The start of the outbreak of covid’s highly contagious delta variant in Sydney was greeted with the usual hubris by NSW policy makers. The magical gold standard contact tracers would fix it without any of those dreadful southern lockdowns. Once again, NSW would show the world that covid could be vanquished without cost.
The first six days of the outbreak were, indeed, not scary at all. Daily cases were low. Why worry? They went 2, 2, 2, 2, 5, 10. Small numbers. Easily handled by contact tracing. Nothing to fret about. Except, to anyone who knows what exponential growth is, the 2, 5, 10 progression is deeply worrying. That’s case numbers doubling every day. And though 10 is still a small number, doubling every day takes 10 to 20, 40, 80, 160 within just five days. (For a quick video explainer on exponential growth, check out this one that I made last year.)
The next day was 18, awfully close to 20. But then we had 11, and everyone breathed a sigh of relief. We must always remember that we know how many people have tested positive for covid. We don’t know for sure how many people have it. That’s why we need everyone to get tested as much as possible, to keep the “tested positive” as close to “people who have it” as possible. But disease spread is not as consistent or predictable as an exponential series in a textbook, so the one low number was not particularly useful. What matters is the trend. The next day, June 25th, 29 new cases were reported. Still not scary numbers, especially when we’ve been watching the rest of the world in the hundreds of thousands, and even Melbourne got up to nearly 700 per day. 29 was nothing to fret about.
But understanding that growth was making epidemiologists and maths nerds very nervous, especially because the delta variant is three times as infectious as the variant Melbourne was dealing with last year. Delta has an R0 of 5, meaning for every person infected, on average 5 more people will catch the disease. That’s exponential growth. 1 becomes 5, which becomes 25, then 125, etc. When Gladys Berejiklian expressed her relief one day that “only” 9 of the new cases detected the previous day were out in the community while infectious, those 9 cases were likely to translate to 45 new ones. That was definitely not good news.
On July 2nd, increasingly alarmed by what looked to me to be awfully like Melbourne’s second wave being played out up north, I graphed the Sydney outbreak against Melbourne’s numbers, and saw this.
I kept producing those graphs, as numbers grew, and every time I tweeted them someone would say “but we have contact tracing” or “but we’re in lockdown earlier.” And that scared me, too, because Sydney’s “lockdown” was not lockdown as we knew it in Melbourne. Non-essential retail was not closed. Neither was childcare. And numbers were still rising.
I tweeted this graph on July 10th, with more Melbourne numbers, and noted that within days of numbers around 50, the numbers in Melbourne were over 100.
Later that day, 77 new cases were reported. The next day was 112. But still people were saying “it’s ok, we’re in lockdown.” So I made this graph, showing the types of restrictions introduced in Melbourne, against the case numbers.
I had to scrabble around a bit to find the timeline of exactly what restrictions happened when, but the actual data work was very, very simple. Yet I had not seen a graph of this type anywhere (and believe me, I was doom scrolling all of the data out there). It shocked people, because it shows really clearly that it wasn’t until we were in the very strictest of lockdowns that the numbers started to come down. Lockdown light didn’t work. Locking down individual postcodes didn’t work. In Melbourne, we are watching Sydney try all of the “can we avoid really seriously locking down” strategies that we know failed us, with a three times more contagious variant of the virus. We are like a cinema audience shouting at the screen, with as much impact on the outcome of the story.
Would our collective understanding of covid have been different if we were all more data literate? If we were used to working with real data that bounces around rather than reliably following the textbook curve? I think it would. I think if we all recognised exponential growth when we saw it, even when the numbers were small, maybe we’d be more able to resist the pressure to just “live with the virus”.
We can build data literacy by changing the way we teach (and I lay out a detailed plan for that in Raising Heretics: Teaching Kids to Change the World), but we can also do it by changing the way we communicate. Scientists, governments, people who understand data all have the power to show us the data in different ways. And no one way is going to communicate to everyone. So we all need to share our skills, tell the data stories, and show people why they matter.
The truth is, we are increasingly being faced with the kinds of scenarios where understanding data matters. From pandemics to climate change. From income inequality to fake news. If we understood more about how data works, and what it means, maybe we’d be more supportive of the scientists warning us of the brick walls we’re speeding towards.
For more reasons why we need to be data literate, and how to teach our kids to change the world, check out Raising Heretics, pre-order now, or buy online in all the usual places from August 1st.