add activity solution

instructors/05-miscellanea-tutors.qmd

@@ -441,6 +441,14 @@ Room 3
 
 ::: {.content-visible unless-format="docx"}
 
+## Code
+
+### Activity 2
+
+```{r, file = "fig/05-miscellanea-instructor-2.R", eval = FALSE}
+
+```
+
 # Continue your learning path
 
 <!-- Suggest learners to Epiverse-TRACE documentation or external resources --->

instructors/files/05-miscellanea-tutors.md

@@ -372,6 +372,161 @@ Use assumptions assigned to your room.
 | 2 | $3.0$ | Germany | {0, 20, 40, 60} | 1 | 1 |
 | 3 | $0.9$ | Netherlands | {0, 30, 50, 70} | 1 | 1 |
 
+## Code
+
+### Activity 2
+
+``` r
+# nolint start
+
+# Practical 2 -miscellaneous on final size of heterogeneous population
+# Activity 1
+
+room_number <- 1
+
+  # Load packages ----------------------------------------------------------
+library(finalsize)
+library(tidyverse)
+library(socialmixr)
+
+# Declare the value of the given R_0 ---------------------------------------
+r0 <- 1.8
+
+# load the polymod survey ----------------------------------------
+polymod <- socialmixr::polymod
+
+# Extract data for the country and age groups assigned to your room--------
+
+# get your country polymod data
+contact_data <- socialmixr::contact_matrix(
+  polymod,
+  countries = "Italy",
+  age_limits = c(0,30,50,80),
+  symmetric = TRUE
+)
+
+# view the elements of the contact data list
+# the contact matrix
+contact_data$matrix
+
+# the demography data
+contact_data$demography
+
+# get the contact matrix and demography data
+# contact_matrix <- contact_data$matrix
+demography_vector <- contact_data$demography$population
+demography_data <- contact_data$demography
+
+
+# Transpose and normalize contact matrix---------------------------------------
+
+# scale the contact matrix so the largest eigenvalue is 1.0
+# this is to ensure that the overall epidemic dynamics correctly reflect
+# the assumed value of R0
+contact_matrix <- t(contact_data$matrix)
+scaling_factor <- 1 / max(eigen(contact_matrix)$values)
+normalised_matrix <- contact_matrix * scaling_factor
+
+normalised_matrix
+
+# divide each row of the contact matrix by the corresponding demography
+# this reflects the assumption that each individual in group {j} make contacts
+# at random with individuals in group {i}
+divided_matrix <- normalised_matrix/demography_vector
+
+# Create susceptibility, and demography-susceptibility distribution matrices---
+n_demo_grps <- length(demography_vector)
+
+# Number of susceptible groups
+n_susc_groups <- 1
+# susceptibility level
+susc_guess <- 1
+
+# Declare susceptibility matrix
+
+susc_uniform <- matrix(
+    data = susc_guess,
+    nrow = n_demo_grps,
+    ncol = n_susc_groups
+)
+
+# Declare demography-susceptibility distribution matrix
+p_susc_uniform <- matrix(
+  data =  1,
+  nrow =  n_demo_grps,
+  ncol =  n_susc_groups
+)
+
+# Calculate the final size -----------------------------------------------------
+
+final_size_data <- final_size(
+  r0 = r0,
+  contact_matrix = divided_matrix,
+  demography_vector = demography_vector,
+  susceptibility = susc_uniform,
+  p_susceptibility = p_susc_uniform
+)
+
+# View the output data frame
+final_size_data
+
+# Visualize the proportion infected in each demographic group-------------------
+
+# order demographic groups as factors
+final_size_data$demo_grp <- factor(
+  final_size_data$demo_grp,
+  levels = demography_data$age.group
+)
+# plot data
+ggplot(final_size_data) +
+  geom_col(
+    aes(
+      demo_grp, p_infected
+    ),
+    colour = "black", fill = "grey"
+  ) +
+  scale_y_continuous(
+    labels = scales::percent,
+    limits = c(0, 1)
+  ) +
+  expand_limits(
+    x = c(0.5, nrow(final_size_data) + 0.5)
+  ) +
+  theme_classic() +
+  coord_cartesian(
+    expand = FALSE
+  ) +
+  labs(
+    x = "Age group",
+    y = "% Infected"
+  )
+# Visualize the total number infected in each demographic group----------------
+# prepare demography data
+demography_data <- contact_data$demography
+
+# merge final size counts with demography vector
+final_size_data <- merge(
+  final_size_data,
+  demography_data,
+  by.x = "demo_grp",
+  by.y = "age.group"
+)
+
+# reset age group order
+final_size_data$demo_grp <- factor(
+  final_size_data$demo_grp,
+  levels = contact_data$demography$age.group
+)
+
+# multiply counts with proportion infected
+final_size_data$n_infected <- final_size_data$p_infected *
+  final_size_data$population
+
+final_size_data
+
+# nolint end
+```
+
 # Continue your learning path
 
 <!-- Suggest learners to Epiverse-TRACE documentation or external resources --->