add guides to tutors

Author: avallecam

episodes/simulating-transmission.Rmd

@@ -67,103 +67,16 @@ library(socialmixr)
 library(tidyverse)
 ```
 
+:::::::::::::::::::::: instructor
 
-```{r traj, echo = FALSE, message = FALSE, fig.width = 10, eval = TRUE}
-# load contact and population data from socialmixr::polymod
-polymod <- socialmixr::polymod
-contact_data <- socialmixr::contact_matrix(
-  polymod,
-  countries = "United Kingdom",
-  age.limits = c(0, 20, 40),
-  symmetric = TRUE
-)
-
-# prepare contact matrix
-contact_matrix <- t(contact_data$matrix)
-
-# prepare the demography vector
-demography_vector <- contact_data$demography$population
-names(demography_vector) <- rownames(contact_matrix)
-
-# initial conditions: one in every 1 million is infected
-initial_i <- 1e-6
-initial_conditions_inf <- c(
-  S = 1 - initial_i, E = 0, I = initial_i, R = 0, V = 0
-)
-
-initial_conditions_free <- c(
-  S = 1, E = 0, I = 0, R = 0, V = 0
-)
-
-# build for all age groups
-initial_conditions <- rbind(
-  initial_conditions_inf,
-  initial_conditions_free,
-  initial_conditions_free
-)
-rownames(initial_conditions) <- rownames(contact_matrix)
-
-# prepare the population to model as affected by the epidemic
-uk_population <- epidemics::population(
-  name = "UK",
-  contact_matrix = contact_matrix,
-  demography_vector = demography_vector,
-  initial_conditions = initial_conditions
-)
-
-# time periods
-preinfectious_period <- 3.0
-infectious_period <- 7.0
-basic_reproduction <- 1.46
-
-# rates
-infectiousness_rate <- 1.0 / preinfectious_period
-recovery_rate <- 1.0 / infectious_period
-transmission_rate <- basic_reproduction / infectious_period
-
-# run an epidemic model using `epidemic()`
-output_plot <- epidemics::model_default(
-  population = uk_population,
-  transmission_rate = transmission_rate,
-  infectiousness_rate = infectiousness_rate,
-  recovery_rate = recovery_rate,
-  time_end = 600, increment = 1.0
-)
-
-output_plot %>%
-  filter(compartment %in% c("exposed", "infectious")) %>%
-  ggplot() +
-  geom_line(
-    aes(
-      x = time,
-      y = value,
-      colour = demography_group,
-      linetype = compartment
-    ),
-    linewidth = 1.2
-  ) +
-  scale_y_continuous(
-    labels = scales::comma
-  ) +
-  scale_colour_brewer(
-    palette = "Dark2",
-    name = "Age group"
-  ) +
-  theme_bw() +
-  labs(
-    x = "Simulation time (days)",
-    linetype = "Compartment",
-    y = "Individuals"
-  )
-```
+Use slides to introduce the topics of:
 
+- Scenario modelling and 
+- Contact matrix.
 
-:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: instructor
-
-By the end of this tutorial, learners should be able to replicate the above image on their own computers.
-
-::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
+Then start with the livecoding.
 
+::::::::::::::::::::::
 
 ## Simulating disease spread
 
@@ -315,6 +228,19 @@ In `{epidemics}` the contact matrix normalisation happens within the function ca
 
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
+:::::::::::::::::::::: instructor
+
+Make a pause.
+
+Use slides to introduce the topics of:
+
+- Initial conditions and 
+- Population structure.
+
+Then continue with the livecoding.
+
+::::::::::::::::::::::
+
 ### 2. Initial conditions
 
 The initial conditions are the proportion of individuals in each disease state $S$, $E$, $I$ and $R$ for each age group at time 0. In this example, we have three age groups age between 0 and 20 years, age between 20 and 40 years and over. Let's assume that in the youngest age category, one in a million individuals are infectious, and the remaining age categories are infection free. 
@@ -377,6 +303,18 @@ uk_population <- population(
 ```
 
 
+:::::::::::::::::::::: instructor
+
+Make a pause.
+
+Use slides to introduce the topics of:
+
+- Model parameters and 
+- New infections.
+
+Then continue with the livecoding.
+
+::::::::::::::::::::::
 
 
 ### 4. Model parameters
@@ -540,6 +478,16 @@ newinfections_bygroup %>%
 
 :::::::::::::::::::::::
 
+:::::::::::::::::::::: instructor
+
+Stop the livecoding.
+
+Suggest learners to read the rest of the episode.
+
+Return to slides.
+
+::::::::::::::::::::::
+
 ## Accounting for uncertainty
 
 The epidemic model is [deterministic](../learners/reference.md#deterministic), which means it runs like clockwork: the same parameters will always lead to the same trajectory. A deterministic model is one where the outcome is completely determined by the initial conditions and parameters, with no random variation. However, reality is not so predictable. There are two main reasons for this: the transmission process can involve randomness, and we may not know the exact epidemiological characteristics of the pathogen we're interested in. In the next episode, we will consider 'stochastic' models (i.e. models where we can define the process that creates randomness in transmission). In the meantime, we can include uncertainty in the value of the parameters that go into the deterministic model. To account for this, we must run our model for different parameter combinations.