Because outbreaks often grow exponentially at first, a small
change in R can have a big effect on the expected number of cases
after a few generations. We’ve just seen that with an R of 2, we’d
expect 32 new cases in the fifth generation of the outbreak. If R were
3 instead, we’d expect 243 at this same point.
Example of an outbreak where each case infects two other people.
Circles are cases, arrows show route of transmissionOne of the reasons R has become so popular is that it can be
estimated from real-life data. From to Ebola, R makes it possible
to quantify and compare transmission for different diseases. Much of
this popularity is down to Robert May and his long-standing
collaborator Roy Anderson. During the late 1970s, the pair had
helped bring epidemic research to a new audience. Both had a
background in ecology, which gave them a more practical outlook
than the mathematicians who’d preceded them. They were interested
in data and how models could apply to real-life situations. In 1980,
May read a draft paper by Paul Fine and Jacqueline Clarkson of the
Ross Institute, who had used a reproduction number approach to
analyse measles epidemics.[44] Realising its potential, May and