During the COVID-19 pandemic, Denmark and Sweden took very different approaches. While Denmark imposed mask mandates, closed schools and repeatedly closed so-called ‘non-essential’ businesses, Sweden imposed hardly any all-encompassing restrictions. Lockdown proponents have accused the Swedish authorities of recklessness and claimed their approach has led to an unnecessary death toll.
But now the numbers are out, and according to two Danish professors, Christian Kanstrup Holm, a virologist and Professor at the University of Aarhus, and Morten Petersen, Professor of Biology at the University of Copenhagen, in an article in the Danish newspaper Berlingske Tidende on July 8th, excess mortality in 2020 and 2021 was in fact the same in both countries.
In Denmark, harsh restrictions were justified by the need to prevent the breakdown of the healthcare system and the public has generally accepted this justification. The professors’ conclusion however is that this justification does not hold: despite very few restrictions in Sweden, the Swedish healthcare system was never even close to breaking down.
In 2020 the Swedish certainly saw excess mortality while mortality in Denmark remained approximately the same as in previous years (see chart below). But in 2021 the situation is reversed, according to the data. The two professors also point out that in 2020 there was in fact no excess mortality in Sweden among those below the age of 75, which simply confirms how COVID-19 primarily attacks the oldest.
According to the models used to justify harsher restrictions in Denmark, about 30,000 people were expected to have died had Sweden’s strategy been followed. But according to the data, the excess mortality in Sweden over the two years was around 6,000 and in Denmark 3,000, which amounts to the same percentage as the Danish population is about half the Swedish. Thus, the models were off by around 90%. It might be added that this year we see continued excess mortality in Denmark well above that in Sweden (though not all the excess mortality is with COVID-19).
“It often happens,” the authors say, “that individuals, groups or even whole populations get caught in false dichotomies. Those are commonly based on powerful anecdotes and lead to a general acceptance of the validity of one or more claims, which do not stand up to scrutiny.“ While some false beliefs may be harmless, “they can also persist for a long time, even if they have serious negative consequences, both for individuals and whole populations“.
They urge the authorities to make sure that in the future all consequences, including negative effects of restrictions on public health, psychological well-being, education and the economy be considered. For this to happen “it is crucial to have the courage to debate and analyse”.
Thorsteinn Siglaugsson is an economist who lives in Iceland. Find him on his Substack page.
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There is zero chance that most people, or many people, in the UK were already infected. As of March 10, 25,888 negative, 373 positive. That tests for presence of the virus, rather than antibodies – but in a rapidly growing epidemic, most cases are very new and would test positive on a PCR-type test like this. They didn’t, so it wasn’t.
That simply does not follow. You can only apply random testing logic to a random test.
That’s correct. In the early days of the infection even those who had been infected without symptoms would have shown up in the initial bulk PCR tests. We did NOT see that, so NO hidden mass of ex-infected existed at that time.
We cannot know how many people had this virus prior to lockdown. Anecdotally, I can think of no one who did not have very unpleasant cold symptoms between late December & early February. So many probably had it as have not had symptoms again. Ferguson is not reliable. His work on Foot & Mouth left many of us with doubts about his modelling.
Jennine, how do you explain, then, that nobody died from these unpleasant colds in late December and early February, yet now we have nearly a thousand dead a day in hospitals and hundreds more in homes? The disease progresses from infection to hospital admission in an average of ~10 days and death an average of ~7 days later – there is abundant data supporting this timeline. The pattern of the disease’s arrival in each geography also fits exactly with the idea that nobody had the disease in the UK in December and very few in February.
I met someone who nearly died from a virus with respiratory complications in November 2019.
4,700 people died from respiratory illnesses in November 2019. Of those, about 90% were over 65 years in age.
The worst numbers this year for respiratory disease are in January 2020, 2,477 in the second week of January, higher than the 2,106 registered last week.
Numerical analysis seems not to get us anywhere, particularly since the whole of Europe is having fewer deaths this year than for the same period in 2016, unless Covid 19 has been massively overhyped.
Hmmmm………
This individual’s son was an estate agent showing Leeds University students around properties to let. Leeds University has an international partnership with Wuhan University of Technology.
In the clip below, Dr Peter Forster, a geneticist from Cambridge University explains how the earliest the virus could have crossed over to humans in China was 13 Sept 2019
https://www.youtube.com/watch?v=AQQf2yoymu0
Chinese nationals from Wuhan were hospitalised in Britain 24 Jan 2020 having been in the country for two weeks.
https://www.theguardian.com/science/2020/jan/24/coronavirus-uk-universities-issue-quarantine-warning-china-chinese-students-in-slugs
The autumn session of Leeds University began Wed 25 Sept 2019.
Knowing what we now know about the infectiousness of the disease, and the mildness of its symptoms, asymptomatic transmission in the able bodied, a great deal of transmission could have occurred in this country before red flags were raised concerning the virus in January 2020.
The Cambridge University paper commentary and link is here:
https://www.cam.ac.uk/research/news/covid-19-genetic-network-analysis-provides-snapshot-of-pandemic-origins
Looks like you’ve needed to revisit your thinking.
As always looked most likely, around 5%-10% of badly affected countries have had it so far and the elevated death rates were pretty much what the national modellers said they were: 0.5%
Funny how doctors around the world were able to spot a new disease though, isn’t it. Seems that if you work in an ICU, the Covid patients are obvious straight away. Again, nobody had it here in November, the pattern of accelerated deaths exactly fits the National models not your pet theory.
Anecdotal evidence but I work in an urgent care centre and last autumn there were a lot of people coming in with persistent cough lasting several weeks, including younger people. I had a persistent cough for 6 weeks around the same time, I have diabetes. I remarked to patients that there were some nasty viruses going around that seemed to affect people with diabetes and asthma worse. This coincided with the start of term at our local university with a large number of overseas students. A whole group of customer facing staff at the university library went off sick in January. Finally the virus was found in effluent in Italy and Brazil in November 2019. People’s reserves of vitamin D were higher than in January/February therefore more likely that their innate immune system could handle the virus. Whereas in January/February vitamin D is low unless people take supplements, it is also the time when other viruses strike. You cannot say categorically that the virus wasn’t present in one form or another late last year. Any admissions to hospital would not have been tested for the virus, simply a Lower respiratory tract infection. No autopsies have been performed so there’s no definitive evidence of death from CoViD19.
The fact that no autopsies were not being done should have sounded warning bells that governments were hiding something.
I know of no one who has had Covid never mind died from it, and after 14 months that is still very much the case!
It is great to see all the arguments addressed without hysteria or censorship.
It is very difficult to see how every area of the UK could be infected so quickly, with a very small percentage of the population infected. A model that spreads from a very few infected people at an R0 of 2-3 would take some time to reach across the UK in January/February.
It was delivered, that’s why.
The Finnish National institute for Health and Welfare estimates the IFR to be less than 0.2%
https://translate.google.fi/translate?hl=fi&sl=fi&tl=en&u=https%3A%2F%2Fwww.hs.fi%2Ftiede%2Fart-2000006473402.html&sandbox=1
How then do they explain how too many people have died in Bergamo? More than 0.2% of the population already. Or that Sweden is at ~50 deaths a day today in Stockholm when only 2.5% of Stockholm had the disease (from a representative sample tested) on the 3rd of April?
The only data that’s available for contained populations, or representative tests supports the Imperial model at 0.5-1.5% IFR.
Hi Will, possibly excess deaths ascribed to COVID but actually caused by limited medical facilities due to temporary overload.
Most papers seem to be taking the antibody samples as a lower bound – this is because positives tend to be retested to confirm but not negatives. Also, it’s possible that asymptomatic cases don’t generate very many antibodies (or any at all). Either/both of those would explain your conundrum.
More proof for a low IFR can be found with the COVID tracking app from Kings College and St Thomas’ Hospital which had up to 2 million cases on 1st April in the UK. That’s symptomatic cases.
Neither of those arguments stack up.
The Covid tracker as a data point for actual cases is a joke, surely. Some 10% of people report a cough at any time over the winter. It’s hard to find someone who doesn’t think “they probably had it in December”.
The Bergamo explanation stretches credibility to say the least. They were taking bodies away in Army lorries. 4,500 people died in a month in a tiny region having been treated for pneumonia in their 1000s. You doubtless saw the pictures and video and testimony of the doctors. 50 working age doctors and nurses died in Lombardy alone
Questioning the efficacy of the lockdown is totally reasonable. But to still try to make this a bad flu you have to turn to the slightest of evidence and implausible explanations and ignore the vast body of evidence from China, Italy and now Sweden. Plus the ice rinks used as morgues in the few places this ran free before social distancing and reduction in R. Occam’s razor says that the IFR is 0.5-1.5% and that only 5%-10% of people in the uk have had it. It fits all the data including the regional concentrations.
Is it a joke? They’re reasonably sure about it – it’s the best way we currently have to get any idea of cases. They’re not looking at coughs but clusters of associated symptoms and they’re using the (large number) of people with the app who have been tested to make judgements based on symptoms. So they’re confident they can discern between the Covid symptoms and a normal cold. If you read the blog https://covid.joinzoe.com/blog then you can see their methodology, and how they claim to distinguish different illnesses.
It’s a joke. Because we have had zero testing outside hospital for many weeks, this may represent our best data in the UK but it’s still a joke, it’s producing results laughably out of line with places that have actual Covid sampling data. In many respects it presents as flu, so how could it be accurate? Fortunately other countries have had better investigative testing regimes and know how many people actually had it at certain points in time.
Hi Will, do you have any sampling data from places that have also used the app? If so please link it!
Now that we have good antibody tests, it’s clear that it was as lethal as originally thought. Still only 5% have had it in Spain.
I am not an immunologist, but I have a degree in computer science and in order to get a handle on what is going on here, I have created an epidemiological model. As a result, it has led me to wonder about the criteria for ‘exposed’, ‘infected’ and ‘immune’.
In immunology there seems to be the concept of ‘minimum infectious dose’. If considering influenza (presumably better understood than Covid-19) we read the following:
“Influenza Virus Aerosols in the Air and Their Infectiousness.
Influenza is one of the most contagious and rapidly spreading infectious diseases and an important global cause of hospital admissions and mortality. There are some amounts of the virus in the air constantly. These amounts are generally not enough to cause disease in people, due to infection prevention by healthy immune systems. However, at a higher concentration of the airborne virus, the risk of human infection increases dramatically…. The human infectious dose of the influenza A virus, when administered by aerosol to subjects free of serum neutralizing antibodies, ranges between 1.95 × 10^3 and 3.0 × 10^3 viral particles…. it is important to consider that the risk of acquiring influenza is determined by both the concentration of the influenza A virus infectious particles (not their total amount) in the air and the immune status of the exposed individuals.”
https://www.hindawi.com/journals/av/2014/859090/#conclusions
So you can breathe in some of the virus, but if your ‘immune status’ is satisfactory you won’t be infected until a threshold is reached, your immune system giving you ‘infection prevention’ even though you have never developed antibodies to the virus before.
In fact, we have two basic types of immune response: cell-mediated and humoral. Both types have the ability to develop ‘memory’ i.e. they’ll act faster next time they encounter the virus. The humoral response is the one that produces antibodies. I get the impression that the cell-mediated variety is more general in its abilities, and would be less confused by virus mutations, for example.
In a study of 175 recovered Covid patients with mild symptoms, 10 were found not to have developed antibodies that could be detected later.
https://www.medrxiv.org/content/10.1101/2020.03.30.20047365v1
According to most people’s definition, they would not be counted as ‘immune’, yet they had successfully cleared their bodies of an infection of the virus. Do we know how many asymptomatic Covid-19 exposures/infections there have been? And what proportion have not produced antibodies as a result? I don’t think we have any idea.
So what if ‘herd immunity’ is not a binary thing defined by the development of antibodies, but an altogether more nuanced continuum of resistance, dependent on the real world doses that individuals are statistically likely to encounter and the states of their immune systems? If only 15% of a population shows antibodies, is it reasonable to conclude that 85% of them have not been ‘exposed’ to the virus at all? I don’t think so.
