Regional differences in Covid-19 transmission rate emerge in England
Transmission rates of Covid-19 remain dangerously close to levels that could bring a second wave, even before the easing of some restrictions this week, as the focus shifts to transmission rates in the north of England.
The latest official estimate places the national R value – the rate at which people are passing on infections to others – at between 0.7 and 1. An R value above 1 means the epidemic will start to grow exponentially again, which would result in a new surge of cases.
It raises questions about the extent to which restrictions can be safely eased in coming weeks. In his address on Sunday, Boris Johnson said: “We have the R below 1, between 0.5 and 0.9 – but potentially only just below 1.”
The slight increase and narrowing in range is not linked to lockdown relaxations this week as the estimate is based largely on hospital admissions and deaths data, which reflect the levels of transmission two to three weeks ago.
A likely explanation is that while overall levels of infection have fallen, hospital and care home settings are making a relatively bigger contribution to the overall estimate of R. Lockdown restrictions have less impact in these settings and there are still concerns about the levels of testing available in care homes.
The figure suggests that, while the lockdown has been effective at bringing down case numbers, R is still close to 1, meaning that it would not be possible to ease restrictions significantly without new measures to contain outbreaks, such as extensive testing and tracing.
It comes as data showed that while London was a hotspot of infection at the start of the epidemic, attention is now shifting to the north of England, where transmission rates of Covid-19 appear to be falling far less steeply.
Some epidemiological models suggest transmission rates in the capital are now half those seen in the northeast and Yorkshire, raising the question of whether all areas will be safe to emerge from lockdown at the same time.
There is growing unease among civic leaders in the north of England at plans to ease lockdown rules, with one council leader branding it as “frankly, madness”.
The leader of Gateshead council, Martin Gannon, said on Friday that the advice to the public to “stay at home” would continue. Gannon, whose views on lockdown were echoed by his counterparts in Newcastle and Sunderland, said there was evidence that the R rate was greater than 1 in his borough.
How do models calculate the R value?
The R value tells you how many infections each infected person passes the virus on to, on average. For an R above 1, the epidemic increases exponentially; below 1 it will eventually fizzle out. In the absence of direct measures of this elusive number, modellers have to rely on what firm data we have and extrapolate from there.
Some models, such as one by Public Health England (PHE) and Cambridge University, rely on the numbers of reported deaths, and the steep, steady decline in deaths in London translates into an estimate that community transmission must have also rapidly dropped off.
Deaths are reliable figures, less subject to biases than some other measures, but they reflect the infection rates that were occurring about three weeks previously and so do not give a dynamic reflection of where things are.
Policy changes around working and socialising introduced in the past week may have influenced R, but a model based on deaths will not give any insights into this. The PHE model projected that there might be as few as 24 new cases in the capital per day on 10 May. This reflects the strong downward trend London has been on, but the model is not designed to give razorsharp predictions of day-by-day infection statistics. On Thursday, 49 people with new infections were admitted to hospitals in London.
Some models rely on measures
such as symptoms reported to phone apps, which have a shorter lag time. However, these data are more “noisy” and so give a less robust measure of R overall, although they can give better insight into short-range changes.
How sure are we about regional differences?
Almost all available data and models suggest there are strong regional differences in terms of the number of infections and current transmission rates. Serology surveys suggest that more than 10% of people in London have been infected with Covid-19, compared with around 4% in the rest of the country. In general, urban centres have had more infections than rural areas.
The data from hospital admissions and deaths also suggest that R has come down far more steeply in London than in other regions. The PHE and Cambridge model suggests that R in London is 0.4, compared with 0.8 in the north-east and Yorkshire and 0.75 for the country as a whole.
This is one of a handful of models feeding into advice from the Scientific Advisory Group for Emergencies (Sage) to government, and all are showing broadly similar patterns across the regions. However, the extent of the differences varies, meaning that an optimistic estimate for London cannot be relied on too heavily.
Matt Keeling, professor of populations and disease at the University of Warwick, described the PHE/Cambridge estimate for London as “extremely low”. His own team’s predictions put the London R value closer to 0.6.
“This still predicts a faster decline in London than the rest of the country, but not as dramatic … The difference between 0.4 and 0.6 might sound like scientists arguing over small details, but these translate to very substantial differences in the rate of decline of cases, or the time for the epidemic to halve,” he said.
What are the reasons for regional differences?
Initially, London was a few weeks ahead of other regions of the country because far more infections were seeded there at the start of the epidemic. However, it has also seen a steeper decline and, in fact, may have always had a slightly lower community transmission rate than some other geographical regions.
There are likely to be a wide range of factors behind this. London may now have a higher level of population immunity, which could be playing a role in bringing down transmission, since a higher proportion of people have been infected.
“If it turns out that there’s loads of immunity in the community, then that’s brilliant, but I don’t think we should plan for that scenario,” said Thomas House, reader in mathematical statistics at the University of Manchester. “We don’t really know yet.”
There are also likely to be substantially different contributions from hospitals and care homes, where transmission rates have not been effectively brought under control. In some regions, the spread of infection in these settings, where lockdown does not have the same impact, could be making a major contribution to keeping R close to 1.
Differences in living arrangements – for instance, larger family groups living together outside London – could play a role. Models may also not fully capture the role of underlying health issues and deprivation in raising the risk of severe cases.
Is there evidence that people in some regions have complied less with the lockdown?
There isn’t strong evidence for this. The lockdown has brought down transmission rates in the community across the country, and a forthcoming study from scientists at Imperial College London does not seem to suggest that London has been an outlier in terms of compliance.
“The uniformity with which the UK responded to the lockdown is remarkable,” said Steven Riley, professor of infectious disease dynamics. “I thought maybe people in London who were close to a large epidemic would be much more compliant than elsewhere. But the behaviour change was very well synchronised across the UK.”
Do the models suggest restrictions should be eased regionally?
This is an emerging view among scientists. “Based on just the epidemiology, it makes a tremendous amount of sense,” said House. “There’s no point locking down if there’s no cases to prevent.” However, he and others acknowledge that the political decision will be weighted by factors beyond simply modelling the trajectory of case numbers.
“It’s much more difficult to message the relaxation than it is to message the lockdown,” said Riley. “I suspect the most difficult aspect on possible regional differences is how you would message it. That uncertainty would be much greater than any mechanistic modelling uncertainty.”