Coronavirus: Reopening schools causes R transmission rate to surge, study suggests

Tranmissions rise by 24 per cent within a month of children returning to classrooms, models using data from 131 countries show

Reopening schools following coronavirus lockdowns is linked to a surge in transmissions within a month, according to the first study to look at the impact of lifting restrictions on the R rate.

Children’s return to classrooms was followed by an average 24-per-cent rise in the R transmission number, University of Edinburgh researchers found after analysing data from 131 countries.

The only other measure linked to a higher increase in the rate is lifting a ban on groups gathering, which led to a 25-per-cent rise in R. To create their models, the authors linked data on country-level R estimates from the London School of Hygiene & Tropical Medicine with information about non-pharmaceutical interventions from the Oxford Covid-19 government response tracker.

R represents the average number of people each person with Covid-19 goes on to infect. When the figure is above one, an outbreak can grow exponentially.

Reopening schools was associated with a 24-per-cent increase in R after 28 days, although the researchers cautioned they were unable to account for different precautions some countries implemented for reopening schools, such as limiting class sizes, social distancing, cleaning, personal hygiene, face masks, and temperature checks.

“We found an increase in R after reopening schools but is not clear whether the increase is attributable to specific age groups, where there may be substantial differences in adherence to social distancing measures within and outside classrooms,” said Harish Nair, professor of paediatric infectious diseases at the University of Edinburgh. “Furthermore, more data are needed to understand the specific role of schools in increased SARS-CoV-2 transmission through robust contact tracing."

The study, published in The Lancet Infectious Diseases journal, also created models of the impact combinations of measures had on the R rate when introduced.

They found a comprehensive package of restrictions including public events bans, school closures, a ban on gatherings of 10 or more people, widespread home-working, and stay-at-home orders was linked to the biggest fall in R rate. Transmissions fell by 52 per cent within four weeks when those measures were all introduced.  

The least comprehensive package of measures - a ban on public events and gatherings of more than 10 people – would reduce R by 29 per cent by day 28, the study concluded.

Looking at the measures individually, a ban on public events was associated with the greatest reduction in R, amounting to a 24 per cent reduction after 28 days.

Prof Nair said: "We found that combining different measures showed the greatest effect on reducing the transmission of Covid-19.

"As we experience a resurgence of the virus, policymakers will need to consider combinations of measures to reduce the R number.

"Our study can inform decisions on which measures to introduce or lift, and when to expect to see their effects, but this will also depend on the local context - the R number at any given time, the local healthcare capacity, and the social and economic impact of measures."

The researchers also analysed Google mobility data, modelling visits to workplaces and time spent in residential areas.

Results indicated that people took some time to adapt their behaviour to comply with workplace closures and stay-at-home requirements, which was similar to the delay between the measures and the effects seen on R of between one and three weeks.

Researchers suggested the delay was possibly due to the population taking time to modify their behaviour to adhere to measures.

They said some of the greatest effects on R were seen for measures that were more easily enforceable by law, like schools reopening and public events bans.

This may have been because their effects were more immediate and compliance was easier to ensure, the researchers added.

Chris Bauch, a professor of applied mathematics at Canada’s University of Waterloo, said the study “could be highly valuable for optimising a country's” coronavirus rules as its findings “tell us that non-pharmaceutical interventions (NPIs) work and which ones work best".

“This information is crucial, given that some NPIs have massive socioeconomic effects,” added Prof Bauch, who was not involved in the research.