rt_opts: Time-Varying Reproduction Number Options

In EpiNow2: Estimate and Forecast Real-Time Infection Dynamics

Time-Varying Reproduction Number Options

Description

Defines a list specifying the optional arguments for the time-varying reproduction number. Custom settings can be supplied which override the defaults.

Usage

rt_opts(
  prior = LogNormal(mean = 1, sd = 1),
  use_rt = TRUE,
  rw = 0,
  use_breakpoints = TRUE,
  future = "latest",
  gp_on = c("R_t-1", "R0"),
  pop = Fixed(0),
  pop_period = c("forecast", "all"),
  pop_floor = 1,
  growth_method = c("infections", "infectiousness")
)

Arguments

prior	A ⁠<dist_spec>⁠ giving the prior of the initial reproduction number. Ignored if use_rt is FALSE. Defaults to a LogNormal distribution with mean of 1 and standard deviation of 1: LogNormal(mean = 1, sd = 1). A lower limit of 0 will be enforced automatically.
use_rt	Logical, defaults to TRUE. Should Rt be used to generate infections and hence reported cases.
rw	Numeric step size of the random walk, defaults to 0. To specify a weekly random walk set rw = 7. For more custom break point settings consider passing in a breakpoints variable as outlined in the next section.
use_breakpoints	Logical, defaults to TRUE. Should break points be used if present as a breakpoint variable in the input data. Break points should be defined as 1 if present and otherwise 0. By default breakpoints are fit jointly with a global non-parametric effect and so represent a conservative estimate of break point changes (alter this by setting gp = NULL).
future	A character string or integer. This argument indicates how to set future Rt values. Supported options are to project using the Rt model ("project"), to use the latest estimate based on partial data ("latest"), to use the latest estimate based on data that is over 50% complete ("estimate"). If an integer is supplied then the Rt estimate from this many days into the future (or past if negative) past will be used forwards in time.
gp_on	Character string, defaulting to "R_t-1". Indicates how the Gaussian process, if in use, should be applied to Rt. Currently supported options are applying the Gaussian process to the last estimated Rt (i.e Rt = Rt-1 * GP), and applying the Gaussian process to a global mean (i.e Rt = R0 * GP). Both should produced comparable results when data is not sparse but the method relying on a global mean will revert to this for real time estimates, which may not be desirable.
pop	A ⁠<dist_spec>⁠ giving the initial susceptible population size. Used to adjust Rt estimates based on the proportion of the population that is susceptible. Defaults to Fixed(0) which means no population adjustment is done. See also pop_floor for the numerical stability floor used when population adjustment is enabled. When pop is specified, returned Rt estimates are adjusted for susceptible depletion (accounting for population immunity), and unadjusted Rt estimates are also provided in a separate output variable R_unadjusted. Adjusted Rt represents the effective reproduction number given the current susceptible population, whilst unadjusted Rt represents the reproduction number that would occur in a fully susceptible population.
pop_period	Character string, defaulting to "forecast". Controls when susceptible population adjustment is applied. "forecast" only applies the adjustment to forecasts whilst "all" applies it to both data and forecasts.
pop_floor	Numeric. Minimum susceptible population used as a floor when adjusting for population depletion. This prevents numerical instability (division by zero) when the susceptible population approaches zero. Defaults to 1.0. Can be interpreted as representing a minimal ongoing import level. Note that if pop_floor > 0, cumulative infections can exceed the population size, though this effect is negligible when pop_floor is very small compared to the population size.
growth_method	Method used to compute growth rates from Rt. Options are "infections" (default) and "infectiousness". The option "infections" uses the classical approach, i.e. computing the log derivative on the number of new infections. The option "infectiousness" uses an alternative approach by Parag et al., which computes the log derivative of the infectiousness (i.e. the convolution of past infections with the generation time) and shifts it by the mean generation time. This can provide better stability and temporal matching with Rt. Note that, due to the temporal shift the "infectiousness" method results in undefined (NaN) growth rates for the most recent time points (equal to the mean generation time).

Value

An ⁠<rt_opts>⁠ object with settings defining the time-varying reproduction number.

References

Parag, K. V., Thompson, R. N. & Donnelly, C. A. Are epidemic growth rates more informative than reproduction numbers? Journal of the Royal Statistical Society: Series A (Statistics in Society) 185, S5–S15 (2022).

Examples

# default settings
rt_opts()

# add a custom length scale
rt_opts(prior = LogNormal(mean = 2, sd = 1))

# add a weekly random walk
rt_opts(rw = 7)

EpiNow2 documentation built on June 17, 2026, 1:07 a.m.