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task-4-outbreak_distribution.R
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# HPRU script
## Outbreak distribution size
# This script builds on the concepts outlined in these vignettes:
# https://epiverse-trace.github.io/epichains/articles/epichains.html
# https://epiverse-trace.github.io/epichains/articles/projecting_incidence.html
# Load required packages --------------------------------------------------
library(epichains)
library(ggplot2)
library(epiparameter)
# Choose a seed so results are reproducible -------------------------------
set.seed(1)
# Define outbreak parameter space -----------------------------------------
statistic <- "size"
offspring_dist <- "rpois"
R <- seq(0.1, 1.0, 0.1)
scenarios <- expand.grid(
offspring_dist = offspring_dist,
statistic = statistic,
R = R,
stringsAsFactors = FALSE
)
# number of simulations to run
n_chains <- 1000
# outbreak size groupings
breaks <- c(0, 2, 5, 10, 20, 50, 100, Inf)
# Simulate outbreak size distribution (Poisson) ---------------------------
outbreak_list <- vector(mode = "list", length = nrow(scenarios))
for (i in seq_len(nrow(scenarios))) {
offspring_dist_fun <- match.fun(scenarios[i, "offspring_dist"])
outbreak_list[[i]] <- epichains::simulate_chain_stats(
n_chains = n_chains,
statistic = scenarios[i, "statistic"],
offspring_dist = offspring_dist_fun,
lambda = scenarios[i, "R"],
stat_threshold = breaks[length(breaks) - 1] + 1
)
}
# Group outbreak sizes ----------------------------------------------------
intervals <- lapply(outbreak_list, cut, breaks = breaks)
prop <- lapply(intervals, function(interval) table(interval) / sum(table(interval)))
outbreak_size_list <- lapply(prop, as.data.frame)
for (i in seq_len(nrow(scenarios))) {
outbreak_size_list[[i]]$R <- scenarios[i, "R"]
outbreak_size_list[[i]]$offspring_dist <- scenarios[i, "offspring_dist"]
outbreak_size_list[[i]]$statistic <- scenarios[i, "statistic"]
}
outbreak_size1 <- do.call(rbind, outbreak_size_list)
head(outbreak_size1)
# Plot outbreak size distribution -----------------------------------------
ggplot2::ggplot(data = outbreak_size1) +
ggplot2::geom_col(
mapping = ggplot2::aes(x = as.factor(R), y = Freq, fill = interval)
) +
ggplot2::scale_x_discrete(name = "Reproduction number (R)") +
ggplot2::scale_y_continuous(name = "Proportion of outbreaks") +
ggplot2::scale_fill_brewer(
name = "Outbreak size",
palette = "Spectral"
) +
ggplot2::theme_bw()
# Change transmission chain statistic to length ---------------------------
scenarios$statistic <- "length"
# Simulate outbreak length distribution -----------------------------------
outbreak_list <- vector(mode = "list", length = nrow(scenarios))
for (i in seq_len(nrow(scenarios))) {
offspring_dist_fun <- match.fun(scenarios[i, "offspring_dist"])
outbreak_list[[i]] <- epichains::simulate_chain_stats(
n_chains = n_chains,
statistic = scenarios[i, "statistic"],
offspring_dist = offspring_dist_fun,
lambda = scenarios[i, "R"],
stat_threshold = breaks[length(breaks) - 1] + 1
)
}
# Group outbreak lengths --------------------------------------------------
intervals <- lapply(outbreak_list, cut, breaks = breaks)
prop <- lapply(intervals, function(interval) table(interval) / sum(table(interval)))
outbreak_length_list <- lapply(prop, as.data.frame)
for (i in seq_len(nrow(scenarios))) {
outbreak_length_list[[i]]$R <- scenarios[i, "R"]
outbreak_length_list[[i]]$offspring_dist <- scenarios[i, "offspring_dist"]
outbreak_length_list[[i]]$statistic <- scenarios[i, "statistic"]
}
outbreak_length <- do.call(rbind, outbreak_length_list)
head(outbreak_length)
# Plot outbreak length distribution ---------------------------------------
ggplot2::ggplot(data = outbreak_length) +
ggplot2::geom_col(
mapping = ggplot2::aes(x = as.factor(R), y = Freq, fill = interval)
) +
ggplot2::scale_x_discrete(name = "Reproduction number (R)") +
ggplot2::scale_y_continuous(name = "Proportion of outbreaks") +
ggplot2::scale_fill_brewer(
name = "Outbreak length",
palette = "Spectral"
) +
ggplot2::theme_bw()
# Change offspring distribution to Negative binomial ----------------------
statistic <- "size"
offspring_dist <- "rnbinom"
R <- seq(0.1, 1.0, 0.1)
k <- c(0.1, 5, 10, 1000)
