different interventions on simulated disease trajectories. We will use:
+In this tutorial, we will learn how to use the R package `{epidemics}` to compare the effect of different interventions on simulated disease trajectories. We will use:
 - `{socialmixr}` for social contact data
 - `{tidyverse}` (including `{dplyr}` and `{ggplot2}`) for data manipulation and visualization
 
@@ -485,7 +485,7 @@ output_vacamole %>%
 
 While visualizations are useful for comparing intervention scenarios over time, we also need quantitative measures of intervention impact. One such measure is the number of infections averted, which helps us understand the difference between intervention scenarios.
 
-The `{epidemics}` package provides the `outcomes_averted()` function to calculate infections averted while accounting for parameter uncertainty. Let's extend our COVID-19 example from [Modelling interventions](../episodes/modelling-interventions.md) to account for uncertainty in the basic reproduction number ($R_0$).
+The R package `{epidemics}` provides the `outcomes_averted()` function to calculate infections averted while accounting for parameter uncertainty. Let's extend our COVID-19 example from [Modelling interventions](../episodes/modelling-interventions.md) to account for uncertainty in the basic reproduction number ($R_0$).
 
 We use these parameter values alongside the population structure and contact matrix used in [Modelling interventions](../episodes/modelling-interventions.md) to run the model for the baseline scenario:
 

episodes/disease-burden.Rmd

@@ -42,7 +42,7 @@ The choice between these approaches depends on whether burden affects transmissi
 - For Ebola, hospitalizations are important for transmission due to high-risk healthcare settings
 - For many respiratory infections, severe illness typically occurs after the infectious period, so burden can be modeled separately
 
-In this tutorial, we'll focus on the separated approach, where we first run an epidemic model to simulate infections, then use these values to estimate disease burden as a follow-on analysis. We'll use the `{epidemics}` package to simulate disease trajectories, `{socialmixr}` for social contact data, and `{tidyverse}` for data manipulation and plotting. 
+In this tutorial, we'll focus on the separated approach, where we first run an epidemic model to simulate infections, then use these values to estimate disease burden as a follow-on analysis. We'll use `{epidemics}` to simulate disease trajectories, `{socialmixr}` for social contact data, and `{tidyverse}` for data manipulation and plotting. 
 
 ```{r, warning = FALSE, message = FALSE}
 library(epiparameter)
@@ -74,7 +74,7 @@ To convert infections to hospitalizations, we need to consider:
 - The time delay from infection to hospital admission
 - The time spent in hospital before discharge
 
-We'll use the `{epiparameter}` package to define these delay distributions. The Gamma distribution is commonly used for these delays because:
+We'll use `{epiparameter}` to define these delay distributions. The Gamma distribution is commonly used for these delays because:
 
 - It's flexible and can model various shapes
 - It's bounded at zero (negative delays don't make sense)

episodes/modelling-interventions.Rmd

@@ -38,7 +38,7 @@ Mathematical models can be used to generate trajectories of disease spread under
 
 Interventions are usually incorporated into mathematical models via manipulating values of relevant parameters, e.g., reduce transmission, or via introducing a new disease state, e.g., vaccinated class where we assume that individuals who belong to this class are no longer susceptible to infection.
 
-In this tutorial, we will learn how to use the `{epidemics}` package to model interventions and access to social contact data with `{socialmixr}`. We'll use `{dplyr}`, `{ggplot2}` and the pipe `%>%` to connect some of their functions, so let's also call to the `{tidyverse}` package:
+In this tutorial, we will learn how to use `{epidemics}` to model interventions and access to social contact data with `{socialmixr}`. We'll use `{dplyr}`, `{ggplot2}` and the pipe `%>%` to connect some of their functions, so let's also call `{tidyverse}`:
 
 ```{r,message=FALSE,warning=FALSE}
 library(epidemics)