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modelling_interventions.Rmd
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modelling_interventions.Rmd
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---
title: "Getting started with modelling interventions targeting social contacts"
output:
bookdown::html_vignette2:
fig_caption: yes
code_folding: show
pkgdown:
as_is: true
bibliography: references.json
link-citations: true
vignette: >
%\VignetteIndexEntry{Getting started with modelling interventions targeting social contacts}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
dpi = 150
)
```
```{r setup}
library(epidemics)
library(dplyr)
library(ggplot2)
```
## Prepare population and initial conditions
Prepare population and contact data.
::: {.alert .alert-info}
### Note on social contacts data {-}
_epidemics_ expects social contacts matrices $M_{ij}$ to represent contacts to $i$ from $j$ [@wallinga2006], such that $q M_{ij} / n_i$ is the probability of infection, where $q$ is a scaling factor dependent on infection transmissibility, and $n_i$ is the population proportion of group $i$.
Social contacts matrices provided by the [_socialmixr_](https://CRAN.R-project.org/package=socialmixr) package follow the opposite convention, where $M_{ij}$ represents [contacts from group $i$ to group $j$](https://epiforecasts.io/socialmixr/articles/socialmixr.html#usage).
Thus social contact matrices from _socialmixr_ need to be transposed (using `t()`) before they are used with _epidemics_.
:::
```{r}
# load contact and population data from socialmixr::polymod
polymod <- socialmixr::polymod
contact_data <- socialmixr::contact_matrix(
polymod,
countries = "United Kingdom",
age.limits = c(0, 20, 40),
symmetric = TRUE
)
# prepare contact matrix
contact_matrix <- t(contact_data$matrix)
# prepare the demography vector
demography_vector <- contact_data$demography$population
names(demography_vector) <- rownames(contact_matrix)
```
Prepare initial conditions for each age group.
```{r}
# initial conditions
initial_i <- 1e-6
initial_conditions <- c(
S = 1 - initial_i, E = 0, I = initial_i, R = 0, V = 0
)
# build for all age groups
initial_conditions <- rbind(
initial_conditions,
initial_conditions,
initial_conditions
)
# assign rownames for clarity
rownames(initial_conditions) <- rownames(contact_matrix)
```
Prepare a population as a `population` class object.
```{r}
uk_population <- population(
name = "UK",
contact_matrix = contact_matrix,
demography_vector = demography_vector,
initial_conditions = initial_conditions
)
```
## Prepare an intervention
Prepare an intervention to simulate school closures.
```{r}
# prepare an intervention with a differential effect on age groups
close_schools <- intervention(
name = "School closure",
type = "contacts",
time_begin = 200,
time_end = 300,
reduction = matrix(c(0.5, 0.001, 0.001))
)
# examine the intervention object
close_schools
```
## Run epidemic model
```{r}
# run an epidemic model using `epidemic`
output <- model_default(
population = uk_population,
intervention = list(contacts = close_schools),
time_end = 600, increment = 1.0
)
```
## Prepare data and visualise infections
Plot epidemic over time, showing only the number of individuals in the exposed and infected compartments.
```{r class.source = 'fold-hide'}
# plot figure of epidemic curve
filter(output, compartment %in% c("exposed", "infectious")) %>%
ggplot(
aes(
x = time,
y = value,
col = demography_group,
linetype = compartment
)
) +
geom_line() +
annotate(
geom = "rect",
xmin = close_schools$time_begin,
xmax = close_schools$time_end,
ymin = 0, ymax = 500e3,
fill = alpha("red", alpha = 0.2),
lty = "dashed"
) +
annotate(
geom = "text",
x = mean(c(close_schools$time_begin, close_schools$time_end)),
y = 400e3,
angle = 90,
label = "School closure"
) +
scale_y_continuous(
labels = scales::comma
) +
scale_colour_brewer(
palette = "Dark2",
name = "Age group"
) +
expand_limits(
y = c(0, 500e3)
) +
coord_cartesian(
expand = FALSE
) +
theme_bw() +
theme(
legend.position = "top"
) +
labs(
x = "Simulation time (days)",
linetype = "Compartment",
y = "Individuals"
)
```
## References