Bieber Fever, More Potent Than Measles? | By Mike Ross

Newspapers had a hay-day last year following the publication of a paper out of the University of Ottawa that discussed Bieber Fever. Some articles included:

Of all of these, I’m most disappointed in the CBC – I often pay attention to the CBC, and it’s discouraging to know that they might be equally as wrong about other things as they are about this.

What’s going on here? A professor from the University of Ottawa published a paper where a new model was developed to look at Bieber Fever, and the paper does indeed include the quote “It follows that Bieber Fever is extremely infectious, even more than measles, which is currently one of the most infectious diseases. Bieber Fever may therefore be the most infectious disease of our time.” Oh my god, those newspapers must be right! Science has confirmed our worst fears! This must be backed by hard facts and empirical evidence!

Well, no. The paper appears to be a chapter in a book that examines diseases through various mathematical models. Each of the papers in the book (four of which are written by the Bieber Fever author) takes on a different disease and models it, then examines mathematically the effects of different approaches to the disease, like pulse vaccination, or changes in infection or relapse rate. I can’t comment on the quality of the other chapters in the book, but they seem to be well developed, and certainly based on real diseases.

The Bieber Fever paper is a little bit different though. First of all, it is clearly written in a tongue-in-cheek manner that I think flew over the heads of most major newspapers. The humor and sarcasm actually make it quite an entertaining read, and if the piece was written as a humorous look into a creative way of adapting a disease model (which is my suspicion), then it could certainly be a fun case study for biology or math students. It is definitely not something worth raising alarms over in newspapers, though, as the model’s predictions aren’t validated against any actual statistics and its math is misleading, allowing them to draw this ridiculous comparison to measles that grabbed newspaper attention.

Mathematical disease modelling is a pretty cool field. The most basic model that can be developed is an SIR model – a population is divided up into three groups (Susceptible, Infected, and Removed), and people move through the groups depending on disease parameters and the size of the groups at a given time. For instance, if a lot of people are Infected, the chance of a healthy Susceptible person getting infected is quite high (perhaps due to lots of people sneezing on them), but as more people are Removed (happily by recovery and immunity, or sadly by death), it may become harder for the disease to propagate.

A Simple SIR model

In this model, βIS represents the rate that healthy people become sick – effectively, it is the chance that in a given time a Susceptible person will encounter an Infected person, multiplied by the chance that that encounter will transmit the disease. On the other end, γI represents the rate at which sick people become healthy, effectively the number of Infected people divided by how long it takes them to get healthy (or die, I suppose).

As long as the rate of people becoming sick (βIS) is larger than the rate people are recovering (γI), then the disease will reach an epidemic of some type – otherwise it will quickly die out. For simple models, the ratio of these rates is known as the Basic Reproduction Number (R0) of a disease, and correlates to the number of new diseases a sick person will cause. This is pretty easy to visualize – if the ratio R0 is bigger than 1, then by the time someone recovers from their illness they’ll have spread it to at least one more person, and the disease will grow. If you’re unlikely to make someone else sick when you fall ill, the disease’s R0 will be less than 1, and the disease will go away without much of an outbreak.

For reference, the flu typically has an R0 of 2-3, HIV is around 2-5, Smallpox is 5-7, and Measles is 12-18. For every person who got Measles, the disease was so infectious and you had it for long enough that you were expected to transmit it to between twelve and eighteen people before you either recover or die.

Frightening stuff. Fortunately, analyses of diseases with these mathematical models shows that as long as a certain proportion of a population is immunized by vaccine, epidemics can be avoided. That proportion needed is (1-1/R0) – so a typical flu needs 60% immunization to prevent outbreak, and measles needs over 90%. If you’re still unsure about getting a flu shot, just remember that if a population doesn’t hit ~60% immunity, it is very much worse off for those who don’t have the vaccine or who are otherwise susceptible.

The Bieber paper develops a more complicated mathematical disease model. It looks something like this:

Image from Smith?

The author, Robert Smith?[sic], proposed a model where media effects have a large impact on the disease. Positive media (P in the picture) can increase the rate at which healthy people become Bieber-infected, and can also make recovered individuals susceptible to re-infection, and Negative media can heal the sick or immunize the susceptible (how miraculous!).

Using the numbers that Smith?[sic] has in his paper, the spread of Bieber Fever in a typical school of 1,500 students would look something like this:


After about 2 months, the system reaches an equilibrium with about 85% of people being Bieber Fanatics. The paper makes a couple of assumptions: first of all, people are assumed to “grow out” of Bieber Fever after a period of two years. People are also expected to interact with everyone else in the population at least once a month, and have a transmission rate of 1/1500. This means that the average infected person will infect 1 person a month for 24 months, giving Bieber Fever an R0 of 24.


Not even a little bit! The transmission rate is absolutely just assumed out of nowhere – no stats, evidence, or explanation given. Similarly, the length of the disease is made up. Essentially, the authors were given a calculation where they had to assume three numbers and multiply them together, and newspapers are surprised that the answer to the multiplication was high. Even the mechanics of the positive and negative media effects are questionable, though the model they developed could help provide insight into other diseases with relapse mechanisms.

The paper is cute, clever, and provides a mathematical analysis of a convoluted set of differential equations, for all of these things it serves a nice purpose as a tongue-in-cheek entry into a textbook examining mathematical modelling of infectious diseases. But newspapers taking essentially the result of an unfounded set of assumptions out of proportion and reporting them as “Science Confirms!” will always annoy me to no end.

One last thing. This is what a graph would   look like if the same school was hit with measles:




Now that’s an epidemic – three people sick can infect up to 1,200 in less than a week. Remember this when deciding whether or not to immunize your baby.


Image CC eviltomthai on Flickr

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