Three studies have found populations with approximately 15% of people showing Covid-19 antibodies. Some people are surprised it is so high, but others are disappointed it isn’t the 50% predicted as a possibility by the Oxford model. A recent article in the Spectator describes such a study in the German town of Gangelt. The author of the article, Ross Clark, asks very perceptively “…is there a ceiling on the number of people who are prone to be infected with the disease? Do many of us have some kind of natural protection against infection? Would it ever spread among more than about one in six of us?”
I think he is right to imply that it is possible that with Covid-19 we might never see 50% or 60% of a population acquiring antibodies by natural means; the virus may have been extinguished before then, most of the population having been exposed to the virus but without necessarily triggering the production of antibodies. It might be that such innate immunity to the virus is not as strong as the antibodies version, but is sufficient to prevent a ‘second wave’ even if an infected visitor comes into town – although unfortunate individuals might contract the virus (possibly a second time if their exposure was insufficient to provoke the antibody response previously).
I have modelled a population with two tier immunity, where the size of the dose (based on infection level, proximity and so on, with random variations of behaviour of individuals) defines the strength of symptoms and type of immunity triggered in an individual. I have tried to use ‘official’ figures where conceivably trustworthy such as the WHO’s timelines for contagiousness and symptoms in their four categories of Covid illness (no symptoms, mild, severe, critical).
The model converges on stable herd immunity with, say, 15% of the population showing full immunity with antibodies, and perhaps 50% with the lower form of immunity. I have assumed that members of the 50% can be re-infected if they subsequently encounter a strongly infected individual, and will go on to become highly contagious and ill, eventually forming antibodies. Even so, this infection doesn’t kick off a second wave in the people around them if the lower form of immunity is strong enough to damp the spread down. No doubt in real life there would be local factors involved such as demographics, living conditions and the way people socialise.
In summary, I am suggesting that we don’t know that Covid-19 herd immunity requires ~60% of a population to have acquired antibodies, and that ‘exposure’, or even ‘infection’, does not necessarily lead to the production of antibodies. If we are going to have to wait for that to happen before we lift the social distancing measures, we may be waiting forever. I think strategists and modellers should be open to this idea.
Great work Caswell. I’ve been writing about this possibility of a 15% infection limit on Conservative Woman for the last couple of weeks. Can you publish your model somehow – maybe Toby could host it here? Did you see that the Charles de Gaulle aircraft carrier reached 33% infections, maybe more once all results are in? I don’t think this invalidates your model as you allow for different conditions driving greater spread but it would be good to include it and maybe reflect on the difference between a cruise ship and a military vessel in terms of the amount of close interaction.
More data: antibody tests show 22% in Robbia, Lombardy were infected. That further corroborates the model I think.
Many thanks, Will. There certainly does seem to be a picture emerging. I saw a nice quote from Dr. John Lee in the latest Spiked:
“The UK models, as I understand it, said that 60 to 80 per cent of the population would be infected by the virus in a short period of time. Whereas in fact some of the evidence from enclosed populations, such as the Diamond Princess cruise ship, suggests that only 15 per cent of the population may be susceptible. Maybe that is because the virus spreads in a different way than we thought. Maybe some people actually have immunity based on other coronaviruses that are already out there in the population. “
That’s a great interview. Well done spiked. John Lee stopped short of saying that in his last Spectator piece so I’m glad he’s voicing it now. I hope you can get the model published somewhere – this seems to be very significant, especially with government listening to all the wrong people who don’t seem to be interested in the actual data or considering other possibilities.
Quick calculation: If 15% of the population are susceptible ~10M. Lowest figure I’ve seen for developing herd immunity is 62% so ~ 6M need to be infected. Most consistent average figure I’ve seen for IFR is 0.19%. Implies around 116K UK deaths with no action. By the end of this lockdown we will probably see about 25 to 30K. Summer may help, but we will need to smooth / delay the remaining 90K. Social distancing, masks and protecting the vulnerable will still be required to lower any possible subsequent peaks.
This doesn’t look right. The idea is that a form of herd immunity would develop after around 15% infections. So given 0.19% death rate the number of UK deaths would be 66.4m x 0.15 x 0.0019 = 18,924 ie similar number to what we seem to be heading for.
Derrick – where have you seen IFR of 0.19%? Only surely on skeptical sites like this – that level is very much an outlier. Vast majority of international models seem to assume 1-3% – the upper end coming when appropriate treatment isn’t possible. And that is because that level is supported by the only widescale representative sample studies eg Stockholm and Wuhan and because places where the virus ran free for weeks, like Bergamo, an implausibly high proportion of the population have died for a 0.19% IFR. – 0.45% of the entire Bergamo region have died. Together with other data it suggests that something like half the population was infected.
The figures are from the Iceland study where a high proportion of the population have been tested:
“Iceland’s higher rates of testing, the smaller population, and their ability to ascertain all those with Sars-CoV-2 means they can obtain. an accurate estimate of the CFR and the IFR during the pandemic (most countries will only be able to do this after the pandemic). Current data from Iceland suggests their IFR is somewhere between 0.01% and 0.19%.
and Finland
https://translate.google.fi/translate?hl=fi&sl=fi&tl=en&u=https%3A%2F%2Fwww.hs.fi%2Ftiede%2Fart-2000006473402.html&sandbox=1
Impossible to be definitive though until the pandemic has run its course or accurate mass anti-body testing has been widely achieved and “excess” deaths are known.
Where’s the data from Iceland showing only 0.01-0.19% IFR?
Stockholm area did a rep sample test on the 3rd April and found 2.5% infection rate with cases doubling every 6 days at that point and 50 deaths/day today, it gives an IFR of 1-1.25% (in line with all major international models).
There are a number of studies that support this kind of death rate including a Danish antibody survey, a German one and an American one from Colorado.
Will, that’s very interesting, would you mind posting links to those?
https://www.t-online.de/gesundheit/krankheiten-symptome/id_87680236/lockerung-der-corona-massnahmen-ergebnisse-der-heinsberg-studie-machen-hoffnung.html
https://www.dr.dk/nyheder/indland/doedelighed-skal-formentlig-taelles-i-promiller-danske-blodproever-kaster-nyt-lys
https://reason.com/2020/04/08/mass-antibody-testing-in-this-rural-colorado-county-sheds-light-on-covid-19s-prevalence-and-lethality/
Also https://www.medrxiv.org/content/10.1101/2020.04.01.20050542v1
Will, the German study shows 0.4%, just outside the 0.5-1.5% consensus.
The Colorado study is not complete and not worth its name yet, it only has 17 positive Covid cases, clearly not enough to draw an infection rate.
The Danish study is a strange sample of only blood donors and it’s author warns not to draw any conclusion from it.
That compares to representative studies from Stockholm giving 1-1.25%, the entire region of Bergamo giving 0.45% as a minimum *if every person was infected*. And more generally, the patterns of spread and death fitting easily with standard models with a 1% death rate.
It’s still possible just about that the IFR could be as low as 0.2% but the evidence stacked up against that is massive, the evidence for is sketchy, at best, it’s not what you’d call an educated best guess. It’s more of a guess with an agenda having to be very selective with data collection.
Question about Bergamo, Will – are you using total excess deaths or just deaths recorded as from COVID? Can you link your numbers? I can’t easily find recent statistics from that town.
Are the deaths spread equally amongst the population of Stockholm?
Do you have a link for Stockholm? Lombardy is generally regarded as atypical for local reasons such as poor air quality and high antibiotic resistance https://www.ansa.it/english/news/science_tecnology/2019/11/19/italy-top-in-eu-in-antibiotic-resistance_369e0123-0107-445e-8c17-f11932c9d27c.html.
You’ve rounded Gangelt up from 0.37 to make it seem closer to 0.5.
A new study from Stanford concludes 0.12% to 0.2% https://www.medrxiv.org/content/10.1101/2020.04.14.20062463v1.
Using data from the cruise ship Diamond Princess, Stanford Professor John Ioannidis showed that the age-corrected lethality of Covid19 is between 0.025% and 0.625%. https://www.statnews.com/2020/03/17/a-fiasco-in-the-making-as-the-coronavirus-pandemic-takes-hold-we-are-making-decisions-without-reliable-data/
The Centre for Evidence-Based Medicine (CEBM) at the University of Oxford argues that the lethality of Covid19 (IFR) is between 0.1% and 0.36% https://www.cebm.net/covid-19/global-covid-19-case-fatality-rates/.
The Finnish epidemiology professor Mikko Paunio from the University of Helsinki has evaluated several international studies in a working paper and comes to a Covid19 lethality (IFR) of 0.1% or less. https://dailysceptic.org/wp-content/uploads/2020/04/How-the-World-got-Fooled-by-COVID-ed-2c.pdf
And another ship, USS Theodore Roosevelt.
“As of today, 94% of USS Theodore Roosevelt crewmembers have been tested for COVID-19, with 655 positive and 3,919 negative results.”
https://navylive.dodlive.mil/2020/03/15/u-s-navy-covid-19-updates/
I believe that’s 14.3%.
Is it the case that you think this supports the idea that only 15% are susceptible? It surely supports a much higher percentage than that.
And another boat:
“Meanwhile, a Dutch navy submarine, MS Dolfijn, has returned to its Den Helder base two weeks early because of a coronavirus outbreak on board. Eight of the 58 crew tested positive and the submarine, which had been sailing near Scotland, is now in quarantine.”
https://www.bbc.co.uk/news/world-europe-52308073
That’s 13.7%.
Also, referring to the same Dutch submarine:
“Fifteen members of the MS Dolfijn’s 58-man crew developed mild flu-like symptoms last week and were tested on Monday. Eight were revealed to have antibodies for the virus.”
https://sputniknews.com/europe/202003311078781646-dutch-sub-cuts-mission-short-after-sailors-catch-coronavirus/
Thus suggesting that only half the people with mild flu-like symptoms went on to develop Covid antibodies..? Maybe it was just coincidence that the other half also fell ill, of course, or maybe they were tested too early in the illness.
Will/Caswell, reading this thread there seems to some confusion of concepts.
The herd immunity infection ceiling is determined by the r0 of the population. A high r0 will see much higher infection ceilings, a low r0 a much lower one. No two populations will have the same r0 and even within the population factors will vary the r0 and infection ceiling, like population density, London will have a much higher ceiling than rural areas.
Importantly a lockdown is an r0 influencing measure, it is trying to suppress a community’s natural r0 to one below 1 to stop the spread. Further, simply measuring infection at a certain point in an outbreak, especially when r0 has been manipulated by introducing a lockdown does not mean that the measured infection rate represents the ceiling for that population.
In all the studies so far (Diamond Princess, USS Therdore, etc) the populations were lockdown and measured, so all the infection proportion reflects is how far the infection had got before this was done. The only comparison you can draw is how quickly the leaders reacted. Without knowing what the r0 of that community was prior to lockdown we can make no estimate about whether it had found its natural ceiling or not.
Further, without being able to say two populations have a similar r0 you can’t compare infection rates or ceilings. This study has attempted to measure the natural average r0s by country before lockdown.
https://www.journalofinfection.com/article/S0163-4453(20)30154-7/fulltext
The UK and German are similar, r0 of 3 and roughly 65-70% for herd immunity, but this will be an average.
Until we discover that genetic variations make some people immune, or that crossover immunity is conferred from other vaccines like the BCG we cannot confidently lower the ceiling number. Worse if the BCG vaccine has just made cases mild, but people still contagious, it may easily have made r0 higher, because they stay more active in the community and spread it.
Hi Sinichol, that’s exactly what they’re discussing – possibility that there is a limit on susceptible people in the population.
Hi sinichol. I think I’m aware of all that, but my point is that when you say “65%-70% for herd immunity” you are allowing only for a binary ‘immune’ or ‘not immune’, and I think you would be counting anyone not showing antibodies as ‘not immune’.
I’m suggesting that there may be graduations of ‘exposed’, ‘infected’ and ‘immune’. In the real world, with some people receiving small exposures to the virus, they may already be resistant to infection to a certain level, or they may develop a degree of immunity that falls short of an illness with symptoms and the production persistent antibodies. Nevertheless, their immune system may develop a memory of the virus ready for next time. This might explain cases where a person seems to become re-infected having already fought off the illness. Maybe the re-infection comes when they receive a much higher dose. Maybe they really were infected previously as shown by the PCR test, but didn’t develop full, binary, ‘immunity’.
