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Can maths plot a path for policy-makers through the UK coronavirus lockdown?

Initial findings from a new study suggests that working from home could enable the UK to manage the coronavirus infection rate

In her keynote presentation at the virtual Retail Expo conference, UCL associate professor and TV presenter Hannah Fry discussed the power of data and how mathematical models can plot paths through the UK’s coronavirus lockdown.

As the daily Covid-19 updates show, the reproduction number, known as “R”, is one of the key criteria the government will assess before it starts to ease the lockdown. The figure for R is directly correlated to the number of people someone infected with coronavirus comes into contact with.

“It is crucial that R is less than one,” said Fry.

Discussing some preliminary findings from new research, she said: “Based on our data, going back to normal won’t reduce R enough. Even if we tested 450,000 people a day, it would not be enough.”

Fry said manually tracing all acquaintances with whom an infected person had been in contact would help to bring R back almost to where it needs to be, but this would not quite be where the R value falls below one, which is needed for the infection rate to fall.

Based on the assumption that everyone goes back to work, Fry said R would fall below one if manual tracing is combined with people limiting their social interactions to four people a day. 

“If we find ourselves in a situation where half the country can work from home, where half the country has zero work contacts, then the number of people we can interact with actually doesn’t have an effect,” she said.

Discussing the implications of this, she added: “One of the biggest things for me is to keep the number of people working from home as much as possible, so that we can go back to seeing our friends and families again.”

Read more about data models

  • Policy-makers are calling on the global data science community to develop data models that can can help them better understand the Covid-19 transmission rate.
  • has been working as part of a collaborative private sector effort to better analyse epidemic information. This could help build resilience against Covid-19, and similar viruses.

In 2017, Fry took part in a citizen science experiment in conjunction with the BBC, looking at how a flu-like pandemic could spread across a community. The findings have fed into the policy-making decisions the government has taken to put the UK into lockdown and try to ensure people respect social distancing measures.

“When you have good data, good models, you can make a genuine difference,” she said. “We’ve known that a pandemic is inevitable. It was really a case of when, not if, and we knew we had to be as prepared as possible.”

In 2017, Fry said that the group of researchers from the London School of Hygiene and Tropical Medicine realised they needed to improve the data they had in order to create a mathematical model to illustrate the effects of  a flu-like pandemic.

“Along with the BBC, we did a study with real people, who gave us permission to track them for 24 hours,” she said. “The data we collected was where people and, crucially, how often, they come into contact with other people.”

The researchers were able to collect 100,000 days of social interaction, said Fry, which is the fundamental data behind the government’s models. “We started our study in Haslemere in Surrey with me as patient zero. What we could do with all the data is simulate different scenarios.”

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