plot_infectious {epidemia}

R Documentation

Plot total infectiousness over time.

Description

Plots credible intervals and the median for total infectiousness over time. This is basically a weighted sum of all infected individuals. Each infected individual is weighted by how infectious they are expected to be given how long they have been infected for. The user can control the interval levels (i.e. 30%, 50% etc.) and the plotted group(s). This is a generic function.

Usage

plot_infectious(object, ...)

## S3 method for class 'epimodel'
plot_infectious(
  object,
  groups = NULL,
  dates = NULL,
  date_breaks = "2 weeks",
  date_format = "%Y-%m-%d",
  levels = c(30, 60, 90),
  by_100k = FALSE,
  log = FALSE,
  ...
)

Arguments

object	A fitted model object returned by epim. See epimodel-objects.
...	Additional arguments for posterior_infectious. Examples include newdata, which allows predictions or counterfactuals.
groups	Either NULL or a character vector specifying the groups to plot for. Default is NULL, which plots all modeled groups.
dates	A length 2 vector of Date objects. This defines the start and end dates of the date-range to be plotted. Must be coercible to Date if not NA. If an element of the vector is NA then the default lower/upper limit is used. See examples.
date_breaks	A string giving the distance between date tick labels. Default is "2 weeks". This is passed as the date_breaks argument to scale_x_date. Please see here for details.
date_format	This function attempts to coerce the dates argument to a vector of Date objects. date_format is passed as the format argument to as.Date. Default is "%Y-%m-%d".
levels	A numeric vector defining the levels of the plotted credible intervals.
by_100k	If TRUE, all quantities are plotted per 100k of population. Only possible if the model used a population adjustment.
log	If TRUE, plot quantities on a log10-scale. This argument must be logical, and defaults to FALSE.

Value

A ggplot object which can be further modified.

See Also

plot_rt, plot_obs, plot_infections, posterior_infectious

Examples

data("EuropeCovid2")
data <- EuropeCovid2$data
data <- dplyr::filter(data, date > date[which(cumsum(deaths) > 10)[1] - 30])
data <- dplyr::filter(data, date < as.Date("2020-05-05"))

rt <- epirt(
  formula = R(country, date) ~ 0 + (1 + public_events + schools_universities + 
     self_isolating_if_ill + social_distancing_encouraged + lockdown || country) + 
     public_events + schools_universities + self_isolating_if_ill + 
     social_distancing_encouraged + lockdown,
  prior = shifted_gamma(shape=1/6, scale = 1, shift = log(1.05)/6),
  prior_covariance = rstanarm::decov(shape = c(2, rep(0.5, 5)),scale=0.25),
  link = scaled_logit(6.5)
)

inf <- epiinf(gen = EuropeCovid$si, seed_days = 6)

deaths <- epiobs(
  formula = deaths ~ 1,
  i2o = EuropeCovid2$inf2death,
  prior_intercept = rstanarm::normal(0,0.2),
  link = scaled_logit(0.02)
)

args <- list(rt=rt, inf=inf, obs=deaths, data=data, seed=12345)
args$group_subset <- c("Italy", "Austria", "Germany")
args$algorithm <- "fullrank"
args$iter <- 1e4
args$tol_rel_obj <- 1e-3

fm <- do.call(epim, args)

# different ways of using plot_rt
p <- plot_rt(fm) # default, plots all groups and dates
p <- plot_rt(fm, dates=c("2020-03-21", NA)) # plot 21 March 2020 onwards
p <- plot_rt(fm, dates=c(NA, "2020-03-20")) # plot up to  20 March 2020
p <- plot_rt(fm, dates=c("2020-03-20", "2020-04-20"))
p <- plot_rt(fm,
         dates=c("2020-03-20", "2020-04-20"),
        date_breaks="1 day") # ticks every day
p <- plot_rt(fm,
       dates=c("2020-20-03", "2020-20-04"),
       date_format="%Y-%d-%m") # (different date format)

# other plotting functions
p <- plot_obs(fm, type = "deaths")
p <- plot_infections(fm)
p <- plot_infectious(fm) 

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