23 April 2020

Here's how to calculate your personal risks from COVID-19 infection

| Genevieve Jacobs
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Calculating your personal risks from COVID-19 infection is often guesswork. Image: Wikimedia.

Do you have any idea how likely you are to be infected with COVID-19? If you did come down with the virus, what are your chances of getting a hospital bed? How likely are you to die from the virus?

For most of us, it’s completely uninformed guesswork. But if you base your behaviour on an inaccurate risk assessment, you potentially put yourself and everyone around you in danger.

A data analyst who spent much of his childhood on the Tablelands near Crookwell has devised a personal risk calculator for COVID-19.

Charles Ashton runs data analytics company Magniform, whose usual business lies in providing professional services firms with complex business data analysis.

He now lives with his family in Waverley in Sydney – one of Australia’s COVID-19 hotspots – and says he’s been shocked to see how many people in his local area are still congregating in groups in the local parks and going out for coffees.

“We’re in the business of telling stories with data. Our concern about COVID-19 was that numbers like how many cases there are don’t encourage people to personally relate to the situation,” he says.

“You can’t motivate people to continue social distancing over the medium term unless you can convince them that it’s in their personal interest.”

Mr Ashton’s dashboard aims to give anyone a simple understanding of their personal risk with COVID-19 by age group for Australia, the United Kingdom, United States and Canada.

It’s based on country census data, Imperial College’s influential COVID-19 paper from 16 March, and published hospital bed numbers.

“We tried to talk about the different kinds of risks: that you need hospitalisation, that you need ICU, that you might die, the risks associated with hospital resources and how available those beds would be for you,” Mr Ashton says.

He thinks many people don’t have a firm grasp of what the numbers mean and, in particular, the notion of flattening the curve.

“There’s a lot of discussion about how long these social distancing measures will last, and if the curve flattens whether we can lift the measures,” he says. “That makes the idea of flattening the curve appealingly simple.

“The clinical assumption is that you get to herd immunity at 60 per cent. The question we’re asking is if you need 60 per cent of people to get the virus within the constraints of the health system, how long would that take to work through the population?”

He thinks the data suggests that establishing herd immunity would take years while managing the rate of infection within the constraints of the healthcare system. That means that the only realistic answers are either eradicating the disease or developing a vaccine.

That also foreshadows continuing difficult decisions around who is allocated the healthcare resources. In the model Magnifirm developed, 89 per cent of intensive care beds are required for the over 60s, assuming a three per cent infection rate in the general population.

That relatively low infection rate would exhaust hospital bed availability. OECD hospital bed occupancy normally averages 75 per cent. Therefore, the model assumes as a starting point that 50 per cent of hospital beds are not available because they’re occupied by all the other people who need hospital treatment.

“If this goes on for a long time, how would you decide who gets a bed?” he asks. A good enough reason to continue staying at home and taking care of yourself.

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