Wallinga-Lipsitch reproduction number from growth rates

Calculate a reproduction number estimate from growth rate using the Wallinga and Lipsitch 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, as the method looks at a flux of infection at a single point in time.

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.

Details

This method is quite slow compared to some others and the default is non deterministic.

Examples

data = ggoutbreak::test_poisson_rt_smooth

tmp = data %>%
  poisson_locfit_model() %>%
  rt_from_growth_rate(ip=ggoutbreak::test_ip)

if (interactive()) {
  plot_rt(tmp, date_labels="%b %y")+sim_geom_function(data,colour="red")
}