fix space and :

Author: Degoot-AM

episodes/compare-interventions.Rmd

@@ -109,7 +109,7 @@ output_baseline <- epidemics::model_default(
 
 Learners should familiarise themselves with following concept dependencies before working through this tutorial: 
 
-**Outbreak response** : [Intervention types](https://www.cdc.gov/nonpharmaceutical-interventions/).
+**Outbreak response**: [Intervention types](https://www.cdc.gov/nonpharmaceutical-interventions/).
 :::::::::::::::::::::::::::::::::
 
 
@@ -340,7 +340,7 @@ There is no vaccination scheme in place
 
 ::::::::::::::::: hint
 
-### HINT : Running the model with default parameter values
+### HINT: Running the model with default parameter values
 
 We can run the Vacamole model with [default parameter values](https://epiverse-trace.github.io/epidemics/articles/model_vacamole.html#model-epidemic-using-vacamole) by just specifying the population object and number of time steps to run the model for:
 
@@ -543,9 +543,9 @@ output_baseline <- epidemics::model_default(
 
 Then, we create a list of all the interventions we want to include in our comparison. We define our scenarios as follows:
 
-+ scenario 1 : close schools
-+ scenario 2 : mask mandate
-+ scenario 3 : close schools and mask mandate.
++ scenario 1: close schools
++ scenario 2: mask mandate
++ scenario 3: close schools and mask mandate.
 
 In R we specify this as: 
 ```{r}
@@ -579,7 +579,7 @@ head(output)
 
 Now that we have our model output for all of our scenarios, we want to compare the outputs of the interventions to our baseline. 
 
-We can do this using `outcomes_averted()` in `{epidemics}`. This function calculates the final epidemic size for each scenario, and then calculates the number of infections averted in each scenario compared to the baseline. To use this function we specify the :
+We can do this using `outcomes_averted()` in `{epidemics}`. This function calculates the final epidemic size for each scenario, and then calculates the number of infections averted in each scenario compared to the baseline. To use this function we specify the:
 
 + output of the baseline scenario
 + outputs of the intervention scenario(s).
@@ -617,9 +617,9 @@ We recommend to read the vignette on [Modelling responses to a stochastic Ebola
 
 ::::::::::::::::::::::::::::::::::::: challenge 
 
-## Challenge : Ebola outbreak analysis
+## Challenge: Ebola outbreak analysis
 
-You have been tasked to investigate the potential impact of an intervention on an Ebola outbreak in Guinea (e.g. a reduction in risky contacts with cases). Using `model_ebola()` and the the information detailed below, find the number of infections averted when :
+You have been tasked to investigate the potential impact of an intervention on an Ebola outbreak in Guinea (e.g. a reduction in risky contacts with cases). Using `model_ebola()` and the the information detailed below, find the number of infections averted when:
 
 + an intervention is applied to reduce the transmission rate by 50% from day 60 and,
 + an intervention is applied to reduce transmission by 10% from day 30.
@@ -628,11 +628,11 @@ For both interventions, we assume there is some uncertainty about the baseline t
 
 *Note: Depending on the number of replicates used, this simulation may take several minutes to run.*
 
-+ Population size : 14 million
-+ Initial number of exposed individuals : 10
-+ Initial number of infectious individuals : 5
-+ Time of simulation : 120 days
-+ Parameter values : 
++ Population size: 14 million
++ Initial number of exposed individuals: 10
++ Initial number of infectious individuals: 5
++ Time of simulation: 120 days
++ Parameter values: 
   + $R_0$ (`r0`) = 1.1,
   + $p^I$ (`infectious_period`) = 12,
   + $p^E$ (`preinfectious_period`) = 5,

episodes/contact-matrices.Rmd

@@ -283,7 +283,7 @@ $$
 \end{aligned}
 $$
 
-To add age structure to our model, we need to add additional equations for the infection states $S$, $I$ and $R$ for each age group $i$. If we want to assume that there is heterogeneity in contacts between age groups then we must adapt the transmission term $\beta SI$ to include the contact matrix $C$ as follows :
+To add age structure to our model, we need to add additional equations for the infection states $S$, $I$ and $R$ for each age group $i$. If we want to assume that there is heterogeneity in contacts between age groups then we must adapt the transmission term $\beta SI$ to include the contact matrix $C$ as follows:
 
 $$ \beta S_i \sum_j C_{i,j} I_j/N_j. $$ 
 

episodes/disease-burden.Rmd

@@ -160,7 +160,7 @@ new_cases <- new_infections(output, by_group = FALSE)
 head(new_cases)
 ```
 
