diff --git a/vignettes/conmat-population.Rmd b/vignettes/conmat-population.Rmd
index 96a2a997..50a1a1e2 100644
--- a/vignettes/conmat-population.Rmd
+++ b/vignettes/conmat-population.Rmd
@@ -8,9 +8,22 @@ vignette: >
 ---
 
 ```{r, include = FALSE}
+all_times <- list()  # store the time for each chunk
+knitr::knit_hooks$set(time_it = local({
+  now <- NULL
+  function(before, options) {
+    if (before) {
+      now <<- Sys.time()
+    } else {
+      res <- difftime(Sys.time(), now)
+      all_times[[options$label]] <<- res
+    }
+  }
+}))
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#>"
+  comment = "#>",
+  time_it = TRUE
 )
 ```
 
@@ -212,3 +225,8 @@ If you want to use `conmat_population` within your R package, then please get in
 # Conclusion
 
 That's how we can use the conmat population information! Please go ahead and use and enjoy!
+
+```{r}
+all_times
+```
+
diff --git a/vignettes/data-sources.Rmd b/vignettes/data-sources.Rmd
index 6c85fcee..b5b20302 100644
--- a/vignettes/data-sources.Rmd
+++ b/vignettes/data-sources.Rmd
@@ -8,9 +8,22 @@ vignette: >
 ---
 
 ```{r, include = FALSE}
+all_times <- list()  # store the time for each chunk
+knitr::knit_hooks$set(time_it = local({
+  now <- NULL
+  function(before, options) {
+    if (before) {
+      now <<- Sys.time()
+    } else {
+      res <- difftime(Sys.time(), now)
+      all_times[[options$label]] <<- res
+    }
+  }
+}))
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#>"
+  comment = "#>",
+  time_it = TRUE
 )
 ```
 
@@ -203,3 +216,8 @@ eyre_transmission_probabilities %>%
     axis.text = element_text(angle = 45, hjust = 1)
   )
 ```
+
+```{r}
+all_times
+```
+
diff --git a/vignettes/example-pipeline.Rmd b/vignettes/example-pipeline.Rmd
index 402dc243..7edc448a 100644
--- a/vignettes/example-pipeline.Rmd
+++ b/vignettes/example-pipeline.Rmd
@@ -8,9 +8,22 @@ vignette: >
 ---
 
 ```{r, include = FALSE}
+all_times <- list()  # store the time for each chunk
+knitr::knit_hooks$set(time_it = local({
+  now <- NULL
+  function(before, options) {
+    if (before) {
+      now <<- Sys.time()
+    } else {
+      res <- difftime(Sys.time(), now)
+      all_times[[options$label]] <<- res
+    }
+  }
+}))
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#>"
+  comment = "#>",
+  time_it = TRUE
 )
 ```
 
@@ -268,3 +281,8 @@ This is a bit briefer than the two step process, and might be preferable to crea
 <!-- The initial phase of COVID-19 pandemic saw a significant age dependence in the distribution of confirmed cases with fewer confirmed cases among children. This might have been a result of younger ages being less susceptible to infection and/or less likely to exhibit clinical signs when infected [ [Davies et al](https://www.nature.com/articles/s41591-020-0962-9)] . A population's clinical fraction and age-varying susceptibility to infection both profoundly influence the likelihood that an infectious agent would spread there. This likelihood that an infectious agent would spread from an infected source to a new susceptible host and infect the host is known as transmission probability. -->
 
 <!-- The age and setting specific relative per-contact transmission probability matrices when combined with contact matrices could be used to produce the setting-specific relative next generation matrices (NGMs) which is used to obtain the distribution of numbers of new cases in each generation of infection from any arbitrary initial number of introduced infections. Conmat creates these age and setting specific relative per-contact transmission probability matrices through `get_setting_transmission_matrices()` by making use of clinical fraction and relative susceptibility parameters from Davies et al available through `davies_age_extended`.  -->
+
+```{r}
+all_times
+```
+
diff --git a/vignettes/getting-started.Rmd b/vignettes/getting-started.Rmd
index ec7f9aa7..c3eee958 100644
--- a/vignettes/getting-started.Rmd
+++ b/vignettes/getting-started.Rmd
@@ -8,9 +8,22 @@ vignette: >
 ---
 