I’m effectively taking the virologists and immunologists at their word when they say ‘We know nothing about this; this is brand new’ and suggesting that there might be intermediate states that modellers are possibly not allowing for.
What seems crucial to me is that we don’t actually know that 60-70% of a real population would/could develop antibodies even if the virus was allowed to let rip. Hence the interest in populations that seem to be showing an upper limit on the number of people with antibodies. Of course lockdown and social distancing will have an effect, but it’s interesting to consider whether it’s possible that in many cases it’s been applied ‘too late’. If not, the timing seems to be exquisite in the way it freezes the antibody rate within such a narrow range, given that the idea is that this is an out-of-control rampaging infection that doubles every few days.
The reproduction ratio is an outcome from my model, not an input to it. Instead, I calculate transmission based on notional proximity between people, contagiousness that varies over the course and type of the illness, changes of behaviour due to symptoms, social distancing, lockdown and so on. It’s all guesswork of course, but it can show the general shape of the epidemic.
Modelling is not ‘double blind’. The modeller gets to tweak their model until it produces the outcome they seek/expect. But as it happens, the first time I ran my model with the two tier immunity idea it settled on about 15% antibodies! It has random variations, and the same model can settle anywhere between 15 and 30% (another coincidence) and leave between 5% and 20% of the population unexposed to the virus, the remaining people being at the intermediate level of immunity.
With my limited understanding of virology, I think you’re taking them at their word that this is completely new. Viral load is a know concept, and Chris Whitty certainly suggested a few weeks ago the reinfection capacity is not a new thing, it happens with other viruses too.
I don’t think any antibodies are binary with any virus, and I don’t think this is new to Covid. I’d imagine it is quite analog. As in you’re either:
1) a poor healthcare worker who just breaths in loads of virus, or you have a weak immune system and it gets chance to replicate loads and spread around the body, but either way you have large system wide infection before you finally overcome it.
2) you get a small infection or have a very good immune system and still clear the infection with a limited local reaction.
I’d imagine in the former case leads to lots of antibodies being present potentially being better spread out across the body. Whereas the latter see far fewer potentially locally provisioned antibodies, so that with a large viral load reinfection the immune system is still overwhelmed a second time before enough antibodies are created to overcome it again.
As to the model, from what you describe is sounds to me like you’ve ended up with model that needs recalibrating. An r0 of about 1.25 will yield a ceiling of infection of about 15%, the UK has been measured to be more at r0 2.9 and a ceiling of 65%. So you may have some bugs, or calibration issues.
I haven’t ended up with a ‘ceiling of infection’ of 15%. I have ended up with 15% of the people in the population showing antibodies. You are equating antibodies with ‘infection’ and no antibodies with ‘no infection’. Maybe that’s right, but maybe it isn’t.
In this version of the model, I am equating antibodies with ‘heavy infection’. I have a second tier of people who have been infected differently/less, resulting in a lower level of immunity and no antibodies. They have still been notionally infected, though.
If I add the antibodies people to the infected-but-no-antibodies people it is, perhaps, 70% of the population. So my ‘ceiling of infection’ might be said to be 70% on that basis.
The point is not to claim that this is definitely how it really is. It is to attempt to tie together a few ‘anomalies’ that we seem to be seeing and suggest that perhaps the modellers could be simplifying it too much. Neil Ferguson might instruct the government to wait until we see 60% of the population showing antibodies before relaxing social distancing, for example, but I am suggesting that we don’t actually know that it would ever come. We might need more subtle, nuanced assumptions, and maybe different tests if they exist.
Yes, you have created a model that has an infection ceiling of 70% by trying to model two well the analogue nature of infection!
If possible, please explain to Will Jones that you have really led him a merry dance with his mission to prove infection ceilings are all 15% regardless of what r0 the population in question is… I’ve done my best to explain!
By infection rate I mean infection that leads to antibodies, which is what is implied by the antibody surveys purporting to show infection spread and hence IFR. I understand that in Caswell’s model there is a larger group of a presumably lighter form of infection/ exposure. You could count that as infection but then you’d need to be consistent when stating IFR to avoid giving a false impression of death rate. If these ‘light infections’ don’t show up in antibody tests then there’s also no way to identify them, especially if they don’t show up in infection tests either. It may be helpful to use a different term to avoid getting confused between the two types of infection/exposure when they have quite different properties especially in relation to identifying them – one showing up in tests, the other not.
Will, if the infection ceiling is 15%, then the IFR must be at least 2.7% to explain the number of dead in Bergamo. I expect you won’t like that outcome, either.
A number of explanations have been put forward by specialists to explain the
high number of deaths in Lombardy including:
– greater susceptibility to pneumonia because of eg poor air quality or water contaminants (the area is known for this)
– older population
– deaths from isolation
– deaths from other conditions not being treated
– the health service being overwhelmed – this has happened before in bad flu seasons buthas been made worse in this crisis by healthcare staff needing to self isolate or not wanting to turn up to work.
See https://swprs.org/a-swiss-doctor-on-covid-19/ (search the page for Italy to find the relevant parts).
If you read my original comment, you’ll see that I explained precisely what I meant, and Will Jones understands it, too. Maybe you’ve leapt in half way down.
I am suggesting that the boundaries between ‘exposed’ and ‘infected’, ‘infection resistant’ and ‘immune’ are likely to blurred, not binary. A study has apparently found a number of Covid patients with mild symptoms who went on to produce no detectable antibodies. Were they originally ‘infected’? By your definition they weren’t. I say that they were infected in a way that didn’t provoke the production of persistent antibodies. I am also open to the idea that there are other levels of innate ‘resistance’ or maybe we could call it ‘immunity’ that act before ‘infection’. These may also contribute to ‘herd immunity’.
This discussion is, in fact, very illuminating and if Neil Ferguson also thinks along rigid, binary lines then I can see we’re in big trouble!
Caswell/Will, I think you’re confusing the discussion by adding in to the model a concpet that virologist already call something else.
The concept of genetic immunity is well understood by virologists. I think you’re trying to add that to your infection model in a way that threw me.
As an example the way that viruses attack cells can not affect certain people, so they don’t show infection as the virus can’t affect them so they don’t produce antibodies because they simply don’t actually get infected. These people are simply already immune, or need taking out of the total in some way – e.g. 2/3 don’t get infect so with an r0 of 2.9 and a ceiling of 65% for those that can be infected you will get a population ceiling of 65% x 1/3 = 21.7%.
I believe an existing genetic study in the UK has added Covid and is looking into this…
https://www.the-scientist.com/news-opinion/dna-could-hold-clues-to-varying-severity-of-covid-19-67435
It is entirely possible that this is the case with Covid, but I stress again none of the “have it now” numbers can show this, and none of the “have had it” (Gangelt and Robbio) numbers show either way at the moment, we either need an actual medical study, or to open up a society and see what happens.
My pinch of salf would be that I don’t think we have a strong handle on the capacity to spread this easily even if you doesn’t affect you. Simply spreading it on our hands, etc.
The natural r0 of a population is what determines an infection ceiling, but within it will vary based on factors like population density, so London has a higher ceiling than rural areas. A lockdown is an attempt to suppress the natural r0 of a population to below 1. This study looked at pre lockdown r0 by country.
https://www.journalofinfection.com/article/S0163-4453(20)30154-7/fulltext
The UK very similar to Germany at 3.
All the studies (Diamond Princess, USS Theodore, etc) locked down populations (changing their natural r0) then assessed the level the infection had got to at that point. Without knowing the r0 for that community prior to lockdown it is impossible to determine if it had got to it’s own natural ceiling, and wrong to take the infection level measured at that point as its ceiling, or to try to apply it to the UK as ours.
Ahead of our own serology survey the best we can do is to find a country as similar to ours as possible in lifestyle factors, healthcare provision, etc, that has done a serology survey and estimate from their figures. At present the Gangelt one from Germany, is the closest; similar r0 of 3 so infection ceiling 65-70%, although their median age is 45 not 40 like ours so their expected deaths are likely to be higher.
The hope is that genetic variation immunity or crossover vaccine immuinty (BCG) might allow us to lower this ceiling dramatically. Until clearly demonstrated, it would be foolish to do so.
To date the rhetoric in measuring UK and German cases is just naive. Measured cases and the mortality rate this forms is just a red herring, and totally due to them having tested 4x what we have per capita. All we can do is take deaths/1m of the two populations and use this as a proxy of relative spread, there’s is 48 ours 202, so we probably have 4x the infection they do as we would expect similar overall death as our r0s are similar.
We can use this comparison method to apply their study results to the UK. They measured 14% infected, so were slightly less than 1/4 of the way to herd immunity. They measured 0.37% mortality for 14% so 518 death/1m, 10x Germany’s national average and 2.5 times ours.
That ratio would mean the UK is roughly 5.4% (3.6m) infected and 1/10 of the way to herd immunity.
What saddens me about this article is that all this was known when it was put on this site.
Studies and surveys keep coming out showing 15% infection rate, including Shenzhen in China and Gangelt in Germany, often after the virus has been circulating for a while. No studies yet show much more than that, with the Charles de Gaulle quite an outlier at 33%. Why then do you assume herd immunity must come at 60% when no study yet has found a population at anywhere near that level of infection?
Very few places have had the virus circulating for long enough to reach more than 15%, have they? In Northern Italy in certain towns, it clearly was circulating with no change in people’s behaviour for many weeks (not the 2-3 months needed for reaching peak) and it’s clear that either the infections were well above 15% OR IFR was much higher than 1%…..
In Robbia, Lombardy 22% were infected. That’s more than 15% but not much more compared to 60%. The death rate in northern Italy may be elevated for a number of reasons including greater susceptibility to serious pneumonia and the consequences of lockdown for other health treatment. I don’t know if the 15-20% ceiling idea is true but given the data that keeps coming in it needs to be properly explored not dismissed because it doesn’t fit with the current models.
All the 22% reflects is how far the infection had spread before lockdown stopped the spread. It is not the natural infection ceiling, it is an artificial one created by the lockdown.
When they lift if the don’t keep r0 below about 1.3 infection will rise.
Interesting numbers they have measure 0.7% mortaility, almost twice that of 0.37% in Gangelt in Germany, the first real sign of the relative degree to which the Italian health service was overwhelmed. Very sad.
I think it is Robbio or Commune Di Robbio:
https://www.tgcom24.mediaset.it/cronaca/a-robbio-pv-il-22-ha-o-ha-avuto-il-coronavirus-ok-del-sindaco-ai-test-per-tutti_17285128-202002a.shtml
Some people, e.g. Suneta Gupta at Oxford University are estimating high R0 numbers, etc. That translates, for simple amateurs, to a doubling time every 3 days.
e.g., https://philip.greenspun.com/blog/2020/04/09/evidence-that-sunetra-gupta-and-her-oxford-team-were-right-r0-for-coronavirus-may-be-5-7/
https://www.medrxiv.org/content/10.1101/2020.04.04.20050427v2
The most interesting case to date is the French aircraft carrier that was probably infected when it visit port in mid march and had 1081 cases about 30 days later. 2 to the power 10 is 1024.
1) Infection ceiling
I don’t assume anything about the study groups infection ceilings, they will all be different. It is not some fixed number, the infection ceiling changes with the r0 – e.g. 1.25 will have an infection ceiling of roughly 15%, 2.9 will have a ceiling of 65%. The less aggressive the spread, the fewer immune people you need to stop it overall.
All the populations in the various studies will have had natural r0s and different infection ceilings, for the Theodore or de Gaulle likely really high as they all pack in like sardines, say r0 of 10 and infection ceilings of 95%. Diamond Princess probably lower than you think as lots of separate cabins, the London tube probably more like Navy’s! The UK was measure to have an r0 of 2.9, so a 65% ceiling.
The virus spread, and then authorities (governments or captains) intervened and lockeddown the population in question as people started dying – e.g. the Diamond Princess confined to quarters, the UK semi-lockeddown. Once lockeddown the r0 changes and drops below 1 which makes the theoretical infection ceiling 0% as in it can’t grow. So whatever the infection level was at that point is as bad as the spread got.
