Update vignette and wordlist

inst/WORDLIST

@@ -4,6 +4,7 @@ Altman
 Analogs
 BH
 BMC
+Bazar
 Bjørnstad
 CMD
 COVID

vignettes/diphtheria.Rmd

@@ -24,7 +24,7 @@ knitr::opts_chunk$set(
   warning = FALSE,
   fig.width = 5,
   fig.height = 4,
-  dpi = 300
+  dpi = 150
 )
 ```
 
@@ -35,6 +35,10 @@ knitr::opts_chunk$set(
 This vignette shows how to model an outbreak of diphtheria (or a similar acute directly-transmitted infectious disease) within the setting of a humanitarian aid camp, where the camp population may fluctuate due to external factors, including influxes from crisis affected areas or evacuations to other camps or areas.
 In such situations, implementing large-scale public health measures such contact tracing and quarantine, or introducing reactive mass vaccination may be challenging.
 
+::: {.alert .alert-info}
+The [vignette on modelling a vaccination campaign](modelling_vaccination.html) shows how to model the introduction of a mass vaccination campaign for a fixed, stable population.
+::: 
+
 _epidemics_ provides a simple SEIHR compartmental model based on @finger2019, in which it is possible to vary the population of each demographic group throughout the model's simulation time and explore the resulting epidemic dynamics.
 This baseline model only tracks infections by demographic groups, and does not include variation in contacts between demographic groups (e.g. by age or occupation), as contacts are likely to be less clearly stratified in a camp setting.
 The baseline model also does not allow interventions that target social contacts (such as social distancing or quarantine), and does not include a 'vaccinated' compartment. It is therefore suited to analysis of a rapidly spreading infection prior to the introduction of any reactive vaccine campaign.
@@ -64,7 +68,8 @@ initial_conditions <- matrix(0, nrow = n_age_groups, ncol = 5)
 initial_conditions[, 1] <- demography_vector - 1
 initial_conditions[, 3] <- rep(1, n_age_groups)
 
-# camp social contact rates are assumed to be uniform within and between age groups
+# camp social contact rates are assumed to be uniform within and between
+# age groups
 camp_pop <- population(
   contact_matrix = matrix(1, nrow = n_age_groups, ncol = n_age_groups),
   demography_vector = demography_vector,
@@ -82,6 +87,7 @@ prop_vaccinated <- c(0.05, 0.2, 0.05)
 ```
 
 We run the model with its default parameters, assuming that:
+
 - diphtheria has an $R_0$ of 4.0 and a mean infectious period of 4.5 days, giving a transmissibility ($\beta$) of about 0.889;
 - diphtheria has a pre-infectious or incubation period of 3 days, giving an infectiousness rate ($\sigma$) of about 0.33; and
 - the recovery rate of diphtheria is about 0.33.
@@ -150,12 +156,24 @@ data <- model_diphtheria_cpp(
   population = camp_pop,
   population_change = pop_change
 )
+
+# summarise population change in susceptibles
+data_pop_size <- filter(data, compartment == "susceptible") |>
+  group_by(time) |>
+  summarise(
+    total_susceptibles = sum(value)
+  )
 ```
 
-```{r class.source = 'fold-hide', fig.cap="Model results from a single run showing the number of individuals infectious with diphtheria over 100 days of the outbreak, with an increase in the camp population size."}
-filter(data, compartment == "infectious") |>
-  ggplot() +
+```{r class.source = 'fold-hide', fig.cap="Model results from a single run showing the number of individuals infectious with diphtheria over 100 days of the outbreak, with an increase in the camp population size. Shaded blue region shows the number of individuals susceptible to infection (right-hand side Y axis)."}
+ggplot() +
+  geom_area(
+    data = data_pop_size,
+    aes(time, total_susceptibles / 10),
+    fill = "steelblue", alpha = 0.5
+  ) +
   geom_line(
+    data = filter(data, compartment == "infectious"),
     aes(time, value, colour = demography_group)
   ) +
   geom_vline(
@@ -174,11 +192,15 @@ filter(data, compartment == "infectious") |>
     colour = "red"
   ) +
   scale_y_continuous(
-    labels = scales::comma
+    labels = scales::comma,
+    sec.axis = dup_axis(
+      trans = function(x) x * 10,
+      name = "Individuals susceptible"
+    )
   ) +
   scale_colour_brewer(
     palette = "Dark2",
-    name = "Age group",
+    name = "Age group (individuals infectious)",
     labels = c("<5", "5-15", ">15")
   ) +
   expand_limits(