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Reproduction number from modelled incidence

Source: R/estimator-rt-incidence.R
rt_from_incidence.Rd

Calculate a reproduction number estimate from modelled incidence using the methods described in the vignette "Estimating the reproduction number from modelled incidence" and using a set of empirical generation time distributions. This assumes that modelled incidence has the same time unit as the ip distribution, and that this is daily, if this is not the case then rescale_model() may be able to fix it.

Usage

rt_from_incidence(
  df = i_incidence_model,
  ip = i_discrete_ip,
  approx = FALSE,
  .progress = interactive()
)

Arguments

df

modelled incidence estimate - a dataframe with columns:

  • time (ggoutbreak::time_period + group_unique) - A (usually complete) set of singular observations per unit time as a `time_period`

  • incidence.fit (double) - an estimate of the incidence rate on a log scale

  • incidence.se.fit (positive_double) - the standard error of the incidence rate estimate on a log scale

  • incidence.0.025 (positive_double) - lower confidence limit of the incidence rate (true scale)

  • incidence.0.5 (positive_double) - median estimate of the incidence rate (true scale)

  • incidence.0.975 (positive_double) - upper confidence limit of the incidence rate (true scale)

Any grouping allowed.

ip

an infectivity profile (aka generation time distribution) - a dataframe with columns:

  • boot (anything + default(1)) - a bootstrap identifier

  • probability (proportion) - the probability of new event during this period.

  • tau (integer + complete) - the days since the index event.

Minimally grouped by: boot (and other groupings allowed).

A default value is defined.

approx

use a faster, but approximate, estimate of quantiles

.progress

show a CLI progress bar

Value

A dataframe containing the following columns:

  • time (ggoutbreak::time_period + group_unique) - A (usually complete) set of singular observations per unit time as a time_period

  • rt.fit (double) - an estimate of the reproduction number

  • rt.se.fit (positive_double) - the standard error of the reproduction number

  • rt.0.025 (double) - lower confidence limit of the reproduction number

  • rt.0.5 (double) - median estimate of the reproduction number

  • rt.0.975 (double) - upper confidence limit of the reproduction number

Any grouping allowed.

Examples

tmp = ggoutbreak::england_covid %>%
  dplyr::filter(date < "2021-01-01") %>%
  time_aggregate(count=sum(count)) %>%
  poisson_locfit_model() %>%
  rt_from_incidence()


if (interactive()) {
  plot_rt(tmp, date_labels="%b %y") %above%
   ggplot2::geom_errorbar(
     data=england_consensus_rt %>% dplyr::filter(date < "2021-01-01"),
     mapping=ggplot2::aes(x=date-14,ymin=low,ymax=high),colour="grey60")+
   ggplot2::coord_cartesian(ylim=c(0.5,1.75),xlim=as.Date(c("2020-05-01",NA)))
}