So all the difference in infection levels tell us is at what point authorities acted.
The UK is now at r0 below 1, so if we stay like this forever, the infection will burn out to some infection level. My estimate is that will be about 20%, based on a rough estimate that in a few weeks when this wave of infection dies down we will have probably had about 50k deaths, using Gangelt’s mortaility ratio of 0.37% will mean we are at about 20% with antibodies.
However, when we lift lockdown the r0 will lift again and the ceiling will rise too and infection will restart. I’d imagine with our increased caution and wearing masks it might be more like 2.3 (a total guess) which I believe ceilings at about 56%. It is fluid, and very local too. At that r0 and ceiling we’d see another 60-70k deaths.
Whatever the studies ceilings were is kind of irrelevant as what matters what the r0 of the UK will be if we lift lockdown, as this will define our new infection ceiling. If we can use funky phone contact tracing apps and we can get it to 1 then that would be great.
2) Comparing studies
You seem to be confusing active infections with antibody infection estimates.
Very few of the studies have used serology surveys (as in “who has had it”) they just did “who has got it” tests so you really can’t compare them. Diamond Princess, USS Theodore, returning Japanese nationals, Vo, Iceland, all did not. Gangelt did and they measure 2% active infection and 14% having had it, and a mortality of 0.37%, a really important study.
To clarify the active level of infection really tells you nothing becuase of the way it changes over the course of the spread, At the start of the infection you will have few active cases, this will rise to a peak of active case level, then die down again as it approaches the infection ceiling as spreading gets harder and harder. The height of the peak will depend on the r0, but without knowing the level of spread or the r0s you’re just matching up random numbers.
So the 2% from Gangelt and the 15% or 33% from the Theodore and de Gaulle. All mean something different, but uncomparable. My read would be that the two Navy ships will have had really high r0s because they pack in like sardines, and at 15% and 33% active infection levels they all had it, the infection ceiling was like 95% easy. They could lock them up for 21 days and serology survey them, but what does it tell us as they are all mostly men and probably mostly in their 20s/30s.
Gangelt was a proper unbias age/sex survey of a reasonably infected town, really comparable to the UK and the best guide until Porton Down get on with it and complete our surveys.
You’re forgetting Shenzhen which found 15% of those who lived with infected people had developed antibodies – that was unaffected by lockdown. So that’s Gangelt, Shenzhen and Diamond Princess – although the last may have had some who had recovered before they were tested. Then there’s Robbia at 22%. You are right that this could reflect a spread stopped in its tracks – but as Caswell says it’s quite a coincidence all stop around the same time, and match the Shenzhen rate which was unaffected by lockdown. Why not consider the possibility that there is a lower infection ceiling? You keep quoting natural R0 figures and corresponding ceilings but those are just the product of the assumptions put into them. What if this virus behaves differently to those assumptions, such as in the way Caswell suggests?
Diamond Princess did not do a serology survey, they simply sampled active infections as everyone came of the ship after 2 weeks of quaratine to the cabins, bear in mind the lockdown will have lowered active infection counts, especially after 2 week, and we don’t know how many had antibodies.
I’m not aware of a serology survey in Shenzen, if you are please share the link?
Gangelt and Robbio are the only two reasonably large, unbias sampled, serology studies that I’m aware of from countries I would trust numbers from.
It is no coincidence, when you notice people dying, you stop them seeing each other and you lock in whatever infection level has occurred by that point, and that this happens early in infection. We are humans, we act to protect.
As to thinking about infection ceilings, just look at the de Gaulle numbers, 33% active infections, active, not serology survey, so we have to assume more had had it and tested negative on Gangelt numbers of 2% active and 14% with antibodies. The de Gaulle r0 was probably like 10 and probably had the capacity for an infection ceiling of 95% or something mad like that.
I really wish they had locked all the sailors up separately for 21 days and done a serology study, it would really have concretely gone some way to answering the generic immunity question I discuss in my other response.
Read the other comment on Caswell’s thread about more subtle models. My reference to genetic or cross-over immunity in my original post is in my view the same thing as Caswell is trying to model and the way you were talking about it was just confusing. I agree the idea of including a certain capacity for these immune people to still spread virus matter and infect others is an important addition to the model, but we need to be really clear that this is what you are trying to do otherwise others will be confused too.
They tested for the virus rather than antibodies in Shenzhen but concluded ‘the household secondary attack rate was 15%, and children were as likely to be infected as adults.’ https://www.medrxiv.org/content/10.1101/2020.03.03.20028423v1.full.pdf
So the same as the Diamond Princess.
I memory serves the best guess is a 5 day test window with 70% accuracy. The study quotes 6.3 day mean serial interval meaning many tests of household members will have gone negative by the time they were sampled, and what if they gave it to the person that caused their contact trace test? They would definitely test negative for an active infection. Chances that 15% was the actual secondary infection rate seems really low, easily far higher, and you still need to actually catch it. Were talking about people that knew about it and will have self-isolated within the home so will have been actively trying not to catch it. This dramatically changes the r0.
Sorry, just don’t buy that number as meaning anything.
Did you read the paper? You appear to be dismissing their research and conclusions because it doesn’t fit with your assumptions.
These will also be of interest to you. Knut Wittkowski in a new paper concludes: ‘In China and South Korea (and the first wave in Iran), incidence peaked after about 2 weeks and then declined. In Europe it took and in the U.S. it takes twice as long. The shorter duration of the epidemic in China and South Korea, however, does not demonstrate the effectiveness of social distancing, because the social distancing started too late to be effective. Instead, the longer duration in Europe is consistent with premature interventions in Europe prolonging the epidemic.’
On Sweden: ‘There was no difference in the shape of the epidemic or the height of its peak to the other Scandinavian countries.’
https://www.medrxiv.org/content/10.1101/2020.03.28.20036715v3.full.pdf
In terms of lockdown reducing reproduction rate, it’s clear that in Germany the reproduction rate had already fallen below 1 before the lockdown https://www.rki.de/DE/Content/Infekt/EpidBull/Archiv/2020/Ausgaben/17_20_SARS-CoV2_vorab.pdf and the same in Switzerland https://bsse.ethz.ch/cevo/research/sars-cov-2/real-time-monitoring-in-switzerland.html.
Yes, I read the paper, it makes no claims to be asserting an infection ceiling, you’ve taken a number and decided to imply that yourself. I’m just trying to point out to you why it is wrong to do that. Please read my response to sunchap below which tries in the clearest way possible explain why you can’t read PCR “have it now” surveys results like that.
The Knut Wittowski paper, which I’ve just read, is terrible. You can’t compare cases, they are just a function of testing levels. So he just compared a whole load of policy driven almost random numbers that don’t even provide him with comparatively correct proportions of true infection levels between countries, meaningless. Until we have serology numbers, you can only compare deaths/1m between comparable countries and even then only to gauge the relative degree of spread. Everything else is a fools errand given testing strategies are all so different.
Sweden has the same measures in place as us, the only steps they did not take is to enforce working from home and close schools. People keep talking like it is so different, go to any park in the UK, it really isn’t that lockeddown here, not like Italy was. Worse still for comparisons they are have 1/10 our population density, 2x the healthcare provisioning per capita, have been able to contact trace aggressively because of much better testing provisioning, so they have been able to suppress r0 in ways we have not slowing their epidemic like Germany and have been able to avoid lockdown so far. Good job, on them, but there per capita there figures aren’t too different from ours and there is less clear evidence their numbers are falling, time will tell.
Both Germany and Switzerland lockeddown 15th of March, but had introduced considerable curbs to freedom ahead of this, and Germany too is the champion of contact tracing, the best in Europe, likely to be the reason why its death/1m is 52 and ours is 215. I don’t speak German, but from what I can fathom of the paper, it had r0 a 1.8 at lockdown on the 15th and it seems quite clear on worldometer’s new case plots that they continued to see aggressive case growth peaking on the 28th. For Germany I think the paper supports that it was the lockdown was what got the r0 below 1.
I agree Switzerland’s paper is puzzling. On worldometer, they certainly seems to continue to see new case growth like Germany until the 28th, but the paper does seem to show r0 dropping to 1 around the 15th. It would require much deeping digging, understanding all the measures they had already introduced, but they weren’t in anyway “running around freely” state for the few weeks before the 15th, and we don’t really know if testing strategy changed, or exactly what happened, but things do look inconsistent.
When the paper concludes ‘the household secondary attack rate was 15%’ I hardly think I’m inserting the idea myself – they don’t say ‘15% of those tested tested positive at the time of testing,’ they draw the wider conclusion.
I agree that it seemed odd that Wittowski relied on test data. Test data obviously doesn’t show the true spread, and when test numbers are changing the raw number of positive tests is not a useful statistic and has been widely misused. However, once the number of tests becomes more constant then it can be used to detect a decline in infections, if the testing strategy also remains broadly the same.
News reports say Germany imposed lockdown on 22 March not the 15th. Do you have a link for the earlier date?
Sweden does not have lockdown in anything like the same way as other countries, though yes it has introduced some social distancing, much of it voluntary but being observed.
Willow has an interesting comment on yesterday’s post:
“The first response of the immune system to an invading pathogen is mediated by what’s called innate immunity. Innate immunity is not pathogen-specific. It is a range of bodily defences that include local inflammation, fever and the engagement of cells called Natural Killer Cells to literally consume the pathogen – as well as others I won’t go into. It’s complex!
“Healthy young people and adults under ~40 seem to be able to see off the virus with just their innate immunity. This is good, it means the virus didn’t present much of a threat to them. They don’t have antibodies only because they didn’t need to produce them. They are not “immune” in the antibody sense, but they aren’t particularly vulnerable either.
“If the innate immune system doesn’t defeat the pathogen, that’s when the adaptive immune system kicks in and the chain of events that leads to antibody production is kicked off.”
This corresponds to what Caswell is talking about I believe and would explain the reducing of the reproduction rate ahead of lockdown and the low infection and antibody rates surveys are finding. Importantly it means herd immunity defined by 60% antibodies is not a realistic or necessary goal, and a form of herd immunity is achieved much sooner.
Yes you are, they are simply saying that was the number of active infections measure at on average 6.3 days after the original contact in other people in their household. They make no claim this is in infection ceiing. That is your theory.
… and as data science it is very flawed.
I’m not saying anything different to Caswell. I’ve said all along that genetic immunity is entirely possible in subset and they will NOT present antibodies. If 50% have such immunity, then you simply start the population infection at that level, so for an r0 of 2.9 to achieve 65% herd immunity you would need to measure 15% antibodies to get to overall herd immunity, and Gangelt is done. Robbio 22%, could mean the same just that the r0 was slightly higher in that case making the infection ceiling 72%.
I entirely accept this is possible. I really want this to be true, but I’m just saying you can’t interpret PCR test study numbers the way you are to argue this, and you need to be looking for medical study proof the virus can’t infect certain people. All the quotes above are rhetoric not medical study results.
We can only use antibody survey results for comparison, and if the society locked-down we have to read it as them having artificially suppressed their true infection ceiling by lowering their r0.
If we don’t see second waves as societies open back up, then the genetic immunity (with no antibodies) theory becomes a stronger argument, if we do, it quashes this theory.
Thanks. I don’t really disagree with this comment, except that the claim isn’t that people have genetic immunity that means the virus can’t infect them. The claim is they have an innate immunity based on a first response of the immune system that gives them a measure of resistance. They can be infected if they are sufficiently exposed to it. This might be (if this is true) why some surveys in some contexts are coming back with higher proportions of positive tests.
I have just posted a fresh thread with all the unanswered questions I see open from this and other broader discussions, grouped into 4 strands.
I hope this means you accept why PCR results can not be read like this.
We all accept that antibody numbers may NOT be recording enough immunity in a population that we might all have been exposed to the virus, at the same time we need to remain open to the fact that no numbers disprove that the lockdown is simply suppressing the spread.
There should be no ego here, just a search for evidence.
As to your point, if their response does not show up in the antibody tests then it is in the same group as genetic immunity as it represents a group people who won’t show up in the antibody test numbers, so this strand could be added to group 3 on my open questions list?
