Lockdown slashes the coronavirus R rate in half within a month, study shows

Ordering people to stay at home is a futile move when it comes to stopping the spread of coronavirus, according to a major review published today in the Lancet. 

It found that, on its own, this measure sees the R rate drop from 1 to just 0.97 after 28 days.   

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

Researchers from the University of Edinburgh studied various government intervention measures on the R rate in 131 different countries.

They found combining measures similar to a national lockdown, including banning public events and closing schools, could cut the R rate by as much as 52 per cent.  

However, opening schools again can lead to a 24 per cent increase in the R rate within a month of the measure being lifted, the researchers discovered.  

The UK today announced another 21,242 positive coronavirus tests and the deaths of another 189 people due to the virus.

The chief scientific adviser, Sir Patrick Vallance, said that numbers are ‘still heading in the wrong direction’ but also admitted Britain’s outbreak appears to be slowing.

The Scottish researchers examined a variety of measures and how they individually, or in combination with other options, can reduce or raise the rate of infection.  

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.    

Meanwhile, the measures most strongly associated with an increase in R were lifting bans on gatherings of more than 10 people  – seeing a 25 per cent spike in the rate. 

The findings, published in The Lancet Infectious Diseases journal, are based on a modelling analysis, taking into account of measures across 131 countries.

Study author Harish Nair said a combination of measures was the best approach when looking to reduce the rate of transmission for Covid-19. 

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

He said the findings can be used to inform decisions on whether to introduce or lift various restrictions and when to expect to see them take effect. 

Individual measures considered included: School closures, workplace closures, public event bans, limit of 10 people mixing, public transport closure, stay at home orders, limits on internal movement and and international travel restrictions.

A ban on public events was associated with the greatest reduction in R at 24 per cent after 28 days, which could be due to the fact they are likely causes of super spreader events and often the first restriction imposed by a country. 

Previous studies have found that measures, including school closure, social distancing, and lockdown, could reduce R substantially to near or below 1, but this is the first study to look at the effects on R following the relaxation of these measures.  

The analysis included 790 phases from 131 countries and used a model to measure the association between which measures were in place and changes in the R. 

The authors used this to estimate the effect up to 28 days on the R of introducing or lifting measures. In addition, they modelled four combinations of measures that could be introduced to tackle the resurgence of SARS-CoV-2.  

The combinations included mixtures of each of the individual measures, from a ban on events and limiting gatherings, to what is effectively a full lockdown. 

The team found that the least comprehensive package of measures would still reduce R by 29 per cent within 28 days of the measures being imposed.

That is still four per cent more than the most effective individual measure – banning public events such as sport matches and concerts. 

In contrast, the most comprehensive package – similar to a lockdown including school closures and limits on movement – would lead to a 52 per cent reduction.  

The effect of introducing measures was not immediate; it took an average of 8 days after introducing a measure to see 60 per cent of its effect on reducing the R.

Researchers didn’t, or couldn’t consider the impact of other measures linked to certain restrictions – such as hand washing, masks or people following the rules. 

For example, although reopening schools was associated with a large increase in R, the researchers said they were unable to account for the impact of class size limits, deep cleaning, social distancing or temperature checks on arrival. 

Professor Nair said: ‘We found an increase in R after reopening schools but it is not clear whether the increase is attributable to specific age groups.’

This is because there could be substantial differences in adherence in social distancing measures from one class to another – but they didn’t have the data.  

‘Furthermore, more data are needed to understand the specific role of schools in increased Sars-CoV-2 transmission through robust contact tracing,’ he said.

The study authors also did a secondary analysis using Google mobility data, modelling the total visits to workplaces and the total 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 – around one to three weeks.

The authors suggest the delay was possibly due to the population taking time to modify their behaviour to adhere to measures.

The researchers also said that some of the greatest effects on R were seen for measures that were more easily implementable by law, like school reopening and introduction of a public events ban.

They suggest this may have been because their effects were more immediate and compliance was easier to ensure. 

However, likely low compliance when it comes to bans on gatherings of 10 or more people could explain why that measure saw a minimal impact on the R rate.

Writing in a linked Comment, Professor Chris T Bauch from University of Waterloo, Canada, said despite the imperfections with R, the findings show measures including lockdown do work to reduce the rate. 

‘This information is crucial, given that some [measures] have massive socioeconomic effects. In a similar vein, transmission models that project COVID-19 cases and deaths under different scenarios could be highly valuable for optimising a country’s portfolio of [measures], the researcher, not involved in this study explained.

‘The success of large-scale [measures] requires population adherence. R can stimulate populations to act and gives them useful feedback on the fruits of their labour. Perhaps this is one reason that R has entered our vernacular in 2020.’