 ```{r, include = FALSE}
+all_times <- list()  # store the time for each chunk
+knitr::knit_hooks$set(time_it = local({
+  now <- NULL
+  function(before, options) {
+    if (before) {
+      now <<- Sys.time()
+    } else {
+      res <- difftime(Sys.time(), now)
+      all_times[[options$label]] <<- res
+    }
+  }
+}))
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#>"
+  comment = "#>",
+  time_it = TRUE
 )
 ```
 
@@ -310,3 +323,8 @@ autoplot(
   title = "Setting-specific synthetic contact matrices (fairfield 2020 projected)"
 )
 ```
+
+```{r}
+all_times
+```
+
diff --git a/vignettes/other-data-sources.Rmd b/vignettes/other-data-sources.Rmd
index 51b92503..623a9c5e 100644
--- a/vignettes/other-data-sources.Rmd
+++ b/vignettes/other-data-sources.Rmd
@@ -8,9 +8,22 @@ vignette: >
 ---
 
 ```{r, include = FALSE}
+all_times <- list()  # store the time for each chunk
+knitr::knit_hooks$set(time_it = local({
+  now <- NULL
+  function(before, options) {
+    if (before) {
+      now <<- Sys.time()
+    } else {
+      res <- difftime(Sys.time(), now)
+      all_times[[options$label]] <<- res
+    }
+  }
+}))
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#>"
+  comment = "#>",
+  time_it = TRUE
 )
 ```
 
@@ -99,3 +112,8 @@ ngm_italy_vacc <- apply_vaccination(
 
 ngm_italy_vacc
 ```
+
+```{r}
+all_times
+```
+
diff --git a/vignettes/parallel-computing.Rmd b/vignettes/parallel-computing.Rmd
index eadf9013..849c5d2f 100644
--- a/vignettes/parallel-computing.Rmd
+++ b/vignettes/parallel-computing.Rmd
@@ -8,9 +8,22 @@ vignette: >
 ---
 
 ```{r, include = FALSE}
+all_times <- list()  # store the time for each chunk
+knitr::knit_hooks$set(time_it = local({
+  now <- NULL
+  function(before, options) {
+    if (before) {
+      now <<- Sys.time()
+    } else {
+      res <- difftime(Sys.time(), now)
+      all_times[[options$label]] <<- res
+    }
+  }
+}))
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#>"
+  comment = "#>",
+  time_it = TRUE
 )
 ```
 
@@ -70,3 +83,8 @@ synthetic_settings_5y_perth <- predict_setting_contacts(
   age_breaks = c(seq(0, 85, by = 5), Inf)
 )
 ```
+
+```{r}
+all_times
+```
+
diff --git a/vignettes/sir-model.Rmd b/vignettes/sir-model.Rmd
index da0ef35c..36949898 100644
--- a/vignettes/sir-model.Rmd
+++ b/vignettes/sir-model.Rmd
@@ -7,10 +7,24 @@ vignette: >
     %\VignetteEncoding{UTF-8}
 ---
 ```{r, include = FALSE}
+all_times <- list()  # store the time for each chunk
+knitr::knit_hooks$set(time_it = local({
+  now <- NULL
+  function(before, options) {
+    if (before) {
+      now <<- Sys.time()
+    } else {
+      res <- difftime(Sys.time(), now)
+      all_times[[options$label]] <<- res
+    }
+  }
+}))
 knitr::opts_chunk$set(
   collapse = TRUE,
-  comment = "#>"
+  comment = "#>",
+  time_it = TRUE
 )
+
 ```
 
 ```{r setup}
@@ -44,10 +58,30 @@ people at a given time point. We can model how these numbers change at each time
 
 We start with a complicated version of a relatively simple model: an age-stratified SIR Model, but with all age groups acting exactly the same.
 
+For the sake of speed, we will use just 3 age bands. 
 We will use 17 age groups, each in 5 year age bands, and turn these into a `conmat_population` object. This is an object that knows which columns represent age and population, which is used by other functions within `conmat`.
 
