Last updated on April 24, 2020
I’ve seen a ton of posts online comparing Covid-19 to H1N1 and claiming that the media has over-hyped this pandemic. Certainly, the media has been known to blow things out of proportion. Are quarantining and social distancing “over-reactions”? While it may be tempting to compare the two pandemics on surface level statistics like death counts or case numbers, it is important to realize the ways in which they are not comparable. One of the most obvious differences is in their transmissibility. Last week, I threw out some numbers comparing the transmissibility of Covid-19 to that of the flu; every person with Covid-19 spreads it to 2.5 people on average while every person with the flu spreads it to 1.3 people on average. But what does that actually mean in terms of the spread of the virus? Well, in epidemiology terms, these numbers are referred to as each virus’ reproduction number (R0).
On the most basic level, R0 is calculated by counting the number of infected individuals in a defined population, waiting an appropriate amount of time, and then counting infected individuals again. R0 is then equal to the number of new cases divided by the number of old cases. So, if 10 people are infected at the first count and 100 people are newly infected at the second count, R0=10.
The “appropriate amount of time” between counts depends on the length of time that an infected individual remains infectious. The exact length of the infectious period for Covid-19 is not yet known but the incubation period (the time between infection and becoming symptomatic) ranges between 2 and 14 days and infected individuals appear to be most infectious during the early stages of the infection. H1N1 has an incubation period of 1-4 days and an infectious period of 5-7 days. The comparably drawn out nature of Covid-19 makes it much harder to track and respond to.
But what does R0 actually mean in terms of how these viruses spread? Let’s look at a couple different scenarios. In the case of H1N1, the R0 value is slightly elevated from the seasonal flu to around 1.5. Of course, this is an average, as you can’t really have half of a person; so, in our scenario, we will assume that an infected person has equal chances of spreading the virus to either 1 other person or 2 other people. We will start with 1 infected person. That infected person spreads it to 2 other people. Our total is now 3 infected. One of those new infected people infects just 1 other person, while the other infects 2 other people. Our total is now 6. Of the three newly infected, two of them infect only 1 person each and the other infects 2. Our total is now 10. Of the four newly infected, half infect 1 person each and the other half infect 2 each. Our total is now 16. Again, half of our six newly infected infect 1 person and the other half infects 2 people. Our total is now 25. And so on…
In the scenario of a SARS-Cov-2 infection, we would follow the same procedure, but this time each infected person has an equal chance of infecting either 2 or 3 people. Our first person creates 3 newly infected people for a running total of 4. Our three newly infected people create 7 for a running total of 11. The seven create 18 for a running total of 29. In just three periods of infection, SARS-CoV-2 has out paced H1N1.
We could keep going with this, but it is more efficient to simply visualize the number of newly infected (IN) at any given time (t) as an exponential graph, IN=R0t. We would want to graph them on a logarithmic scale (10; 100; 1,000; 10,000; 100,000; etc.), in order to see the difference more clearly. Like this:
In this graph, we see how quickly the gap between SARS-CoV-2 and H1N1 widens. Each infection starts with 1 person, but by 20 infectious periods, H1N1 has only infected nearly 10,000 people while SARS-CoV-2 has infected nearly 100,000,000. H1N1 infected 60.8 million people worldwide over the nearly 16 months that it circulated and lead to over 100,000 deaths. By comparison, Covid-19 has infected over a billion people and led to over 52,000 deaths since January 10th (also the death count was 12,000 last week if you remember…).
There are a lot of factors that can make it difficult to calculate R0. For one thing, it depends on your ability to actually reliably count the number of infected at any given time, a feat that has proven increasingly difficult in the U.S. due to limited testing capability.
R0 isn’t an inherent property of a virus, it changes as our response to the pandemic changes. R0 can be reduced by reducing the number of susceptible individuals via vaccination. Widespread vaccination builds up a population’s herd immunity — the more individuals that are immune, the harder it is for a virus to spread out of control. An H1N1 vaccinewas distributed to the public in the U.S. starting nearly six months after the pandemic began. Because H1N1 was a strain of flu, a virus we manufacture new vaccines for every year, we had a significant leg up on creating a vaccine for it. In the case of Covid-19, there is no previously manufactured SARS vaccine to start with, but researchers are racing to develop one to quell this pandemic.
Another way to reduce R0 is by reducing contact between individuals who could be infected. This is why quarantining and social distancing are being encouraged and/or enforced around the world right now. These practices slow the spread of Covid-19 so fewer cases are happening at once, making it easier for the healthcare system to keep up.
Vox posted a wonderful video on instagram explaining this concept in more detail so I’ll just include that here for you to watch:
See my last post for a detailed list of tips for staying healthy and stopping the spread. Comment below or email me at contact@anyonecanscience.com to let me know what you think about this week’s blog post and tell me what sorts of topics you want me to cover in the future. And subscribe below for weekly science posts sent straight to your email!
