In chapter 2 of this book, Adam Kucharski explains the details about R, the crucial parameter in any epidemic. Right now it seems that we are at 2,3 and waiting to decrease below 2.
R is a more intuitive – and general – way to think about contagion. It simply asks: how many people would we expect a case to pass the infection on to? As we shall see in later chapters, it’s an idea that we can apply to a wide range of outbreaks, from gun violence to online memes.PS. The statistics of contagion
R is particularly useful because it tells us whether to expect a large outbreak or not. If R is below one, each infectious person will on average generate less than one additional infection. We’d therefore expect the number of cases to decline over time. However, if R is above one, the level of infection will rise on average, creating the potential for a large epidemic.
Some diseases have a relatively low R. For pandemic flu, R is generally around 1–2, which is about the same as Ebola during the early stages of the 2013–16 West Africa epidemic. On average, each Ebola case passed the virus onto a couple of other people. Other infections can spread more easily. The sars virus, which caused outbreaks in Asia in early 2003, had an R of 2–3.
R therefore depends on four factors: the duration of time a person is infectious; the average number of opportunities they have to spread the infection each day they’re infectious; the probability an opportunity results in transmission; and the average susceptibility of the population. I like to call these the ‘DOTS’ for short. Joining them together gives us the value of the reproduction number:
R = Duration × Opportunities × Transmission probability × Susceptibility
