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)



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.


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).


## 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) } }
#> Loading required package: outbreaks
#> Loading required package: incidence
#> Registered S3 methods overwritten by 'ggplot2': #> method from #> [.quosures rlang #> c.quosures rlang #> print.quosures rlang
#> Warning: 22 dates with incidence of 0 ignored for fitting
#> Min. 1st Qu. Median Mean 3rd Qu. Max. #> 1.182 1.261 1.290 1.288 1.318 1.424