I agree if merits further investigation, although it could just be a theory that explains the same numbers you would see if people are just getting the weaker of the two strains of the virus, and the Gangelt/Robbio antibody test does not register people with immunity to that strain.
Bigger problem then we are trying to track two different viruses as one and does this also explain the reports of re-infection? This theoy needs adding to strand 1… sigh.
It is a shame we can’t take the strands and have a better way of getting them all tracked and discussed technically in a better way…
There seems to be a lot of ‘bootstrapping’ in epidemiology. Dodgy stats (that the same scientists would accept were dodgy if asked about them) are ‘laundered’ to produce values like ‘R0’, then everything else is derived from that, resulting in statements such as “…the estimated number of people who could potentially die from COVID-19, whilst the population reaches the Pcrit herd immunity level, may be difficult to accept.”
I can’t help but start from the position that the ‘confirmed cases’ figure is dodgy – we know it is. So I wouldn’t even get to the next stage of fitting a model to it and declaring I knew the value of ‘R0’. And I wouldn’t run the model based on this dodgy ‘R0’ and then make a sort of moral judgement on the output of my own dodgy model!
Is the R0 used derived from confirmed cases? That’s obviously going to be inaccurate.
I think you guys are on to something. Covid 19 clearly turned down BEFORE the lockdown in Wuhan and South Korea : Wittkowski on. MedArxiv. It also went down naturally in Japan and Sweden. Something must have caused this and it must be immunity in the population.
The only accurate studies as to maximum infection rate are stated above and point to a “saturation” rate of about 15-20%. Being cooped up on the Diamond Princess for two to three weeks must maximise infection. It is highly possible that many humans have developed cross-immunity from other coronaviruses, i.e. the common cold.
It also appears unlikely that 60-70% have been infected from all the data.
In terms of cross-immunity, that’s exactly what German virologist Christian Drosten has now suggested. https://www.watson.de/!324026684
They would still show antibodies so would be included in the 14% or 22% from serology surveys to date, just part of the mild group. They wouldn’t be a reason to argue that even with an r0 of 3 we won’t see those numbers rise to 66%.
The silver bullet we are all after is genetic immunity, as in some chunk of the population that can be exposed to the virus, don’t get it and therefore never need to produce antibodies.
If they can find that, say 2/3 of us are like that then all of a sudden only 1/3 of us can get it and will develop antibodies so that 66% ceiling becomes 22% and we know that Robbio is all the way there, pub time!
… but, we need that kind of silver bullet.
Not just genetic immunity, cross-immunity too. And maybe some blurring of the boundaries between ‘exposed’ and ‘infected’, ‘infection resistant’ and ‘immune’, as Caswell suggests.
Yes, I mentioned both too, my point was that cross-immunity will still lead to antibodies, as it means you do become infected, but you have similar antibodies you can adapt to fight the infection.
By genetic immunity I mean you don’t even become infected, you have no need to develop antibodies.
The former you would get counted in a serology survey, the latter you would not, so modelling and accounting for them in interpreting number is very different.
The Diamond Princess did not do a serology survey. You’re really misreading the numbers and thinking about it wrong.
Firstly, let’s consider the measure you’ll get from a “have it now test” as the spread starts you’ll read low cases, this will rise to a peak halfway to the infection ceiling then reduce again up to infection ceiling as the virus finds it harder and harder to propagate – e.g. a population with a ceiling of 65% at 63% infection will probably show 0.5% (guess) active infections, much like it did at 2% spread, but at 30% probably 10-15% (guess). The height of the peak will depend on the r0 and window length of a positive test. So without knowing these figures comparing “have it now” samples is pointless.
Secondly, think about the dynamics of r0 and infection ceiling. An r0 of 1.25 will have an infection ceiling of 15%, 2.9 will have 65%, 6.6 of 85%. It is just harder for a slow spreading infection to spread with fewer people being immune. So comparing active infection measures without knowing the r0, or how progressed it was, is meaningless.
Thirdly, think about how the active infections changed over the quaratine in the the case of the Diamond Princess (or any “have it now” survey)… they were all milling around on the ship enjoying their cruise say with an r0 of 3 and an infection ceiling of 66% with the virus spreading quickly. Suddenly they started dying and got rapidly confined to quarters and sat there for 2 weeks in quaratine.
During that time what happened was that the r0 was push to below 1, and spread will have dropped dramatically over the pre-lockdown state. So at the start of the 2 weeks we will have had a true capacity to test active infections that had been caused by the r0 of 3, by the end you have a very different sample, as lots of people will have gone from testing positive to testing negative.
Here is the actual study data…
https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.10.2000180
If you look at the table of the 3711 passengers they had only tested 336 by 5 days into the quaratine. The bulk of the tests were done after 10 days. So the positive test numbers are going to look nothing like what they would have a day 0 of the quaratine.
Add to this that the r0 will mean the active test measure will be at most say 10-15% (as per example above) even if you’re heading to a 66% ceiling because you only have a 5 day window in which it will be positive. All this combines together to make “have it now” test result interesting, but incomparable, and certainly not something you infer an infection ceilings from.
You can only meaningfully look at serology results, so Gangelt and Robbio, 14% and 22% spread, and this is just how far they had got before quaratines stopped the spread, without genetic immunity evidence we have to assume that lifting the quaratine will cause further infection as the infection ceiling will lift to whatver the new r0 will determine. Sure we might all be more cautious and that r0 might be below what it was before the lockdown, creating a lower infection ceiling, but if that ceiling is higher than the current level of spread, we will get new infections.
This is an interesting podcast, with professionals talking among themselves about SARS-Cov-2:
https://www.microbe.tv/twiv/twiv-602/
Maybe the most interesting bit starts at about 57.45
The main points I take away from the podcast are:
1. The most common phrase is “We don’t know” (that’s not a criticism of them, it’s just how virology seems to be)
2. Mild infection/illness can result in low antibody counts, or antibodies that fade over time. Not much is known about it.
3. Reinfection might be possible, or maybe not; they don’t really know.
4. Antibodies for a previous respiratory virus were found to be present in about 30% of workers in a Chinese market.
5. Everyone seems to agree that the only practical outcome is natural herd immunity. “The virus is so widespread”.
6. Then they start talking about vaccine trials and the problems with possible vaccine induced enhancement. It seems to be superfluous, given point 5 – as though they are following a professional road map that must end in a vaccine, and the bit about herd immunity was just a moment of offline speculation.
7. Nice people can do very nasty things to mice.
8. A very low dose of the virus can act like a vaccine.
9. The dynamics of children becoming infected and taking it back to their parents is one of the possible contributions to herd immunity. “The parents don’t get sick because they have a little bit of immunity, right?”.
10. The expert immunologist, having described the many possible subtleties of the immune response, ‘hands over’ the problem to imaginary modellers, saying that they can tell you what proportion of a population needs to be immune to ensure herd immunity. I think this is very revealing. As far as I can tell, none of the immunologist’s knowledge (or uncertainty) about the shades of grey in immune responses would cross over the professional boundary to the modellers, and he wouldn’t inquire too deeply about what their definition of ‘immunity’ means, exactly.
The apparently blind handover to epidemiologists seems very worrying and may be where things are going awry.
Did you see Willow’s comment on yesterday’s post? It seems very helpful for your model:
“The first response of the immune system to an invading pathogen is mediated by what’s called innate immunity. Innate immunity is not pathogen-specific. It is a range of bodily defences that include local inflammation, fever and the engagement of cells called Natural Killer Cells to literally consume the pathogen – as well as others I won’t go into. It’s complex!
“Healthy young people and adults under ~40 seem to be able to see off the virus with just their innate immunity. This is good, it means the virus didn’t present much of a threat to them. They don’t have antibodies only because they didn’t need to produce them. They are not “immune” in the antibody sense, but they aren’t particularly vulnerable either.
“If the innate immune system doesn’t defeat the pathogen, that’s when the adaptive immune system kicks in and the chain of events that leads to antibody production is kicked off.”
It means aiming for herd immunity defined by 60% antibody presence may be unrealistic and unnecessary as a form of herd immunity is achieved much sooner due to a large proportion of people having this ‘innate immunity’.
That’s brilliant. Many thanks.
Yes, I’m beginning to realise that some scientists are very uninterested in fields other than their own – seemingly going from total expertise in their own field to sub-member of the public awareness of others, even if closely related to their own. I find that fascinating, because you’d imagine that the philosophy of science would run through their veins; that they wouldn’t be able to stop themselves from absorbing science in general like a sponge.
They might not know exactly how a self-driving car works, for example, but you’d think they might understand perfectly well that there’s a crucial philosophical and political question about who the car should kill in the event of an unavoidable accident. The Covid-19 crisis seems to suggest that this decision would be left in the hands of people like Neil Ferguson, simply because that person would be an ‘expert’ and the rest of us couldn’t possibly have a valid view on it.
The interesting questions that remain open for investigation to me (please feel free to add) can be grouped roughly into these strands:
1) The New Scientist reported a while back that there were two strains to Covid, one weak, one strong. UK infection could well be a mix of both, Caswell’s model could be seen as trying to model both at the same time, but only recording immunity to the smaller one.
https://www.newscientist.com/article/2236544-coronavirus-are-there-two-strains-and-is-one-more-deadly/
… there is an S and L type, the L was more prevalent in China.
a) Is there evidence of the UK tracking the two different types?
b) Does the PCR test pick up both?
c) Are the really sick testing mainly for one type?
d) Does immunity to one confer immunity to the other? Or, do they need modelling as two totally separate viruses?
e) Does the Gangelt/Robbio test look from antibodies for both?
f) Did Gangelt and Robbio use the same test? And how accurate is it, might 14/22% have large margins for error?
e) Why is the Porton Down so slow to release antibody results? Are they stuck with all these problems too?
… this is worth a read…
https://www.sciencemediacentre.org/expert-comments-on-different-types-of-test-for-covid-19/
2) The de Gaulle PCR test showed 33% active infections, huge. It means populations can see that level of infection, it does not disprove the 50% genetic immunity argument we are all so keen on, it is just on a cramped boat the r0 could be much higher, making the infection ceiling say 95% meaning 45% needed to get infected to achieve herd immunity leading to 33% being active when tested, as they all just got it really quickly due to the high r0, so trying not to get distracted by pointless unanswerble numerical conjecture… it raises:
a) Would an antibody survey have counted that many? Can we find evidence of PCR testing shows more cases when tracked over the term of the infection than antibody testing shows at the end?
b) If so, does PCR count both strains and antibody only one?
d) Can we find any evidence of ongoing research into any of these trying to extend a PCR study into an antibody study? e.g. locking up sailors.
e) Are there any examples of studies showing much higher serology proportions than Gangelt/Robbio from confined populations with really high r0s?
3) What are the genetic immunity research projects ongoing around the world and what are their theories so far?
a ) Is there evidence some can’t be attacked by the virus?
b) Is there evidence that at day 10 of infection something distinct makes you get the nasty kind?
4) Why are communities like China and S. Korea that have reopenned not seeing aggresive growing second peaks? They are counting cases, but they don’t seem to be growing yet.
a) Still too early to tell? It did take longer for thngs to takeoff first time round.
b) Have they found aggressive effective contact tracing strategies? This was certainly why S. Korea never had a proper outbreak in the first place.
c) Is China copying their approach? What do their phone apps do? Do they really work? Should we follow?
d) Is there strong evidence that the virus has simply died out during the lockdown?
e) Or, is it the case that it can’t resurge because we have all had it and we just don’t know how to measure all the different types of immunity yet like we all hope? – e.g. second strain has different antibodies or generic immunity, etc. Scientific study evidence we can trust from any of these countries?
Lots of unanswered questions. Please feel free to post solid looking scientific evidence on any of these.
Yes, a good list of questions. At the end of the day, though, doesn’t the size of the list show simply that ‘life finds a way’? (to quote the Jeff Goldblum character in Jurassic Park)
Even if we answered all the questions, a third strain could pop up tomorrow. And a universal vaccine for the first two might react badly with it, making the current infection look like a (government-sanctioned) walk in the park.
