epinow | R Documentation |
This function wraps the functionality of estimate_infections()
in order
to estimate Rt and cases by date of infection and forecast these infections
into the future. In addition to the functionality of
estimate_infections()
it produces additional summary output useful for
reporting results and interpreting them as well as error catching and
reporting, making it particularly useful for production use e.g. running at
set intervals on a dedicated server.
epinow(
data,
generation_time = gt_opts(),
delays = delay_opts(),
truncation = trunc_opts(),
rt = rt_opts(),
backcalc = backcalc_opts(),
gp = gp_opts(),
obs = obs_opts(),
stan = stan_opts(),
horizon = 7,
CrIs = c(0.2, 0.5, 0.9),
filter_leading_zeros = TRUE,
zero_threshold = Inf,
return_output = is.null(target_folder),
output = c("samples", "plots", "latest", "fit", "timing"),
plot_args = list(),
target_folder = NULL,
target_date,
logs = tempdir(),
id = "epinow",
verbose = interactive(),
reported_cases
)
A list of output from estimate_infections with additional elements summarising results and reporting errors if they have occurred.
estimate_infections()
forecast_infections()
regional_epinow()
# set number of cores to use
old_opts <- options()
options(mc.cores = ifelse(interactive(), 4, 1))
# set an example generation time. In practice this should use an estimate
# from the literature or be estimated from data
generation_time <- Gamma(
shape = Normal(1.3, 0.3),
rate = Normal(0.37, 0.09),
max = 14
)
# set an example incubation period. In practice this should use an estimate
# from the literature or be estimated from data
incubation_period <- LogNormal(
meanlog = Normal(1.6, 0.06),
sdlog = Normal(0.4, 0.07),
max = 14
)
# set an example reporting delay. In practice this should use an estimate
# from the literature or be estimated from data
reporting_delay <- LogNormal(mean = 2, sd = 1, max = 10)
# example case data
reported_cases <- example_confirmed[1:40]
# estimate Rt and nowcast/forecast cases by date of infection
out <- epinow(
data = reported_cases,
generation_time = gt_opts(generation_time),
rt = rt_opts(prior = list(mean = 2, sd = 0.1)),
delays = delay_opts(incubation_period + reporting_delay)
)
# summary of the latest estimates
summary(out)
# plot estimates
plot(out)
# summary of R estimates
summary(out, type = "parameters", params = "R")
options(old_opts)
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