How big is the job of vaccination? The aim is herd immunity, to protect enough people so that the virus starts to run out of people to infect and rates fall. This is expected to happen when between 60 to 80 per cent of the population is protected, so quite a job for the NHS. Until this is achieved, ministers seek to use lockdown as a tool to keep the R below 1. This means the cycle of lockdown and release could be with us for some time, especially in light of the new ‘mutant’ strain of the virus. But are ministers seeing the whole picture?
As a professor of risk management, my coronavirus modelling has shown a large gap in the data on coronavirus cases between the government’s dashboard figures and the ONS weekly surveillance data. It’s a gap that could be explained by a basic fact: that a large chunk of the population, perhaps as many as 30 per cent of us, already have a significant degree of immunity to the virus.
Let me explain. I’d like to look at two types of immunity; the first type is acquired by having caught and fought off the virus – and this is about 17 per cent, 6 per cent from the first wave, as measured by the ONS in antibody surveys, and roughly double that amount in the lower but broader second wave, as deduced by modelling. The second is T-cell immunity which studies show can exist in people who have never been exposed to Covid-19.
Last month, Public Health England’s EDSAB-HOME study estimated that 12.9 per cent (with a confidence interval likely between 11.5 and 14.3 per cent) of the English population had T-cell levels and consequent protection – despite never having contracted Covid-19. The conclusion of the study, which is awaiting peer review, was that ‘People with higher levels of T cell recognising SARS-CoV-2 are protected from Covid-19.’ It went on:
‘About 1/4 of the key worker population studied had high levels of T-cells which recognised SARS-CoV-2 in their blood […] However, about half the people with high levels of T cells in their blood have not had COVID-19, as far as we could tell – the cells were probably there because of previous infection with coronaviruses other than SARS-CoV-2.’
The size of the effect seen corroborates a study made earlier by the Karolinska Institute in Sweden, which found in their T-cell survey, carried out at a time when 7 per cent of Stockholm’s population carried antibodies, twice as many Covid-resistant people had T-cell immunity. It is fair to conclude that roughly one in eight of us may well possess prior T-cell immunity.
We have been developing at Bristol a predictor-corrector coronavirus model, known as the PCCF, which uses the daily official coronavirus data to measure the key parameters of the Covid-19 epidemic in England. But as the below graph for active infections shows, our PCCF has so far done a pretty good job predicting the trajectory of the virus, provided that we assume that an eighth of the population of England has T-cell immunity.
We estimate epidemic parameters continuously, and our estimates refer to a date four days before the latest figures for ‘cases by date reported’ are published on the government’s website. For comparison, the ONS infection survey reports only once a week, and its results are then published 10 days in arrears.
The growth of a virus, of course, can also be expressed in an R-number. Here is our estimate of the R number in the days running up to lockdown on 5 November (with the official ONS data superimposed). It casts doubt on what the Prime Minister was told that weekend that his Tier system was failing and the virus was surging.
Famously, keeping the R-rate below 1.0 has been the government’s policy. So it is natural to ask: if the Prime Minister had been aware of how consistently the R-rate had been falling in the days and weeks preceding lockdown, would he still have authorised the second English shutdown, with all its potential to exacerbate and extend the recession? The ONS data arrived too late.
But back to our PCCF models and their assumptions. We look at key factors: the number of active cases in the population, the number of new cases, the R-rate and the R0-value (both of which change all the time). The architecture of the measuring instrument, with a first-stage model feeding its results into a more realistic second model, has been chosen to make the measurement process quick and uncomplicated. There is, in any case, little point in trying to construct complex models when the data is sparse – which is likely always to be the situation whenever a new disease strikes and is certainly the case now.
However, there is another factor every epidemic modeller must acknowledge: assumptions on immunity. That is to say: how many are susceptible to the virus at any one time and how many are protected. This will affect how easily the virus can spread.
So our PCCF model can sense-check government figures – and work out how they relate to each other. Our model shows that they can only be reconciled by accepting a large degree of pre-existing immunity.
The point: Public Health England’s estimate of T-Cell immunity – about 13 per cent – is being ignored by policy makers now. But this figure, which can be deduced from two independent sources, is corroborated by our PCCF model. It could well be that more of us are immune than ministers think, and this has implications for both lockdown and how quickly the vaccination programme bears fruit.
It is worth spending a little time thinking about the assumption made in the models contained within the PCCF and, indeed, all standard epidemic models, that someone who has recovered from Covid-19 is now immune. It is not failsafe, because there have been a handful of well documented cases where someone has had a second dose. But, against this, there have been 77 million recorded cases worldwide (as well as almost certainly at least twice as many again that have gone unrecorded). While tough on the person concerned, it will not matter too much, from a population immunity perspective, if a very small number of people get the virus twice as long as the overwhelming majority retain their immunity while the epidemic is active. This is basically the assumption made with the vaccination process.
Our model suggests that 17 per cent of the population has recovered which, when you add to the 13 per cent who have T-cell protection, would make 30 per cent. Make no mistake, this is a significant amount of immunity.
With social distancing, the virus faces another headwind and one that suggests the restrictions can be calibrated to start off with the R0-value closer to 1.4 than 1.
Got something to add? Join the discussion and comment below.
Philip Thomas is Professor of Risk Management at the University of Bristol