-To convert the new infections to hospitalisations we need to parameter distributions to describe the following processes :
+To convert the new infections to hospitalisations we need to parameter distributions to describe the following processes:
 
 + the time from infection to admission to hospital,
 
@@ -185,7 +185,7 @@ infection_to_admission <- epiparameter(
 # with right-skewed distribution reflecting variability in disease progression
 ```
 
-To visualise this distribution we can create a density plot :
+To visualise this distribution we can create a density plot:
 
 ```{r}
 x_range <- seq(0, 60, by = 0.1)

episodes/model-choices.Rmd

@@ -47,7 +47,7 @@ The focus of this tutorial is understanding existing models to decide if they ar
 
 ### Choosing a model 
 
-When deciding which mathematical model to use, there are a number of questions we must consider :
+When deciding which mathematical model to use, there are a number of questions we must consider:
 
 :::::::::::::::::::::::::::::::::::::::::::::::::::
 
@@ -274,7 +274,7 @@ An advantage of using `finalsize` is that fewer parameters are required. You onl
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
 
-## Challenge : Ebola outbreak analysis 
+## Challenge: Ebola outbreak analysis 
 
 
 
@@ -287,11 +287,11 @@ You have been tasked to generate initial trajectories of an Ebola outbreak in Gu
 1. Run the model once and plot the number of infectious individuals  through time
 2. Run model 100 times and plot the mean, upper and lower 95% quantiles of the number of infectious individuals through time
 
-+ Population size : 14 million
-+ Initial number of exposed individuals : 10
-+ Initial number of infectious individuals : 5
-+ Time of simulation : 120 days
-+ Parameter values : 
++ Population size: 14 million
++ Initial number of exposed individuals: 10
++ Initial number of infectious individuals: 5
++ Time of simulation: 120 days
++ Parameter values: 
   + $R_0$ (`r0`) = 1.1,
   + $p^I$ (`infectious_period`) = 12,
   + $p^E$ (`preinfectious_period`) = 5,
@@ -332,7 +332,7 @@ guinea_population <- population(
 
 ::::::::::::::::: hint
 
-### HINT : Multiple model simulations
+### HINT: Multiple model simulations
 
 Adapt the code from the [accounting for uncertainty](../episodes/simulating-transmission.md#accounting-for-uncertainty) section
 

episodes/modelling-interventions.Rmd

@@ -27,7 +27,7 @@ library(epidemics)
 
 Learners should also familiarise themselves with following concept dependencies before working through this tutorial: 
 
-**Outbreak response** : [Intervention types](https://www.cdc.gov/nonpharmaceutical-interventions/).
+**Outbreak response**: [Intervention types](https://www.cdc.gov/nonpharmaceutical-interventions/).
 
 **R packages installed**: `{epidemics}`, `{socialmixr}`, `{scales}`, `{tidyverse}`.
 

episodes/simulating-transmission.Rmd

@@ -33,9 +33,9 @@ webshot::install_phantomjs(force = TRUE)
 
 Learners should familiarise themselves with following concept dependencies before working through this tutorial: 
 
-**Mathematical Modelling** : [Introduction to infectious disease models](https://doi.org/10.1038/s41592-020-0856-2), [state variables](../learners/reference.md#state), [model parameters](../learners/reference.md#parsode), [initial conditions](../learners/reference.md#initial), [differential equations](../learners/reference.md#ordinary).
+**Mathematical Modelling**: [Introduction to infectious disease models](https://doi.org/10.1038/s41592-020-0856-2), [state variables](../learners/reference.md#state), [model parameters](../learners/reference.md#parsode), [initial conditions](../learners/reference.md#initial), [differential equations](../learners/reference.md#ordinary).
 
