plot_infectious {epidemia}  R Documentation 
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.
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,
...
)
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
data("EuropeCovid2")
data < EuropeCovid2$data
data < dplyr::filter(data, date > date[which(cumsum(deaths) > 10)[1]  30])
data < dplyr::filter(data, date < as.Date("20200505"))
rt < epirt(
formula = R(country, date) ~ 0 + (1 + public_events + schools_universities +
self_isolating_if_ill + social_distancing_encouraged + lockdown  country) +
public_events + schools_universities + self_isolating_if_ill +
social_distancing_encouraged + lockdown,
prior = shifted_gamma(shape=1/6, scale = 1, shift = log(1.05)/6),
prior_covariance = rstanarm::decov(shape = c(2, rep(0.5, 5)),scale=0.25),
link = scaled_logit(6.5)
)
inf < epiinf(gen = EuropeCovid$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 < 1e3
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("20200321", NA)) # plot 21 March 2020 onwards
p < plot_rt(fm, dates=c(NA, "20200320")) # plot up to 20 March 2020
p < plot_rt(fm, dates=c("20200320", "20200420"))
p < plot_rt(fm,
dates=c("20200320", "20200420"),
date_breaks="1 day") # ticks every day
p < plot_rt(fm,
dates=c("20202003", "20202004"),
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)