If it was ever the time for graphs to shine, the 2020 novel coronavirus pandemic is surely it.
While the last 2 years shaped up to be a pretty weird year so far – it’s also been a reminder that maths is everywhere, and understanding maths and data can literally save lives in a public health emergency.
We’ve rounded up three of the biggest maths lessons that we’ve learned from COVID-19.
R0 (pronounced R-nought) is a mathematical term that’s been used a lot since the new coronavirus started taking over our news channels and social media feeds.
It’s also called a basic reproduction rate (the R stands for reproduction), and it’s the average rate at which an infectious disease like COVID-19 spreads between people.
The R0 of COVID-19 has been estimated at 2.2, which means an infected person passes the disease onto an average of 2.2 other people. Of course you can’t have 0.2 of a person – averages is another useful mathematical concept to help understand all this!
When we imagine numbers getting bigger, we have a natural habit of imagining them doing so in a straight line – 1, 2, 3, 4, 5 or 10, 20, 30, 40, 50. That’s called linear growth, and if you put it on a graph it’s a straight line.
But exponentials are different, and they can be hard to get your head around. That’s why people who remembered learning about exponentials in high school maths have probably been better able to understand why only small numbers of confirmed COVID-19 cases on one day could get very big, very fast.
Understanding exponentials has meant we can act when it seems too early, but before it’s too late.
That means hand-washing, social isolating and all the other actions individuals and governments have taken all over the world to slow the spread of the virus.
"The study of exponential and logarithmic functions is important in developing students’ ability to solve practical problems involving rates of change in contexts such as population growth and compound interest"
- from Mathematics Advanced course content
Our scientists and mathematicians can model the spread of a virus using data and computer algorithms. They know that if we don’t do anything to slow it down, cases will rise exponentially – that means our healthcare systems are overwhelmed, which is bad news.
The alternative is everyone acts fast to slow the spread and that rate of change decreases, spreading the cases over a longer timescale, and taking pressure off our hospitals because there are fewer sick people turning up all at once.