The function r2R0 can be used to transform a growth rate into a reproduction number estimate, given a generation time distribution. This uses the approach described in Wallinga and Lipsitch (2007, Proc Roy Soc B 274:599–604) for empirical distributions. The function lm2R0_sample generates a sample of R0 values from a log-linear regression of incidence data stored in a lm object.

r2R0(r, w, trunc = 1000)

lm2R0_sample(x, w, n = 100, trunc = 1000)

## Arguments

r A vector of growth rate values. The serial interval distribution, either provided as a distcrete object, or as a numeric vector containing probabilities of the mass functions. The number of time units (most often, days), used for truncating w, whenever a distcrete object is provided. Defaults to 1000. A lm object storing a a linear regression of log-incidence over time. The number of draws of R0 values, defaulting to 100.

## Details

It is assumed that the growth rate ('r') is measured in the same time unit as the serial interval ('w' is the SI distribution, starting at time 0).

## Examples


## Ebola estimates of the SI distribution from the first 9 months of
## West-African Ebola oubtreak

mu <- 15.3 # days
sigma <- 9.3 # days
param <- gamma_mucv2shapescale(mu, sigma / mu)

if (require(distcrete)) {
w <- distcrete("gamma", interval = 1,
shape = param$shape, scale = param$scale, w = 0)

r2R0(c(-1, -0.001, 0, 0.001, 1), w)

## Use simulated Ebola outbreak and 'incidence' to get a log-linear
## model of daily incidence.

if (require(outbreaks) && require(incidence)) {
i <- incidence(ebola_sim$linelist$date_of_onset)
plot(i)
f <- fit(i[1:100])
f
plot(i[1:150], fit = f)

R0 <- lm2R0_sample(f\$model, w)
hist(R0, col = "grey", border = "white", main = "Distribution of R0")
summary(R0)
}