The Thing About the Basic Reproductive Number
Cool scientific terminology is being misunderstood by millions and shared by millions more
Unless you live under a rock, you know about the situation in China and other parts of the world as it pertains to the 2019 Novel Coronavirus (2019-nCoV). Basically, someone in China came into contact with a new version of the coronavirus we all know and love, and the virus has caused thousands of cases and dozens of deaths because we humans don’t have immunity against it. The virus has now spread outside of China, and it is popping up and causing concern in other parts of the world.
Several people on social media have been pointing at certain measurements of the epidemic and making some interesting pronouncements about where the epidemic will go based on those measurements. One such measurement is the basic reproductive number. The basic reproductive number (also called “R naught”) tells you how many new infections you can expect from one case of an infectious disease.
In order to calculate the R naught, you need to know how many people who are sick had contact with a case. Did the 12 people sick have contact with 12 other people (R naught equals 1.0) or did they have contact with 2 people (R naught equals 6.0) or 1 person (R naught equals 12.0)?
Different infectious diseases have different R naught values. For example, measles’ R naught is close to 18. Whooping cough’s is between 12 and 17. The dreaded smallpox — which we eradicated through a worldwide vaccination program — has an R naught value between 4 and 7. Seasonal influenza is between 1 and 3, and maybe as high as 4.
There is some uncertainty in the exact number because, again, you need to know how many primary cases caused how many secondary cases, and that is hard to do. If a child walks into a daycare carrying measles, they may infect all other children if those other children are under the age of 12 months and likely to be too young to have received the MMR vaccine against measles. If the children were all vaccinated, the R naught is going to be zero.
There is also the matter of population density. If you get the flu from a bird while living out in the woods, the chances of you giving that flu to other people is very low (R naught close to zero). If you then travel to Hong Kong — one of the most densely populated cities in the world — and walk around coughing and sneezing, then you’re going to infect a lot of people.
At the end of the day, the R naught is not going to be calculated for this novel coronavirus with any certainty until we get a clear grip on the number of people at risk, people exposed, people sick and people dead. Without a readily available laboratory test, it is going to be difficult to know who is a case and who isn’t. It is going to take some time to figure this all out, so it is not helping anyone to make proclamations — with three or more exclamation points — about the danger of this or any other disease based on the R naught alone.
So is there any sense in even trying to calculate the R naught of this coronavirus right now? Yes, but it’s all academic. Epidemiologists on the ground are more worried about things like population density and human migration/transportation patterns than the R naught. We are more concerned with the virology of the coronavirus than the R naught. And we lose sleep at night over the misinformation and how to counter it, than we are with trying to figure out the R naught.
Now, if you’ll excuse me, I’m off to find the mystical Patient Zero in order to make a vaccine… Or whatever science fiction says finding a patient zero is good for.
René F. Najera, MPH, DrPH, is a doctor of public health, an epidemiologist, amateur photographer, running/cycling/swimming enthusiast, father, and “all around great guy” (according to confidential sources with knowledge of the internal workings of his head). You can find him working as an epidemiologist a local health department in Virginia, grabbing tacos at your local taquería, or on the campus of the best school of public health in the world where he is an associate in the Epidemiology department.