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Reproduction number from renewal equation applied to modelled incidence using statistical re-sampling

Source: R/estimator-rt-renewal.R
rt_from_renewal.Rd

Calculate a reproduction number estimate from modelled incidence estimates, by statistical sampling from a log-normally distributed incidence estimate, such as you get from a I_t ~ Poisson(I_0 e^{rt}) model using a log link function. This is combined with uncertain infectivity profile specified as multiple discrete empirical distributions, to calculate the range of possible values for $R_t$.

Usage

rt_from_renewal(
  df = i_incidence_model,
  ip = i_discrete_ip,
  bootstraps = 1000,
  seed = Sys.time(),
  .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

infectivity profile - 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.

bootstraps

the number of samples to take at each time point. This will be rounded up to a whole multiple of the infectivity profile distribution length.

seed

a random number seed for reproducibility

.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 moderately slow and non deterministic by default.

Examples

data = test_poisson_rt_smooth

tmp2 = data %>%
   ggoutbreak::poisson_locfit_model() %>%
   ggoutbreak::rt_from_renewal(test_ip)

if (interactive()) {
  plot_rt(tmp2, date_labels="%b %y")+sim_geom_function(data,colour="red")
}