diff --git a/instructors/files/baseline-interventions.R b/instructors/files/baseline-interventions.R
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+++ b/instructors/files/baseline-interventions.R
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+# nolint start
+
+# load packages
+library(epidemics)
+library(socialmixr)
+library(tidyverse)
+
+# load survey data
+survey_data <- socialmixr::polymod
+
+# generate contact matrix
+cm_results <- socialmixr::contact_matrix(
+  survey = survey_data,
+  countries = "United Kingdom",
+  age.limits = c(0, 15, 65),
+  symmetric = TRUE
+)
+
+# transpose contact matrix
+cm_matrix <- t(cm_results$matrix)
+
+# prepare the demography vector
+demography_vector <- cm_results$demography$population
+names(demography_vector) <- rownames(cm_matrix)
+
+# initial conditions: one in every 1 million is infected
+initial_i <- 1e-6
+initial_conditions <- c(
+  S = 1 - initial_i,
+  E = 0,
+  I = initial_i,
+  R = 0,
+  V = 0
+)
+
+# build for all age groups
+initial_conditions <- base::rbind(
+  initial_conditions,
+  initial_conditions,
+  initial_conditions
+)
+rownames(initial_conditions) <- rownames(cm_matrix)
+
+# prepare the population to model as affected by the epidemic
+uk_population <- epidemics::population(
+  name = "UK",
+  contact_matrix = cm_matrix,
+  demography_vector = demography_vector,
+  initial_conditions = initial_conditions
+)
+
+# time periods
+preinfectious_period <- 4.0
+infectious_period <- 5.5
+basic_reproduction <- 2.7
+
+# rates
+infectiousness_rate <- 1.0 / preinfectious_period
+recovery_rate <- 1.0 / infectious_period
+transmission_rate <- basic_reproduction * recovery_rate
+
+# run baseline simulation with no intervention
+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_baseline
+
+# challenge ------------------------
+
+# Run this visualization of the baseline model using ggplot2
+# Then share with the tutor
+
+output_baseline %>%
+  filter(compartment == "infectious") %>%
+  ggplot(aes(
+    x = time,
+    y = value,
+    linetype = demography_group,
+    colour = compartment
+  )) +
+  geom_line()
+
+# ----------------------------------
+
+# nolint end
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