I would like to estimate the degree of super-spreading of an outbreak for which I have transmission chain data. From the paper I have read on this I need to estimate the individual reproduction numbers (e.g. the number of infections caused be each case) and fit a distribution to this. However, I am unsure how to do this.
I have tried using the
epicontacts package because I know this is meant for transmission chain data, and have tried counting the number of infections using the
table function, but this doesn’t seem to count the cases that haven’t caused any infections.
I am using the
mers_korea_2015 dataset from the
outbreaks package for a reproducible example below.
library(outbreaks) library(epicontacts) ## make the epicontacts object epi <- make_epicontacts(mers_korea_2015$linelist, mers_korea_2015$contacts) ## this counts the number of infections caused but doesn't include the cases that haven't caused any infections table(epi$contacts$from)
Could anybody help me count the individual reproduction numbers correctly and fit a negative binomial distribution to this?