Calculate the reproduction number from a growth rate estimate and an infectivity profile

This function uses a single empirical distribution for the infectivity profile / generation time. If multiple are provided then the average central value is chosen (i.e. this does not propagate uncertainty in infectivity profile)

Usage

wallinga_lipsitch(
  r,
  y = i_empirical_ip,
  a1 = seq(0.5, length.out = length(y)),
  a0 = dplyr::lag(a1, default = 0)
)

Arguments

r

a growth rate (may be a vector)

y

an empirical infectivity profile either as a probability vector or as a dataframe of format: 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)

Minimally grouped by: boot (and other groupings allowed). A default value is defined.

a1

the end time of the infectivity profile probability estimate (defaults to 0.5,1.5,2.5,...).

a0

the start time of the infectivity profile probability estimate (defaults to 0,0.5,1.5,...).

Value

a reproduction number estimate based on r

Examples

# using a probability vector.
wallinga_lipsitch(r=seq(-0.1,0.1,length.out=9), y=stats::dgamma(1:50, 5,2))
#> [1] 0.8524904 0.8882735 0.9247920 0.9620371 1.0000000 1.0386712 1.0780412
#> [8] 1.1181002 1.1588382

# using an infectivity profile
wallinga_lipsitch(r=seq(-0.1,0.1,length.out=9), y=test_ip)
#> [1] 0.5921890 0.6783048 0.7743792 0.8812971 1.0000000 1.1314881 1.2768221
#> [8] 1.4371252 1.6135852