Maybe we should just accept that we need to carry on living as a free society, and although we don’t like the idea of the threat of this virus in the background, we’re just going to have to get on with it?
Charles De Gaulle ended up at 59% active infections – huge number.
Yes, I really hope they do a serology survey on them. This will help fill in the picture, if the antibody numbers are lower then we know either the test does not measure all antibodies, or that there simply might no be antibodies. If it does, then we know it is much more likely we have simply slowed the spread.
Plus, I really don’t like the way no ones discussing the different strains. For me it fits with Caswell’s model, and potentially what we are observing. Take this scenario…
Strain1 is deadly, but harder to spread so only has an r0 of 1.25.
Strain2 is mild and spreads easily with of 3.
The PCR test picks up both.
The antibody test only shows antibodies for 1.
All the results we have fit this scenario, as would Caswell’s model.
As an r0 of 1.25 would limit Strain1 to an infection ceiling of 15-22%, so Gangelt/Robbio results would be for populations at their infection ceilings.
Strain2’s r0 would mean an infection ceiling of 65% and the de Gaulle PCR results.
If no immunity confers from one to the other, we basically just have two viruses, but if it does then it gets even more complicated as it depends on the order you get them. We know China concretely detected two strains. Why does this no longer matter?
I really wish someone would ask Chris Whitty about this… or for some news agency to spend real time researching the situation…
Did some digging on the Santa Clara serology survey numbers of 0.12-0.2% mortality.
https://www.medrxiv.org/content/10.1101/2020.04.14.20062463v1.full.pdf
Unlike Gangelt/Robbio the author concedes there is bias in the population sample “mostly white women (63%) contacted via Facebook who could drive to test centres, age restricted to 19-64”, and the sample failed considerably to match the demographic of the County. He has had to massage the data to try to correct for this. What worries me more is he makes no observation about the possibility that this will have changed the r0 and therefore spread of antibodies in his sample. If they had a higher r0 (they certainly are able to travel more than the background population was willing to do) then they will have seen higher spread and his denominator in the mortality calculation wil be too big…
More worryingly though, he has also had to estimate deaths to rush his results out. He based them on a sample of only 50 deaths in hospitals in Santa Clara by the 10th of April projecting what that number might be by the 22nd, but does not seem to make any mention of needing to make allowances for in-community deaths. He estimates this will lift to 100, but if that just lifts to 185 the top end of his range would match Gangelt.
I’m taking that study with a pinch of salt until he can at least revise on actual deaths.
On the antibody test which is what matters for mortality ratios, I’ve found this article…
https://www.aruplab.com/news/4-21-2020/How-Accurate-Are-COVID-19-Tests
… which suggests 90% accuracy at 14 days increasing after that. So the 21 day window they recommend makes sense, no discussion of strains.
This was one of the studies they looked at from China…
https://www.ncbi.nlm.nih.gov/pubmed/32221519
… still don’t like the “of symptomatic cases” bit. What about asymptomatic cases? How do they know you can’t be exposed, be asymptomatic, and develop no antibodies like we suspect? Does not jump out of the work.
A very weak article. As the author points out, we simply don’t know a lot of basic data that would allow us to model. We do know that this pandemic has the capability to swamp our health care systems. We do know it kills people old and young. We do know it is killing our healthcare workers. We do know it is quite contagious – but still have work to do on exactly how it spreads. We do know reducing our social contacts cuts transmission and saves lives – and costs us dearly. We also know that we are not equipped with enough PPE or testing.
Until we have some more answers which should be a matter of weeks & have sorted out PPE supply and testing, it seems sensible to cut the spread, reduce the load on our healthcare systems, understand the facts and formulate a proper plan. So yes, the Imperial work is not correct – they are some best estimates with the data we have. I’ll tell you something though, 68% of the population are not infected. I do wonder if the Oxford team quoted this to warn that their models and analysis can throw out odd results.
“it seems sensible to cut the spread”. Quite a statement – if you look at what it means in practice.
Quite a result if you do not act too. Every country is taking some measures to different degrees. Many have gone for limiting interactions – the so-called lock-down which it isn’t.
On reflection, what is so controversial about cutting the spread of a deadly, novel virus for which nobody has natural immunity? Sure complain about how it’s done but not the goal surely?
Of course we have some innate immunity – we do to most pathogens. We may also have some acquired immunity via cross immunity from other coronaviruses. In any case though Sweden is having a better run than us with much weaker restrictions. And it’s now clear the epidemic plateaued before the lockdowns. Plus the lockdowns are themselves killing people. So they really aren’t worth it.
Latest research from Sweden suggests lockdowns to be a complete waste of time.
‘The study indicates that for every confirmed case of COVID-19, a further 999 people are likely to have been infected with the virus without knowing it.’
https://www.forbes.com/sites/davidnikel/2020/04/21/sweden-600000-coronavirus-infections-in-stockholm-by-may-1-model-estimates/#2f5e717f78d6
The virus may well have crossed to humans in China as early as mid September 2019.
Some universities in Britain have exchange programmes with Chinese universities including The Wuhan Institute of Technology. Students started work in Britain after the summer break on 25th September 2019.
It seems increasingly likely from the research that the virus has been with us for a great deal longer than originally thought (and modelled).
Sorry but the Forbes article does not conclude that social distancing is useless. It is stated that the work done that is the centre of the article is too small. There is a lively debate in Sweden about what their experience is. I am not poo-pooing tegh study. I am saying that we just don’t know yet. It’s early days. The data is thin and they medical community is working hard to find the answers. You nor I have them.
Of course social distancing is useful for avoiding contagious viruses.
No-one has suggested otherwise.
But the Swedish study referenced by the Forbes article very much suggests that lockdowns are a waste of time, as I point out above.
Professor Johan Giesecke, former Chief Scientist of the European Center for Disease Prevention and Control (ECDC), further suggests that lockdowns are politically motivated rather than evidenced:
“We take into account the scientific evidence. Norway and Denmark have a more political leadership that want to show strength by implementing restrictions.”
Hmmmm……….
In the John Ioannidis interview above, at about 33 minutes in, he talks about the phenomenon of immunity without antibodies. He says that “preliminary data suggests” that perhaps the majority of younger people may be able to clear the virus without developing detectable antibodies.
https://off-guardian.org/2020/04/21/watch-perspectives-on-the-pandemic-4/
Therefore, you would think he might mention this in his recent serology study on the streets of Santa Clara. If the prevalence of antibodies shows that “the infection is much more widespread”, leading to revised epidemic and mortality projections, then presumably it may be even higher than calculated due to young people having been included in the tests.
Clearly, there is no hint of such shades of grey in the standard epidemiological models, nor in the calculations of what constitutes herd immunity. It is conceivable that policies could be based purely on prevalence of antibodies such as deciding when to lift the lockdown or the issuing of “immunity passports, or decisions on the need for a universal vaccine. It does seem to be a rather important detail.
Of the utmost importance, yet seemingly being only mentioned in asides and not being given the prominence it deserves in models and policy. Bizarre, and incredibly frustrating.
I wrote about this on TCW yesterday to try to raise awareness of the point but I don’t know if anyone is listening. https://conservativewoman.co.uk/we-might-be-more-immune-than-we-thought/
New York antibody results in https://twitter.com/NYGovCuomo/status/1253353516803993600:
13.9% across state, breaks down:
Long Island: 16.7%
NYC: 21.2%
Westchester/Rockland: 11.7%
Rest of state: 3.6%
These are all in the low range we’ve been discussing, even NYC.
Deaths plateaued only 9 days after lockdown suggesting the lockdown was too late to stop the spread. https://www1.nyc.gov/site/doh/covid/covid-19-data.page
It’s around that magic 15% again…
I’d like to see some actual proof that mild cases or younger people don’t produce antibodies… Do you know if age statistics of antibody studies have been published?
On the other hand even if this is the limit of the infection it puts the IFR at 0.5 – well within bad flu epidemics.
0.5% of the UK population of 66m with a infection ceiling of 65% is 215k deaths…
Remember, Flu has a much lower r0 0.9–2.1 (vs 1.4–5.7 for Covid), so has an infection ceiling of about 20%, which means on top of its CFR of 0.1% killing 1/5 the people it only needs to do so with 1/3 of the people they think Covid will to burn itself out.
On these measured numbers (not models) 15x more deaths than Flu.
Given de Gaulle shows you can measure at least 59% active Covid infections… we need:
1) substantive proof lockdowns don’t curtail spread, Sweden is looking dodgy this week…
2) proof serology testing misses big enough proportions of people who were infected to mean that the CFR is too high, and that we are much closer to the infection ceiling than they think in these places…
… for us to have a concrete case this is an over-reaction…
Check out swedenborg’s post on ‘latest news’ article here.
‘Today you can see the most likely day of maximum deaths is most likely 8th April or in the week after. There is a clear downward trend the last 5 days. The day of maximum new intensive care cases seems to be around 2nd April and it also seem to be a downward trend the last days. On the general website you can see that of the 2152 deaths (Swedish population 10 mil) 87% are over 70 years in fact a staggering 24% of all corona virus deaths are 90 years and over (Life expectancy Sweden 82 years). There are no deaths under 20 and only 36 deaths under 50.’
So explain to me why looking at Sweden’s total weekly measures up to and including yesterday’s numbers you get:
NewCases Deaths
Mar 27-Apr 02 2728 231
Apr 03-Apr 09 3573 485
Apr 10-Apr 16 3399 540
Apr 17-Apr 23 4215 688
… don’t believe the rhetoric of people who have a vested interest in proving themselves right… LOOK at the data.
Deaths will not fall if new cases don’t… looks to me like they have an r0 > 1.
Now it is slow growth, they may make herd immunity without busting through ICU limits, I have not forecast beyond this. I say good luck to them, but don’t fool yourself because they choose to fool you… they have not peaked.
Would it be fair to say that the curve is slowing though? I don’t see any evidence it’s about to go skyhigh in the next few days from the intensive care numbers.
Arguably keeping the disease on a ‘slowburner’ is the best way to get through.
I have no crystal ball, I’m just very keen we interpret data correctly.
Trouble is they’ve locked in their next 3 weeks already. We’ll just need to see what happens.
… but, the next Fri-Thu weekly total started cumulating today up on last Friday’s numbers at 812 new cases and 131 deaths, the former their largest ever single new day new case total… but, I agree possibly one day’s noise…
I’ve just checked your numbers Simon, I only make 400 deaths reported from the week ending 23rd thus far. Surely it’s fairly useless going by number reported on a particular day, which you appear to be – especially as Sweden has a huge break on weekends. It appears to me that a lot of the numbers of deaths in the last couple of days have been prior to the current week – at least 288 of them thus far. Ignoring the current week, as that is the most likely to be revised upwards, I get
Mar 27-Apr 02 318
Apr 03-Apr 09 609
Apr 10-Apr 16 673
We’ll have to wait another week to see how far up those 400 go – but if they’re anything like our own stats, by far the highest number are recorded from n- 2 where n is the current day. It tails off quite rapidly. I’d guess that we might see around 650 again.
but you’d also expect to see higher weekly numbers in the ‘plateau’ stage than in the week with the peak, as that’s been the case in all the other countries.
What are you talking about? I’ve given you weekly totals (not daily) entirely to remove the noise of their weekend reporting lag. Putting commas in to make it clearer for you…
Mar27-Apr02, 2728, 231
Apr03-Apr09, 3573, 485
Apr10-Apr16, 3399, 540
Apr17-Apr23, 4215, 688
As to ignoring the large numbers this week because you’re worried they’ll be reported UP!? WTF… the observation is only going to get worse so we can observe it now without fear…
I don’t see how you get the 17th to the 23rd to total up to 400…
Date NewCases Deaths
Fri17, 676, 67
Sat18, 606, 111
Sun19, 563, 29
Mon20, 392, 40
Tue21, 545, 185
Wed22, 682, 172
Thu23, 751, 84
… totalling up, and I really can’t make this any clearer,,,
Cases: 676+606+563+392+545+682+751=4215
Deaths: 67+111+29+40+185+172+84=688
Making a week Fri-Thu minimises the effect of their weekend lag… but, it really makes no difference which day of the week you use as cutoff as long as you do the same thing each week…
Where have you sourced your numbers?