scenarios <- expand.grid(
offspring_dist = offspring_dist,
statistic = statistic,
R = R,
k = k,
stringsAsFactors = FALSE
)
# Simulate outbreak size distribution (Negative binomial) -----------------
outbreak_list <- vector(mode = "list", length = nrow(scenarios))
for (i in seq_len(nrow(scenarios))) {
offspring_dist_fun <- match.fun(scenarios[i, "offspring_dist"])
outbreak_list[[i]] <- epichains::simulate_chain_stats(
n_chains = n_chains,
statistic = scenarios[i, "statistic"],
offspring_dist = offspring_dist_fun,
mu = scenarios[i, "R"],
size = scenarios[i, "k"],
stat_threshold = breaks[length(breaks) - 1] + 1
)
}
# Group outbreak sizes ----------------------------------------------------
intervals <- lapply(outbreak_list, cut, breaks = breaks)
prop <- lapply(intervals, function(interval) table(interval) / sum(table(interval)))
outbreak_size_list <- lapply(prop, as.data.frame)
for (i in seq_len(nrow(scenarios))) {
outbreak_size_list[[i]]$R <- scenarios[i, "R"]
outbreak_size_list[[i]]$k <- scenarios[i, "k"]
outbreak_size_list[[i]]$offspring_dist <- scenarios[i, "offspring_dist"]
outbreak_size_list[[i]]$statistic <- scenarios[i, "statistic"]
}
outbreak_size2 <- do.call(rbind, outbreak_size_list)
head(outbreak_size2)
# Plot outbreak size distribution (Negative binomial) ---------------------
ggplot2::ggplot(data = outbreak_size2) +
ggplot2::geom_col(
mapping = ggplot2::aes(x = as.factor(R), y = Freq, fill = interval)
) +
ggplot2::scale_x_discrete(name = "Reproduction number (R)") +
ggplot2::scale_y_continuous(name = "Proportion of outbreaks") +
ggplot2::scale_fill_brewer(
name = "Outbreak size",
palette = "Spectral"
) +
ggplot2::facet_wrap(
facets = c("k"),
labeller = ggplot2::label_both
) +
ggplot2::theme_bw()
# Simulating outbreak sizes for past outbreaks ----------------------------
offspring_dists <- epiparameter::epiparameter_db(
epi_name = "offspring distribution"
)
# Check all empirical distributions are the same --------------------------
length(unique(vapply(offspring_dists, family, FUN.VALUE = character(1)))) == 1
# Simulate empirical outbreak size distributions --------------------------
outbreak_list <- vector(mode = "list", length = length(offspring_dists))
for (i in seq_along(offspring_dists)) {
offspring_dist_fun <- match.fun(paste0("r", family(offspring_dists[[i]])))
outbreak_list[[i]] <- epichains::simulate_chain_stats(
n_chains = n_chains,
statistic = "size",
offspring_dist = offspring_dist_fun,
mu = get_parameters(offspring_dists[[i]])[["mean"]],
size = get_parameters(offspring_dists[[i]])[["dispersion"]],
stat_threshold = breaks[length(breaks) - 1] + 1
)
}
# Group outbreak sizes ----------------------------------------------------
# paste suffix as some diseases have multiple offspring distributions
diseases <- make.unique(
vapply(offspring_dists, `[[`, FUN.VALUE = character(1), "disease")
)
intervals <- lapply(outbreak_list, cut, breaks = breaks)
prop <- lapply(intervals, function(interval) table(interval) / sum(table(interval)))
outbreak_size_list <- lapply(prop, as.data.frame)
for (i in seq_along(offspring_dists)) {
outbreak_size_list[[i]]$R <- get_parameters(offspring_dists[[i]])[["mean"]]
outbreak_size_list[[i]]$k <- get_parameters(offspring_dists[[i]])[["dispersion"]]
outbreak_size_list[[i]]$offspring_dist <- family(offspring_dists[[i]])
outbreak_size_list[[i]]$disease <- diseases[i]
outbreak_size_list[[i]]$statistic <- "size"
}
outbreak_size3 <- do.call(rbind, outbreak_size_list)
outbreak_size3 <- outbreak_size3 |>
dplyr::mutate(
disease = stringr::str_remove(disease, "\\.\\d+$"),
interval = factor(interval, levels = unique(interval)), # preserve order
# Create a label combining R and k for each disease
disease_label = paste0(disease, "\n(R=", R, ", k=", k, ")")
)
head(outbreak_size3)
# Plot empirical outbreak sizes -------------------------------------------
ggplot2::ggplot(data = outbreak_size3) +
ggplot2::geom_col(
mapping = ggplot2::aes(x = as.factor(disease_label), y = Freq, fill = interval)
) +
ggplot2::scale_x_discrete(
name = "Disease"
) +
ggplot2::scale_y_continuous(name = "Proportion of outbreaks") +
ggplot2::scale_fill_brewer(
name = "Outbreak size",
palette = "Spectral"
) +
coord_flip() +
ggplot2::theme_bw()