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 doesnâ€™t seem to count the cases that havenâ€™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?