My source worldometer:
https://www.worldometers.info/coronavirus/country/sweden/
From Swedenborg’s post, previously referenced:
‘The absolute majority is using the worldometer which is of very doubtful value especially as regards deaths which is not recorded on the day of death but reporting date.’
‘I recommend everyone to got to the Swedish Public Health website and you can see for yourself update each day https://experience.arcgis.com/experience/09f821667ce64bf7be6f9f87457ed9aa
When you see the map of Sweden on that web site you should click below on “information om datakallor”
There you would be able to download an excel file with daily updated figures for day of death (avlidna) and day of intensive care.
Today you can see the most likely day of maximum deaths is most likely 8th April or in the week after. There is a clear downward trend the last 5 days.’
Hope this helps.
I have sourced my numbers from the swedish health ministry. You should know that the numbers on worldometer are the total REPORTED that day. I added up the numbers in the graph on Avlidna/dag – deaths by day – from the swedish portal, linked below.
https://experience.arcgis.com/experience/09f821667ce64bf7be6f9f87457ed9aa?fbclid=IwAR2aLwJtRHm-CPooa1q1S8La8TsFq01u2RyS5uIyNCHiGYVHq1pvjMM_yt4
This is obviously a better metric because they are adding in deaths in hospices & care homes when they happened (the lag for them is greater)
See this chart: https://twitter.com/gizhkoandrii/status/1253658265310957573
Now it’s quite obvious that the tailing off is a product of the lag for reporting, but it’s also quite obvious that they have been at a plateau for a while.
The worldometer numbers are from their official hospital daily mortality numbers, like ours they are always LOWER than ONS (which is what this site is the equivalent of), and these all-mortality stats always lag hospital stats.
I think it morally quetionable given their last offical update date was to the 12th to show partial numbers beyond that will always tail off to very small numbers in recent days. It leads to misleading rhetoric like your twitter post.
You’re also wasting our time here, the data lags by up to 2 weeks, and in the end makes no difference for measuring the trend. BOTH yours and my totals for the 3rd-9th and 10th-17th show the same 11% increase in deaths not a plateau.
Regardless, my entire point was about NEW CASES, for these two ranges it looked as if new cases HAD plateaued in Sweden. BUT, this week new cases are up, 25%, and there is no rhetoric or dramatic change in their testing strategy this week, which means this is r0>1, these are from infections 15-20 days ago and in 2 weeks deaths will go up too. This needs watching this week.
No, their worldometer numbers are lifted directly from the total increase each day on the site I link to above. Not my twitter post, and he adds in (in grey) the average increase expected, which is not unreasonable.
I don’t see a huge increase in ‘Sjukdomsfall per dag’ beyond previous weeks?
It would appear they also backdate cases/day to the day the case was reported – for example Worldometer has 17/04/20 at 676, whilst the government portal has it at 688. The last 4 days are quite high, so we might be seeing an increase in cases, but it’s too early to tell.
If you think that numerically it will lead to a big difference, it won’t.
Sweden have marked all figures before the 12th closed, which means all the people recording deaths (and cases) are always trying to make sure that they clear backlogs in 2 weeks.
Looking at one overnight sample…
Date, 24th, 25th, %change
Apr12, 96, 97, 2.5%
Apr13, 84, 84, 0.0%
Apr14, 91, 90, -2.5%
Apr15, 108, 109, 2.5%
Apr16, 107, 107, 0.0%
Apr17, 72, 74, 5.0%
Apr18, 72, 73, 2.5%
Apr19, 73, 76, 7.5%
Apr20, 70, 70, 0.0%
Apr21, 42, 46, 10.0%
Apr22, 40, 43, 7.5%
Apr23, 31, 41, 25.0%
Apr24, 3, 19, 40.0%
… 93% of the change (40) affected the last 7 days. This daily measure totalled up to a week, will reflect in the same proportion the number of deaths added to the last week, it essentially does a bit of smoothing at the same time.
There will be the same total number of deaths reported either way.
In my view the arcgis representation is a morally far more questionable, their plot of it will always tail of to low numbers more recently over the last 2 weeks making it always look like deaths have peaked, when they have not.
I think worldometers attribution (although less accurate about exact day) makes all the days in the plot equally comparable as they are completely reported and not to be revised, whereas arcgis’ certainly does not.
These two different ways of reporting exist in all countries, and if you don’t understand why it is perfectly valid to interpret worldometers version the way I have then please do wait seven days to discover what I have already observed.
New infections are a product of more testing. Antibody tests show there is a great deal more of the virus winging around, doubtless started a great deal earlier, than anyone had appreciated when they started modelling. Mortality figures are ‘with’ not ‘from’. The data is junk. As I keep repeating, there is no international consistency in recording cause of death so comparisons are pointless.
It is not a measured number of 65% ceiling except in one context. (Also you’re assuming all infected develop antibodies.)
Hi Simon, I didn’t mean to compare with normal flu, but with particularly nasty strains, which can come up higher than that in CFR. What was the CFR of the Hong Kong Flu? This paper:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3357941/
estimates it at 0.5%, and Spanish Flu at 2%:
“The CFR of Spanish influenza is sometimes thought to be approximately 2.0% [5, 24] and those of Asian and Hong Kong influenza pandemics are thought to be up to 0.5%”
Swiss doctor has this:
The German Network for Evidence-Based Medicine reports that the lethality of a severe seasonal influenza (flu) such as 2017/2018 is estimated by the German Robert Koch Institute [government] to be 0.4% to 0.5%, and not only 0.1% as previously assumed. https://www.ebm-netzwerk.de/en/publications/covid-19
… but the important bit is:
“In 2017/18, 25,100 people died of influenza in Germany [12]. This death rate corresponds to 5 million infected people based on the CFR of 0.5% calculated by the RKI for 2017/18. According to the RKI report on the epidemiology of influenza in Germany in 2017/2018, the influenza season lasted 15 weeks, from the 52nd calendar week of 2017 to the 14th calendar week of 2018 [11]. In order to reach 5 million within 15 weeks, the number of infected people would have to double every 4.4 days”
Which means the natural infection ceiling with no mitigation measures at all was 5m/83m = 6% (not 65%) making the r0 about 1.08, so CFR the same, but was 12x less infectious so it did not kill 300k people…
We are talking 1968!? You’ve instantly promoted Covid from seasonal Flu comparsions to once in 100 year events.
From that study, the r0 of Hong Kong Flu was measured to be similar to that of seasonal flu in the first wave 1.06–2.06, and slightly higher in the second 1.21–3.58. The second wave look between seasonal Flu and Covid.
This means it will have burned out at a lower infection ceiling infecting fewer people in the process. So as much as they have the CFR, there would just have been fewer caes with out mitigation strategies… at the moment we’ve certainly lowered the r0 with hand washing/social distancing and made it more like this.
No going to the pub, restaurant or club though…
Did they shut down in 1968? Was there mass hysteria? I think Covid might be on a par with 1968 (possibly) but the lockdown is still a huge problem.
As I’ve already said it is not just about having equal CFR, you have to factor in that a lower r0 creates a lower infection ceiling too…
Hong Kong Flu killed 1m people globally:
– the global population was 1/3 of what it is now
– we were far less interconnected
– and it had a lower r0, probably infecting 1/3 to 1/2 of people Covid might
All your telling us so far is that Covid mortality globally could be in the region of 6-9m when seasonal flu only kill about 650k.
Higher numbers in CDC homeless shelter study: San Francisco 66% residents; 16% staff, Boston 36%; 30% (both with big Chinese communities), lower elsewhere.
https://www.cdc.gov/mmwr/volumes/69/wr/mm6917e1.htm?s_cid=mm6917e1_w
The modelling assumptions (on which the now plainly silly lockdown is based) are looking less and less sensible as more and more data comes in.
Added to which, U.S. Covid 19 mortality rates are unreliable:
‘At Tuesday’s White House coronavirus press conference, task force member Dr. Deborah Birx said that while some countries are reporting coronavirus fatality numbers differently, in the U.S. you are counted as a victim of the pandemic if you die while testing positive for the virus, even if something else causes your death.’
https://www.realclearpolitics.com/video/2020/04/08/dr_birx_unlike_some_countries_if_someone_dies_with_covid-19_we_are_counting_that_as_a_covid-19_death.html
CFR was 0.5% so bang in the middle of Gangelt (0.37%) and Robbio (0.7%) for a healthcare response that creaked more than Germany’s, but not as badly as Italy’s, so consistent, and makes John Ioannidis numbers look even more like an outlier… but, to be expected given he used estimates and had sample bias.
I don’t understand how you make it 9 days?
I think you’re misremembering what they did… they closed everything on the 16th and then locked down like Italy (don’t leave your house) on the 22nd. As this article on the 18th discussed from the 9th crowds were thinning out…
https://www.bbc.co.uk/news/business-51880799
This piece really shows the fall off in subway travel from the 9th…
https://talkingpointsmemo.com/edblog/when-did-new-york-city-shut-down
From your link: hospitalisation plateaued 30th-6th, and deaths peaked on the 7th Apr.
Even from the 22nd to the 7th that is 17days to deaths peaking.
BUT, given they closed everything to the same level we have on 16th, and subway travel had more halved by the 14th it is easily 3 weeks since r0 would have been heavily impacted by their measures. So you can’t dismiss the lockdown as having caused it.
Entirely concede we need somewhere without a lockdown to show they kept growing to demonstrate it wouldn’t have just burnt out itself.
The NYC hospitalisation plateau (drastic slowdown in growth rate) begins on 25 March, surely too soon after the 22 March lockdown to be attributed to it? And then there’s Switzerland and Germany doing similar. And Sweden of course. Surely adds up to a strong case.
Their shutdown in terms of subway rides began on the 9th down 10%, by the 13th down 35%, by the 14th they were at 1/3 of the daily journeys, sliding down from then to a 1/4 by the 20th, and at a roughly 1/10 beyond that.
Incubation period of 5 days, expect hospitalisations to peak 10 days from mitigation measures, which they do by the 30th, but it records 3 further days equally high admissions days through till the 6th.
Your assertion of the 25th is that of a novice. It does briefly plateau and dip 28th/29th, but these are a Sat/Sun, and if you look the week before on the 21st/22nd the admissions numbers dip too, in fact if you look at the plot all Sat/Sun days are lower, suggesting some kind of weekend reporting periodicity.
I do this for a living, I’m a data quant, I look for trends and patterns in sources of data, and work out how to predict statistically likely future events from them.
Germany, by the survey you sent me, when we actually analysed the data had only got it’s r0 to 1.8 before the full lockdown, and this had only happen from 2.4 the week before by having a week of closing everything during that week like New York did…
Switzerland, we have never discussed, what is your evidence?
Please stop “Will”fully misreading the data to fit your narrative, we need real evidence.
What numbers are you suggesting as average time from infection – hospitalization – death? I’ve seen longer numbers than 10 days (6 day average from infection – > symptoms then 7 days for symptoms -> hospitalization.) I’ve also seen 22-24 days from infection to death. Would you broadly agree with those?
I’d also like to know whether you think the same with the UK data? To me it looks like the peak of deaths (8th April) was about 5 days too early for the lock-down to have had much effect, assuming the 22-24 days is correct, and that previous voluntary efforts were sufficient to cause cases to peak. Do you disagree?
I was going on 5 days incubation, 10days to present, 10days till death, as averages I’d read from various places. Hospitalisations start from 10 days in peaking at 15 days.
So we have reduction in numbers from the 9th, peak hospitalisation at the 30th with a slowing ftom the 25th, a plateau to the 6th, then peaks deaths at the 7th.
Tracing back 25 days you get to the 14th, which seems perfectly plausible to me as the lockdown taking effect.
But the lockdown has to coincide with the slowdown of infections not the peak to be regarded as the cause. And while I take your point about subway rides declining from 13th that was far from lockdown, with much remaining open.
Patrick Vallance has said a 75% disruption to community movement is all that is needed to get r0 < 1. This is not digital, so any reduction up to that will slow the rate of hospitalisation.
7th/8th a weekend saw 45% of journeys, people enjoying time at home.
