Wallinga-Lipsitch reproduction number from growth rates

Calculate a reproduction number estimate from growth rate using the Wallinga 2007 estimation using empirical generation time distribution. This uses resampling to transmit uncertainty in growth rate estimates. This also handles time-series that are not on a daily cadence (although this is experimental). The reproduction number estimate is neither a instantaneous (backward looking) nor case (forward looking) reproduction number but somewhere between the two.

Usage

rt_from_growth_rate(
  df = i_growth_rate,
  ip = i_empirical_ip,
  bootstraps = 1000,
  seed = Sys.time(),
  .progress = interactive()
)

Arguments

df

Growth rate estimates - a dataframe with columns:

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

• growth.fit (double) - an estimate of the growth rate

• growth.se.fit (positive_double) - the standard error the growth rate

• growth.0.025 (double) - lower confidence limit of the growth rate

• growth.0.5 (double) - median estimate of the growth rate

• growth.0.975 (double) - upper confidence limit of the growth rate

Any grouping allowed.

ip

Infectivity profile - a dataframe with columns:

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

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

• a0 (double) - the beginning of the time period (in days)

• a1 (double) - the end of the time period (in days)

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

A default value is defined.

bootstraps

• the number of bootstraps to take to calculate for each point.

seed

a random number generator seed

.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_growth_rate()

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)))
}