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()
)

Arguments

df

Growth rate estimates

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`

• 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 containing the following 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)

Must be grouped by: boot (exactly).

A default value is defined.

bootstraps

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

seed

a random number generator seed

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 %>%
  time_aggregate(count=sum(count))


if (interactive()) {
  # not run
  withr::with_options(list("ggoutbreak.keep_cdf"=TRUE),{
   tmp2 = tmp %>%
      poisson_locfit_model() %>%
      rt_from_growth_rate()
  })
}