We are in a rapidly changing landscape. Dynamic would be an understatement. I think that the phrase, ‘In some decades we only move weeks, but in some weeks we move decades’, is absolutely appropriate for what we’re seeing with the Covid-19 pandemic.

Let’s look at what have we’ve learned in recent weeks. We’re now at a point where we’re able to see where different countries are in terms of the outcome, in terms of the number of cases and number of deaths.

Countries like New Zealand, that has gone into lockdown, and Australia, that has significant restrictions – we’ve both been able to bend the curve.

We keep talking about ‘flattening the curve’. Indeed Australia and New Zealand have achieved the flattening of the curve. We’ve also seen some other countries like the United States where parts of the country, e.g. New York, have been absolutely overrun. However, compared to worst case scenarios and some of the models, they’ve actually fared relatively better.

 

 

If we head over to Europe, the United Kingdom is likely to have the highest number of deaths from coronavirus in Europe. They have a significant restriction policy effective currently. Other countries, like Sweden, have adopted a policy that has been much more ‘let the society function more normally’ than what we’ve seen in, for example, Australia and New Zealand. Sweden’s population is about half the population of Australia. Sweden has a significant number of coronavirus cases, almost twice the number of cases and it’s got a 10% fatality rate, which is one of the highest in the world.

So what does all of this mean? What can we take and surmise from all of this? The hard thing about hard things is that they’re hard. They’re really difficult to do. So when we look back at decisions made in the past weeks and months, we look currently with a very different lens of information, because the amount of data that we have now is very different from the data that we had a month ago.

I was looking through some of my emails yesterday, and looking at some emails that I sent about a month ago and it feels like a different world. It was a totally different landscape. The amount of fear was different from now. The amount of restrictions were very different. At that point there was such a high amount of uncertainty that it was unclear what the best action was. I think this is particularly relevant now, with the debate currently in New Zealand and Australia, and most countries around the world, which is ‘what’s next?’. Where do we actually take this?

I want to share with you some recent information that do provide different perspectives. There’s a really interesting study in Santa Clara County, which is an area of California. This was a study done by a variety of different doctors and researchers based out of Stanford University, one of the best universities in the world. They looked at a segment of the population over the last 30 days – they advertised on Facebook to recruit people to come and get an antibody test for coronavirus. If you get coronavirus, you develop antibodies some days to weeks after you actually get the virus. They were looking for people that might have been exposed to coronavirus and developed these antibodies, but were not diagnosed with coronavirus at the time.

What did they find? They found that from their sample of a bit over 3000 people, 2.5 to 4%, depending on how you look at the data, of people had got exposure to coronavirus, which is probably about 50 times what the number of people that we thought had it. What that means is the virus has been spreading around in the community much more than what we thought.

 

One way of thinking about it is that the virus has seen humans more than the humans have seen the virus.

 

What does this practically mean? If this is true, it may significantly change the case fatality rate, because it means that the number of cases of coronavirus is actually much larger than what we previously estimated. You may have seen the talk by Professor Grant Schofield where he was talking about the denominator. The numerator, the number at the top of the fraction, is the number of people that have died from the virus. The denominator is the number of people that have got the virus. If the number of people that have got the virus is far larger than we thought, it’s quite possible that the case fatality rate is much lower.

The Stanford paper that I’m referencing suggests that the case fatality rate is probably about 0.15% to 0.2%. This is about 50% to 100% more fatal than the flu, but vastly different in overall fatality rate than what we thought that this could be, which could be 20 times, or even higher, more deadly than the flu.

But how does this explain Italy and New York? How does this explain a really high fatality rate in other countries? It raises some interesting questions.

The area that I’d like to talk about now focuses on that. I want to introduce the notion of truth.

 

When I think about what science is, I think that science is the pursuit of truth.

 

We are generating hypotheses on what we think may explain how the world functions and then we go out and gather data to test that hypothesis. At the end of the experiment, we get enough data to say, ‘yes, our hypothesis was correct’ or ‘no, our hypothesis was incorrect’. In the pursuit of truth, with every day we’re getting more and more information that is allowing certain hypotheses to be shown as true and certain hypotheses to be shown as false.

With the bending of the curves in many jurisdictions, people are asking, ‘Why are we in these severe restrictions? Why are we doing this? The cost of the restriction on both lives and economics is actually far greater than the cost of the virus’. Some may argue that that’s a bit like saying, you jump out of a plane with a parachute and because the parachute has slowed your descent, now we can cut loose the parachute. The real answer is, we don’t know. Models are exactly that. They’re models based on a variety of different assumptions and we need to make those assumptions explicit to try and actually see with increased data over time, which assumptions proved to be true, which assumptions proved to be false. We need to change the assumptions, and therefore the model, going forward.

But what does that mean for where we’re at now? In summary, if we look back about a month ago with the amount of information that we’ve got now, it could be easy to say that certain countries overreacted with the restrictions. However, the amount of uncertainty at that point was high. It’s easy to look back now with more data and make judgment, but we didn’t know what we were dealing with. Acting more conservatively is very likely the best thing because the decision about what to optimise for would be, “I’m going to optimise for human life over other factors”, at that point.

 

What are we seeking to optimise for?

 

The real question now is what are we seeking to optimise for. If you’re a government of a country, what you’re seeking to optimise for is likely very different to if you’re an individual. If you’re an individual whose business is thriving in the pandemic because you sell food or protective equipment, you may have a very different view than if you work in hospitality and your business has been decimated and is non-operational.

What information do we have currently, that we have learned over the past period, that would give us the best ability to make hypotheses, clarify and test assumptions, and which assumptions over the past month have proved to be wrong that we need to change and reconsider as we move forward?

It’s going to be an interesting time for all of us, no doubt. It’s going to be interesting as we see different countries alter and change their strategies as we move forward.