-**Epidemic theory** : [Transmission](https://doi.org/10.1155/2011/267049), [Reproduction number](https://doi.org/10.3201/eid2501.171901).
+**Epidemic theory**: [Transmission](https://doi.org/10.1155/2011/267049), [Reproduction number](https://doi.org/10.3201/eid2501.171901).
 
 **R packages installed**: `{epidemics}`, `{socialmixr}`, `{scales}`, `{tidyverse}`.
 
@@ -159,7 +159,7 @@ Confusion sometimes arises when referring to the terms 'exposed', 'infected' and
 
 We will use the following definitions for our state variables:
 
-+ $E$ = Exposed : infected **but not yet** infectious,
++ $E$ = Exposed: infected **but not yet** infectious,
 + $I$ = Infectious: infected **and** infectious.
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
@@ -345,7 +345,7 @@ To run our model we need to specify the model parameters:
 - infectiousness rate $\alpha$ (preinfectious period=$1/\alpha$),
 - recovery rate $\gamma$ (infectious period=$1/\gamma$).
 
-In `epidemics`, we specify the model inputs as :
+In `epidemics`, we specify the model inputs as:
 
 - `transmission_rate` $\beta = R_0 \gamma$,
 - `infectiousness_rate` = $\alpha$,

episodes/template.Rmd

@@ -58,7 +58,7 @@ Lesson content
 
 ::::::::::::::::::::::::::::::::::::: challenge 
 
-## Challenge 1 : can the learner run existing code
+## Challenge 1: can the learner run existing code
 
 Load contact and population data from the POLYMOD Zenodo survey
 
@@ -120,7 +120,7 @@ Add additional maths (or epi) content for novice learners
 
 ::::::::::::::::::::::::::::::::::::: challenge 
 
-## Challenge 2 : edit code/answer a question
+## Challenge 2: edit code/answer a question
 
 Load contact and population data for Poland from the POLYMOD Zenodo survey using the following age bins:
 

episodes/vaccine-comparisons.Rmd

@@ -25,7 +25,7 @@ exercises: 20
 
 Learners should familiarise themselves with following concept dependencies before working through this tutorial: 
 
-**Outbreak response** : [Intervention types](https://www.cdc.gov/nonpharmaceutical-interventions/).
+**Outbreak response**: [Intervention types](https://www.cdc.gov/nonpharmaceutical-interventions/).
 :::::::::::::::::::::::::::::::::
 
 
@@ -309,11 +309,11 @@ output %>%
 
 To understand the **indirect** effect of vaccinations, we want to know the effect that vaccination has on transmission, and hence the rate at new infections occur. We will use the function `new_infections()` in `{epidemics}` to calculate the number of new infections over time for the different vaccination programs. 
 
-The inputs required are :
+The inputs required are:
 
-+ `data` : the model output,
-+ `exclude_compartments` : this is an optional input, but in our case needed. We don't want the number of people vaccinated to be counted as new infections, so we need to specify the name of the model compartment where individuals transition out from `susceptible` (in this example `vaccinated`),
-+ `by_group` : should the results be calculated for each demographic group separately. 
++ `data`: the model output,
++ `exclude_compartments`: this is an optional input, but in our case needed. We don't want the number of people vaccinated to be counted as new infections, so we need to specify the name of the model compartment where individuals transition out from `susceptible` (in this example `vaccinated`),
++ `by_group`: should the results be calculated for each demographic group separately. 
 
 
 ```{r}
@@ -919,7 +919,7 @@ The NPI we will consider is temporarily closing schools, an intervention that ha
 
 We define **early implementation** as the first day of the simulation  (i.e. when there are less than 100 infections in total), and **late implementation** as 50 days after the start of the simulation (i.e. when there are approximately 50,000 infections in total). We assume the control measures are in place for 100 days after they start. 
 
-The combinations we will consider are :
+The combinations we will consider are:
 
 + early implementation of closing schools,
 + early implementation of a vaccine,