 ```{r}
 #| label: create-population
+age_bands <- seq(0, 80, length.out = 3)
+n_age_bands <- length(age_bands)
+homogeneous_population <- data.frame(
+  age = age_bands,
+  population = rep(100, times = n_age_bands)
+) |>
+  as_conmat_population(
+    age = age,
+    population = population
+  )
+
+homogeneous_population
+```
+
+As an exercise, we recommend that the user explores using many age bands, e.g., with
+
+```{r}
+#| label: create-population-many
+#| eval: false
 homogeneous_population <- data.frame(
   age = seq(0, 80, by = 10),
   population = rep(100, times = 9)
@@ -64,8 +98,8 @@ Then, we extrapolate these into a set of contact matrices, which we can construc
 
 ```{r}
 #| label: homogenous-contact
-age_breaks_0_80_plus <- c(seq(0, 80, by = 10), Inf)
-mat_ones <- matrix(1, nrow = 9, ncol = 9)
+age_breaks_0_80_plus <- c(age_bands, Inf)
+mat_ones <- matrix(1, nrow = n_age_bands, ncol = n_age_bands)
 
 # Relative number of contacts between individuals in 2 age categories
 # Think of as P(contact)
@@ -84,7 +118,7 @@ Similarly, we construct a set of transmission matrices, which provide the probab
 
 ```{r}
 #| label: homogenous-transmission
-mat_05 <- matrix(0.05, nrow = 9, ncol = 9)
+mat_05 <- matrix(0.05, nrow = n_age_bands, ncol = n_age_bands)
 transmission_matrix <- transmission_probability_matrix(
   home = mat_05,
   work = mat_05,
@@ -100,13 +134,13 @@ We also need to set up our population structures. We'll have all the S states, t
 
 ```{r}
 #| label: initial-condition
-S0 <- rep(999, times = 9)
-I0 <- rep(1, times = 9)
-R0 <- rep(0, times = 9)
+S0 <- rep(999, times = n_age_bands)
+I0 <- rep(1, times = n_age_bands)
+R0 <- rep(0, times = n_age_bands)
 initial_condition <- c(S0, I0, R0)
 names(initial_condition) <- paste(
-  rep(c("S0", "I0", "R0"), each = 9),
-  age_breaks_0_80_plus[1:9],
+  rep(c("S0", "I0", "R0"), each = n_age_bands),
+  age_breaks_0_80_plus[1:n_age_bands],
   sep = "_"
 )
 
@@ -122,9 +156,9 @@ parameters <- list(
   "transmission_matrix" = transmission_matrix,
   "homogeneous_contact" = homogeneous_contact,
   "gamma" = 1,
-  "s_indexes" = 1:9,
-  "i_indexes" = 10:18,
-  "r_indexes" = 19:27
+  "s_indexes" = 1,
+  "i_indexes" = 2,
+  "r_indexes" = 3
 )
 
 parameters
@@ -218,7 +252,7 @@ Now, let's compare this to an SIR model with no stratification - as in, no age g
 ```{r}
 #| label: standard-sir
 parameters_sir <- c("beta" = 1.8, "gamma" = 1)
-initial_condition_sir <- c("S" = 8991, "I" = 9, "R" = 0)
+initial_condition_sir <- c("S" = 8991, "I" = n_age_bands, "R" = 0)
 
 sir <- function(time, state, parameters) {
   N <- sum(state)
@@ -564,3 +598,8 @@ conmat_prem_soln %>%
 
 
 So now we have as fair of a comparison of the two matrices as we will get, and yet, there are significant differences in the dynamics of the two models.
+
+```{r}
+all_times
+```
+
diff --git a/vignettes/visualising-conmat.Rmd b/vignettes/visualising-conmat.Rmd
index 5c144efd..b6277845 100644
--- a/vignettes/visualising-conmat.Rmd
+++ b/vignettes/visualising-conmat.Rmd
@@ -7,16 +7,30 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-```{r, include = FALSE}
+```{r}
+all_times <- list()  # store the time for each chunk
+knitr::knit_hooks$set(time_it = local({
+  now <- NULL
+  function(before, options) {
+    if (before) {
+      now <<- Sys.time()
+    } else {
+      res <- difftime(Sys.time(), now)
+      all_times[[options$label]] <<- res
+    }
+  }
+}))
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
+  time_it = TRUE,
   fig.width = 8,
   fig.height = 8,
   dev = "png"
 )
 ```
 
+
 ```{r setup}
 library(conmat)
 ```