Description Usage Arguments Value See Also Examples
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 |
object |
A fitted model object returned by |
... |
Additional arguments for
|
groups |
Either |
dates |
A length 2 vector of |
date_breaks |
A string giving the distance between date tick labels.
Default is |
date_format |
This function attempts to coerce the |
levels |
A numeric vector defining the levels of the plotted credible intervals. |
by_100k |
If |
log |
If |
A ggplot
object which can be further modified.
plot_rt
, plot_obs
, plot_infections
, posterior_infectious
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | 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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