recover code chunk required for outputs

Author: avallecam

episodes/compare-interventions.Rmd

@@ -48,15 +48,15 @@ uk_population <- epidemics::population(
   initial_conditions = initial_conditions
 )
 
-close_schools <- intervention(
+close_schools <- epidemics::intervention(
   name = "School closure",
   type = "contacts",
   time_begin = 50,
   time_end = 50 + 100,
   reduction = matrix(c(0.5, 0.01, 0.01))
 )
 
-mask_mandate <- intervention(
+mask_mandate <- epidemics::intervention(
   name = "mask mandate",
   type = "rate",
   time_begin = 40,
@@ -74,7 +74,7 @@ infectiousness_rate <- 1.0 / preinfectious_period
 recovery_rate <- 1.0 / infectious_period
 transmission_rate <- basic_reproduction / infectious_period
 
-output_baseline <- model_default(
+output_baseline <- epidemics::model_default(
   population = uk_population,
   transmission_rate = transmission_rate,
   infectiousness_rate = infectiousness_rate,
@@ -121,10 +121,13 @@ 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 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
+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 and `{tidyverse}` (including `{dplyr}`, `{ggplot2}`, and the pipe `%>%`) for data manipulation and visualization.
 
+```{r}
+library(epidemics)
+library(socialmixr)
+library(tidyverse)
+```
 
 :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: instructor
 
@@ -138,15 +141,15 @@ To compare the baseline scenario against the intervention scenarios, we can make
 
 If we wanted to investigate the change in epidemic peak with an intervention applied, we could plot the model trajectories through time:
 ```{r}
-output_baseline <- model_default(
+output_baseline <- epidemics::model_default(
   population = uk_population,
   transmission_rate = transmission_rate,
   infectiousness_rate = infectiousness_rate,
   recovery_rate = recovery_rate,
   time_end = 300, increment = 1.0
 )
 
-output_school <- model_default(
+output_school <- epidemics::model_default(
   # population
   population = uk_population,
   # rate
@@ -336,7 +339,7 @@ We can run the Vacamole model with [default parameter values](https://epiverse-t
 
 
 ```{r, eval = FALSE}
-output <- model_vacamole(
+output <- epidemics::model_vacamole(
   population = uk_population,
   time_end = 300
 )
@@ -384,7 +387,7 @@ initial_conditions_vacamole <- rbind(
 rownames(initial_conditions_vacamole) <- rownames(contact_matrix)
 
 # prepare population object
-uk_population_vacamole <- population(
+uk_population_vacamole <- epidemics::population(
   name = "UK",
   contact_matrix = contact_matrix,
   demography_vector = demography_vector,
@@ -393,15 +396,15 @@ uk_population_vacamole <- population(
 
 # prepare two vaccination objects
 # dose 1 vaccination
-dose_1 <- vaccination(
+dose_1 <- epidemics::vaccination(
   name = "two-dose vaccination", # name given to first dose
   nu = matrix(0.01, nrow = 3),
   time_begin = matrix(30, nrow = 3),
   time_end = matrix(300, nrow = 3)
 )
 
 # prepare the second dose with a 30 day interval in start date
-dose_2 <- vaccination(
+dose_2 <- epidemics::vaccination(
   name = "two-dose vaccination", # name given to first dose
   nu = matrix(0.01, nrow = 3),
   time_begin = matrix(30 + 30, nrow = 3),
@@ -412,14 +415,14 @@ dose_2 <- vaccination(
 double_vaccination <- c(dose_1, dose_2)
 
 # run baseline model
-output_baseline_vc <- model_vacamole(
+output_baseline_vc <- epidemics::model_vacamole(
   population = uk_population_vacamole,
   vaccination = double_vaccination,
   time_end = 300
 )
 
 # create mask intervention
-mask_mandate <- intervention(
+mask_mandate <- epidemics::intervention(
   name = "mask mandate",
   type = "rate",
   time_begin = 60,
@@ -428,7 +431,7 @@ mask_mandate <- intervention(
 )
 
 # run intervention model
-output_intervention_vc <- model_vacamole(
+output_intervention_vc <- epidemics::model_vacamole(
                                          population = uk_population_vacamole,
                                          vaccination = double_vaccination,
                                          intervention = list(
@@ -487,10 +490,34 @@ While visualizations are useful for comparing intervention scenarios over time,
 
 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 ($R_0$).
 
+```{r}
+# time periods
+preinfectious_period <- 4.0
+infectious_period <- 5.5
+
+# specify the mean and standard deviation of R0
+r_estimate_mean <- 2.7
+r_estimate_sd <- 0.05
+
+# generate 100 R samples
+r_samples <- withr::with_seed(
+  seed = 1,
+  rnorm(
+    n = 100, mean = r_estimate_mean, sd = r_estimate_sd
+  )
+)
+
+beta <- r_samples / infectious_period
+
+# rates
+infectiousness_rate <- 1.0 / preinfectious_period
+recovery_rate <- 1.0 / infectious_period
+```
+
 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:
 
 ```{r}
-output_baseline <- model_default(
+output_baseline <- epidemics::model_default(
   population = uk_population,
   transmission_rate = beta,
   infectiousness_rate = infectiousness_rate,
@@ -525,7 +552,7 @@ intervention_scenarios <- list(
 We use this list as our input to `intervention` in `model_default`
 
 ```{r}
-output <- model_default(
+output <- epidemics::model_default(
   uk_population,
   transmission_rate = beta,
   infectiousness_rate = infectiousness_rate,
@@ -544,7 +571,7 @@ We can do this using `outcomes_averted()` in `{epidemics}`. This function calcul
 + outputs of the intervention scenario(s).
 
 ```{r}
-intervention_effect <- outcomes_averted(
+intervention_effect <- epidemics::outcomes_averted(
   baseline = output_baseline, scenarios = output
 )
 intervention_effect
@@ -553,7 +580,7 @@ intervention_effect
 The output gives us the infections averted in each scenario compared to the baseline. To obtain the infections averted overall we specify `by_group = FALSE`:
 
 ```{r}
-intervention_effect <- outcomes_averted(
+intervention_effect <- epidemics::outcomes_averted(
   baseline = output_baseline, scenarios = output,
   by_group = FALSE
 )
@@ -632,7 +659,7 @@ beta <- withr::with_seed(
 )
 
 # run the baseline
-output_baseline <- model_ebola(
+output_baseline <- epidemics::model_ebola(
   population = guinea_population,
   transmission_rate = beta,
   infectiousness_rate = 2.0 / 5,
@@ -645,12 +672,12 @@ output_baseline <- model_ebola(
 )
 
 # create intervention objects
-reduce_transmission_1 <- intervention(
+reduce_transmission_1 <- epidemics::intervention(
   type = "rate",
   time_begin = 60, time_end = 100, reduction = 0.5
 )
 
-reduce_transmission_2 <- intervention(
+reduce_transmission_2 <- epidemics::intervention(
   type = "rate",
   time_begin = 30, time_end = 100, reduction = 0.1
 )
@@ -666,7 +693,7 @@ intervention_scenarios <- list(
 )
 
 # run model
-output_intervention <- model_ebola(
+output_intervention <- epidemics::model_ebola(
   population = guinea_population,
   transmission_rate = beta,
   infectiousness_rate = 2.0 / 5,
@@ -680,7 +707,7 @@ output_intervention <- model_ebola(
 )
 
 # calculate outcomes averted
-intervention_effect <- outcomes_averted(
+intervention_effect <- epidemics::outcomes_averted(
   baseline = output_baseline, scenarios = output_intervention,
   by_group = FALSE
 )