BRITAIN’S crucial coronavirus R rate has fallen to 1, experts claim – raising questions over a second national lockdown.
New data from the Covid Symptom Tracker app, run by King’s College London, has revealed a slight drop in Covid-19 infections.
It also suggests the R value – which is the number of people an infected person will spread the disease to – has dropped to 1 for the whole country.
The data goes against the Government’s official R rate for the UK which was estimated to be between 1.1 and 1.3 on Friday.
Meanwhile, today Boris Johnson told the Cabinet there is “light ahead” in the battle against Covid.
The Prime Minister said the R rate is “only just above one” and the lockdown would bring it back below that crucial threshold.
Professor Tim Spector, who leads the app study, said on Twitter today: “More good news as the Zoe CSS app survey continues to show a plateauing and slight fall in new cases in England, Wales and Scotland with an R of 1.0.”
If the R number is above 1, the epidemic is growing – and the higher the figure the faster it grows.
But below 1 means it is shrinking and the crisis is stable.
‘TIER SYSTEM WORKING’
This latest data from the Covid app would suggest the current three tier system is working to drive down infections in localised hotspots.
The app has often been ahead of official Government data by predicting localised hotspots with its weekly watch list.
Experts behind the study also raised concerns earlier in the pandemic that a loss of smell and taste may be a sign of Covid after a spike in users reporting the symptom.
The data is based on around a million weekly users self-reporting symptoms and swab test results.
The app is one of several major studies tracking Covid infections.
When considered alongside the ONS weekly infection survey, Imperial College London’s REACT study and Sage findings it can help paint a picture of the virus’s spread.
On Saturday, Boris Johnson the country will go back under strict measures for four weeks after local restrictions failed to sufficiently reduce infections.
However, data from the app shows that cases in the North of England and the Midlands stopped increasing four days age.
There is still some small rises in the South of England, but from a lower base.
Meanwhile, cases in Liverpool have fallen dramatically since the city was placed into Tier 3 last month.
Professor Carl Heneghan, director of the Centre for Evidence-Based Medicine at the University of Oxford, said the R value in Liverpool is “well below one at this moment in time”.
He told BBC Radio 4’s Today Programme: “You’ve got … these pockets around the country where trusts, like Liverpool, have got into trouble with over half the patients being Covid patients.
“But the data in Liverpool is showing cases have come down by about half, admissions have now stabilised, so, yes, there is a problem in Liverpool.
“But, actually, the tier restrictions… the people in Liverpool have dropped cases from about 490 a day down to 260 a day – a significant drop.
“The R value is well below one in Liverpool at this moment in time.”
It comes as the chief scientific adviser and chief medical officer of England are due to be grilled by MPs today.
Experts raised concerns over the data they presented at the Prime Minister’s press conference on Saturday evening.
A graph shown by Sir Patrick Vallance suggested that without another national lockdown there could be 4,000 daily deaths by December 20.
This worst case scenario forecast means there would be more than four times as many fatalities as on the worst day of the first peak in the spring.
But scientists have pointed out that this modelling was complied on October 9 – five days before new tier restrictions came into place.
Researchers at Oxford University also highlighted that if it had been correct, then deaths would be at around 1,000-a-day now.
The current rolling seven day average death toll is around 265, while yesterday’s grim figure was 136.
It’s since emerged that the modelling was based on an estimated R rate of 1.3 to 1.5, which was published on October 16.
The crucial value dropped the following week on October 23 and was estimated to be between 1.2 and 1.4.
It then fell slightly again and in new figures published on Friday – the day before the press conference – it was estimated to be between 1.1 and 1.3.
The data used by Government scientists to determine the estimated weekly figure has a time delay of up to three weeks, experts have warned.
Professor James Naismith, director of the Rosalind Franklin Institute, said that because of the time lag between infections and deaths, waiting for a model to prove accurate risked further deaths if no action was taken.
He said: “What even the most optimistic models agree on is that we will see around 500 deaths per day in two to three weeks (best case). We know these deaths will happen because of the number of people infected last week.
“What matters now is how many people are going to be infected each day this week and next week. This will determine how many people die in four weeks time.”
He said predicting the future “always has uncertainty” but that we cannot afford to simply wait and see if existing measures start working.
Prof Naismith added: “If the virus continues at the rate we saw last week, then taking the two or three weeks to prove beyond any doubt that the current measures have failed, then we will be unable to avoid over 1,000 deaths a day (in a best case scenario) before Christmas.
Professor Neil Ferguson, of Imperial College London, said that the 4,000 deaths a day scenarios were “preliminary work” to create a new “reasonable worst case planning scenario”.
He added: “SPI-M [Scientific Pandemic Influenza Group on Modelling] undertakes a wide range of modelling for government.
“The ‘up to 4,000 deaths a day’ scenarios represent preliminary work to generate a new reasonable worst case planning scenario to assist NHS and other Government planning.”
He added: “Even allowing for the effects of the current tier system, the most recent SPI-M projections suggest that without further action, the second wave is set to exceed the first wave in hospital demand and deaths.”