A study looking at the link between Covid-19 case resurgence and population-level intervention measures found that school reopenings and lifting bans on gatherings of more than 10 people were the most significant causes of a growing outbreak.
The study published in The Lancet Infectious Diseases journal used data from 131 countries and looked at how individual measures as well as different combinations of measures effected Covid-19 transmission up to 28 days after being introduced or lifted.
They used daily country-level estimates of the R number (reproduction number) and linked it with data on what measures those countries had in place from January 1, 2020 to July 20, 2020 to analyse changes in transmission.
For the uninitiated, an R value above 1 indicates a growing outbreak, whereas an R value below 1 indicates a shrinking outbreak.
What they found was reopening schools was associated with a 24% increase in virus transmission after 28 days and lifting of bans on public gatherings of more than ten people was found to increase Covid-19 spread by 25%.
Although, the effect of lifting measures was not immediate; it took an average of 17 days to see 60% of its effect on increasing the R number.
The authors also warn that they weren’t able to account for different precautions some countries implemented for reopening schools, such as limiting class sizes or temperature checks.
On the flip side, the study also found that individual measures like school closure, workplace closure, public events ban, requirements to stay at home, etc. were linked to a reduction in Covid-19 transmission but a combo of measures was more effective than individual initiatives.
For example, the only individual measure that had a significant reduction on coronavirus spread was banning public events, which reduced spread by 24% after 28 days. But when the event ban was combined with lockdown type restrictions including school and workplace closures, gathering and internal movement limits plus stay home requirements, transmission was reduced by 52%.
Similarly to lifting restrictions, the researchers noted that the effect of introducing measures took about eight days to see 60% of its effect on reducing the R number.
“We found that combining different measures showed the greatest effect on reducing the transmission of Covid-19,” said Harish Nair, study author and professor at the University of Edinburgh.
“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.”
Further to Nair’s point, while the study does provide a good picture of the effects of population-level interventions, the study doesn’t account for other potentially influential factors. One thing the study didn’t account for are the behavioral changes like mask wearing or increased hand washing, which have both been shown to have marked effects on slowing the transmission of Covid-19.
The study also failed to include data from large countries such as the U.S., Canada, Brazil, China, India and Russia because of how much different regions within the country varied in how they implemented Covid-19 interventions.
Finally, it’s worth noting that the R number is only part of the picture when it comes to understanding how best to control the pandemic. For example, R can’t tell us how fast the virus will spread and it can miss regional clusters of infection.
But despite this there’s still valuable information in the study, says Chris T Bauch, an applied mathematics professor from University of Waterloo, in a comment
“Despite R’s imperfections, the findings […] tell us that NPIs [non-pharmaceutical interventions] work and which ones work best,” he said.
“This information is crucial, given that some NPIs have massive socioeconomic effects. In a similar vein, transmission models that project Covid-19 cases and deaths under different NPI scenarios could be highly valuable for optimising a country’s portfolio of NPIs. Moreover, I think R provides a social utility that epidemiologists can easily overlook. The success of large-scale NPIs requires population adherence. R can stimulate populations to act and gives them useful feedback on the fruits of their labour.”