How Hannah Fry's math can guide us through coronavirus’ deadly trail

Hannah Fry, one of United Kingdom’s most celebrated mathematicians, has said that mathematical models backed by data on population behaviour and movement could be vital tools in understanding the progression of the COVID-19 infection.

Recently, speaking at a conference in London, Fry, an associate professor at University College London, said that during a pandemic like COVID-19, a comprehensive population data containing information on the pattern of people’s movement and social interaction would not only help governments map the spread of the disease but also identify hotspots, super spreaders and where to focus containment on.

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Giving an example of schoolchildren, Fry says that shutting schools during a pandemic isn’t enough assurance that the virus wouldn’t affect the children, as they are also exposed to contact across generations and may contract the virus from some other source.

“When you’re deciding whether to close schools, to close train stations, to cordon off different areas, you have to do a cross-benefit analysis. It’s not as simple as saying close this school and you’re done,” a Verdict article quoted her as saying at the conference.

“Young kids have intergenerational mixing that older adults don’t have. They have much more contact across the generations [age groups]. So it’s not as simple as closing schools and everything is fixed. What you need are really effective mathematical models that are capable of predicting how things will spread in option A versus option B,” she adds.

To determine the nature of such interactions, she says it is necessary to have a comprehensive population data based on a bigger sample size.

Findings of Fry’s simulated pandemic

Fry, in March 2018, in collaboration with BBC, shot the documentary ‘Contagion! The BBC Four Pandemic.’ It was an experiment which with the help of a simulated virus, tried to analyse the nature and spread of a pandemic in the city of Haslemere in Surrey.

Volunteers considered “infected” were tracked with the help of an app which in turn recorded everything – from places they visited to number of people they interacted with.

The data helped Fry and her team not only to track the virus but also identify dangerous carriers.

Recounting the experiment in an article in the Independent in 2018, Fry said she considered herself as the patient zero (or the first carrier of a contagion) of the experiment and spent a day wandering around the town, where hundreds of people had downloaded the app.

She spent the day doing normal things – attending yoga class, shopping, having lunch at a café and paying a visit to the local pub.

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The results were surprising – she had ‘infected’ nine people in eight hours, without any forced movement in crowd or prolonged stay anywhere.  These nine people in turn had ‘infected’ 69 more.

By the third day, more than 500 people in the town were infected, she wrote.

Identifying, quarantining ‘super spreaders’

Fry said such exercises, most importantly, will help in identifying super spreaders (professionals who come in contact with a large number of people on a daily basis) in communities and quarantining them in time.

“In Haslemere, for instance, our biggest super-spreader was a lady who worked in a hardware shop, but it could be school teachers, train staff, paramedics, waiting staff, anybody who has close contact with high numbers of people every day,” she wrote.

Once the super spreaders are identified, the limited vaccines can be used to treat them, as their confinement would cut down the speed of the virus transmission.

While speaking at the London conference, Fry said such data, could be now analysed while also taking care that people’s privacy is not compromised with, to make “strategic decision-making process as accurate as possible.”