Mon 9th at 90% … dropping to 64% by that Fri 13th (caused by social distancing measures).
This 7th-13th range 15 days later would be the 22nd-28th, exactly the range you are saying saw a slowing in admissions, upto the peak on the 30th then a plateau through to the 6th.
Yes, it is not conclusive proof lockdowns work, as it could be an entire coincidence, but if anything it shows it working exactly as they intended.
But social distancing that stops short of lockdown to flatten the curve is what many lockdown sceptics advocate. So this analysis, crediting the pre lockdown social distancing, supports their position not the lockdown zealot position.
Will, that does not follow.
No one denies social distancing will lower r0, the question is simply whether for a particular population it lowers it enough to avoid more invasive measures.
One size will not and does not have to fit all.
In my view the evidence from around the world seems to be pointing toward population density being the biggest deciding factor. Sweden is 1/11 our density, looking at cities…
NewYork: 10k people per square km
London: 8.6k
Stockholm: 4.8k
… and from the looks the rate of virus spread pre any measures London and NY were growing too quickly and they needed the extra measure to get their r0<1.
As to Sweden, they were growing slower and so far have got away with not needing the extra measure. That said it looks to me like they have a very slightly positive r0, say 1.04, and have seen a 25% growth in new cases in the last week. They might be able to get that under control again without new guidance and no lockdown, good luck to them. Great if they can ride to herd immunity without needing to do so, we just need to get ourselves to the same place.
In my view London and NY (and some other UK cities) needed their lockdowns to create time to think and plan, the rest of the UK, maybe not so much, but it is hard to sustain different policies like that in terms of people doing it.
As to the rest of the US, as per this plot…
… I'm sure Nevada or Idaho (<25) I'd say it can easily now be shown that they could have coped with the same measures as Sweden, and states are independent enough to have been able to do different things.
I don't disagree that social distancing had an effect in NY, as you say from the 25th hospitalisations where slowing, but NY closed bars, restaurants, etc from the 16th, on the 22nd they went to the Italy lockdown level. So there were already well beyond what you're talking about being enough.
I simply don't agree that the peak, plateau and fall off between the 30th and the 6th cannot be attributed to these lighter measures.
Trying to find fault in NY's strategy to demonstrate that Sweden's or even Stockholm's policy was correct is a fools errand, the comparison are meaningless in my view.
Each population has had different circumstances and needs.
I'd really like to be spending more time investigating lockdown mortality than arguing the toss over things people would like to be true, that plainly aren't.
My lockdown scepticism is based on its use beyond a short window to create breathing room to put policy in place. I think as a tool for everyone to use it is probably excessive, as a longterm way even for cities to manage the r0 of Covid is too blunt an instrument. There will be a whole load of unintended consequences. BUT, trying to argue it will not have been an effective shortterm tool in densely packed city like NY seems like a pointless exercise to me.
Go look at Nevada's data and argue it there, then we have some meaningful policy guidance.
Mistake! … should read…
“I simply don’t agree that the peak, plateau and fall off between the 30th and the 6th CAN be attributed to these lighter measures.”
I agree population density appears to be a big driver of rate of spread. I imagine it will drive the overall infection rate as well. I also imagine air quality and population healthiness (eg obesity), among other things, will affect mortality rate.
How do you know what the case growth rate is in Sweden? It obviously isn’t the reported case numbers which are just a reflection of the number of positive tests. Is there a reliable data source?
This sounds reasonable: ‘In my view London and NY (and some other UK cities) needed their lockdowns to create time to think and plan, the rest of the UK, maybe not so much, but it is hard to sustain different policies like that in terms of people doing it.’ But when you think about what that costs you have to ask whether the cost can be justified. If the health service can be boosted to cope, what is actually gained that can possibly justify the immense cost of lockdown?
You seem incredulous that lighter measures in the UK/London could reduce the reproduction rate to below 1. Yet peak UK deaths occurred on 10 April, 21 days before that is 20 March – so peak infection in the UK occurred 4 days prior to the full lockdown. Why doesn’t that speak for itself? This isn’t wishful thinking – it’s not the result I expected, I was surprised it was true, I was convinced by experts and analysts pointing it out.
I’m happy to disagree on this. As you say, one can be a lockdown sceptic without denying that full lockdown could be justified for some places for some period of time.
Will, choose your experts better, that intepretation of London is as flawed as the one for NY…
… tube journeys started to decline heavily on the 10th of March… down to 50% by the 15th… 25% by the 20th… even from the 20th that is 3 weeks to the 10th.
… kick the tyres on other people’s theories before taking them as your own when they are clearly not very good analysts… you’re filling the debate with falsehood and I’m having to spend far more time than I would like keeping us honest, and spending far less time being able to research real data on lockdown mortality.
In fact the CEBM data with deaths on day of death shows that the UK peak was on 8 April and London’s on 4 April. That suggests London’s infection peak occurred on 14 March, 10 days before lockdown.
… what, matching the day by which tube travel in London had halved?
Please think about what you’re saying.
If only obeying social distancing caused the London peak, then on the 14th the data would need to show that 100% of people were still travelling to work and only following those rules… BUT, it clearly does not.
You’re ignoring data staring you in the face.
Even by the 14th 50% of tube users had clearly changed behaviour before the official lockdown, people thought Boris was being wreckless, they had self-isolated themselves working from home.
It peaked around that day and was slowing before it. If you think that fact supports lockdown then that’s up to you.
Will, I merely think the data doesn’t disprove it could have. For me two things remain possible from the London and NY data.
1) the lockdown had the effect
2) errors in antibody surveys mean these populations were hitting their infection ceilings at the same time, and the lockdown was conincidental.
I’m pulling you up for trying to make conclusive statements about 1, that I would not put money on.
What is increasingly frustrating me, is you’re occupying all of our time discussing what you’d like to be true, but in the data plainly isn’t.
If you want people reading this site looking to see if we are credible to judge we are, you need to make sure you really kick the tyres on what you’re saying, before you say it.
I’d like to be able to look at information you’re posting and trust your judgement on it, and I can’t.
Isn’t this the point though? Voluntary action had a strong effect? I don’t think any (sensible) lockdown skeptic would suggest otherwise; Sweden might be more normal, but it’s hardly fully normal.
Why do you suggest that 100% of people following the rules is necessary? What if it only took 50% reduction in public transport to lower the R0?
We should be encouraging people to work from home, reduce public transport. We shouldn’t be putting them under house arrest, we shouldn’t have enabled a police state, we shouldn’t have crashed the service economy (where social distancing was possible, as it is in most highstreet shops, garden centers, restaraunts.) We shouldn’t have closed the schools – there’s relatively little data that this changes much, especially in the case of Coronavirus.
We should have continued to quarantine anybody with symptoms
And we should certainly be interrogating the science around outside transmission.
I don’t suggest that. I was just pointing out the conditions that would have needed to be present in the data for me to take the interpretation that Will has.
I do think a 50% reduction is enough to significantly impact transmission, they recon 75% is enough to get r0<1. I don't think 50% of people no longer going to work on the tube represents following just the advice of washing hands and keeping 2m apart, which is what I take Will to mean when he says "the lockdown definitely had no effect".
Maybe he mean't something else.
According to your graph above the tube was at 60% on 14 March and other forms of transport had barely reduced at all. And that was the peak – it slowed before that. I regard this as possibly showing the power of some modest social distancing – assuming it wouldn’t have happened anyway. It certainly speaks against the necessity of anything stronger.
Any idea where the data on London hospital admissions is published? I see it referred to in various places but haven’t been able to find it.
No need to be rude! We’re on the same side here and if you disagree then it doesn’t need to come with insults or accusations of dishonesty.
I can recognise a drastic reduction in growth rate when I see it and that occurs around the 25th, starting five days of similar numbers after a period of fast growth.
We have discussed Switzerland before. This is the link. https://bsse.ethz.ch/cevo/research/sars-cov-2/real-time-monitoring-in-switzerland.html
You said: ‘I agree Switzerland’s paper is puzzling. On worldometer, they certainly seems to continue to see new case growth like Germany until the 28th, but the paper does seem to show r0 dropping to 1 around the 15th. It would require much deeping digging, understanding all the measures they had already introduced, but they weren’t in anyway “running around freely” state for the few weeks before the 15th, and we don’t really know if testing strategy changed, or exactly what happened, but things do look inconsistent.’
My apologise, even to the 25th, we are still talking 10 days back inclusive being to the 16th inclusive at which point subway journeys were down to 25%, and Patrick Vallance talked about 75% being the disruption level needed to get r0 < 1. Given the peak lasts to the 6th, it seems entirely plausible the lockdown could take credit.
I just don't think it is conclusive evidence.
Apologise again, I recall Switzerland now, worldometer seemed to support lockdown, the paper was confusing and did not. I think we need more evidence (are there any other studies?) to be sure…
Sweden is the centre of the lockdown focus now, they have locked in the next 3 weeks, we just need to see what happens.
In a spirit of helpfulness (from Swedenborg’s post):
‘One of the most disputed issue on twitter threads or blogs is the reporting from Sweden. The absolute majority is using the worldometer which is of very doubtful value especially as regards deaths which is not recorded on the day of death but reporting date. The most important figures are always the date of maximum deaths and date of maximum use of intensive care. There is a predicted time lag from infection peak to COVID deaths peak of approximately 21 to 28 days. The peak of intensive care is most likely a week before peak of deaths.
I recommend everyone to got to the Swedish Public Health website and you can see for yourself update each day https://experience.arcgis.com/experience/09f821667ce64bf7be6f9f87457ed9aa
When you see the map of Sweden on that web site you should click below on “information om datakallor”
There you would be able to download an excel file with daily updated figures for day of death (avlidna) and day of intensive care.
Today you can see the most likely day of maximum deaths is most likely 8th April or in the week after. There is a clear downward trend the last 5 days.’
My point was about new cases… and this ticking up again this week… and your website shows exactly the same problem.
As to the entirely secondary you have created about deaths, assessing the general direction of travel from hospital daily figures by assessing weekly totals is entirely reaonsable, and as per the other post both sets of numbers when used to just look at direction of travel show the same 11% increase.
I repeat again, that website (for deaths) have marked numbers up to about the 12th as official. They have a similar 2 week lag to the ONS in gathering all the data, the ONS has only reported to the 10th.
From then owards up to now daily counts will always tail of to zero as reporting from different regions comes in at different rates…
I think it is morally very questionable for them to display partial figures as it will ALWAYS make it look like deaths have peaked. Which is entirely the trap you have fallen in to.
The ONS don’t do such a live stream for exactly this reason. It just isn’t honest about data, and for them to actively say it is a better source is almost like the state trying to pull the wool over its own electorates eyes… sinister.
Well done for playing devil’s advocate, providing good perspective on all the numbers. Very much appreciated. The state media narrative shifted this morning, big time, so I think the panic is over. All that remains is a politically stage managed retreat from lockdown to grab headlines now likely to be full of mud hurled in the general direction of the government, much of it, alas, well merited.
Thanks, I just want arguments to be sound.
I have no issues with making sure they have their numbers right on mortality, etc, but once it is clear that they aren’t fooling themselves it is really a case of working out the impact of the unintended consequences and demonstrating we may have the wrong lesser of two evils.
Caswell. I’ve noticed that antibody prevalence seems to vary in surveys so far between a few per cent up to a third of the population. In NYC for example it was 21 per cent but in NY state overall around 14 per cent. It seems to be higher in denser population centres. I imagine that different communities have different curve profiles for the virus not just in terms of rate of growth but also height of peak. I guess it’s because different conditions interact with pre-existing resistance differently to give different requirements for herd immunity. So a city like New York has a higher requirement than the wider state. Is there any way to reflect this in the modelling?
Hi Will. I think the Imperial College model already has this structure in place:
“… individuals reside in areas defined by high-resolution population density data. Contacts with other individuals in the population are made within the household, at school, in the workplace and in the wider community. Census data were used to define the age and household distribution size. Data on average class sizes and staff-student ratios were used to generate a synthetic population of schools distributed proportional to local population density. Data on the distribution of workplace size was used to generate workplaces with commuting distance data used to locate workplaces appropriately across the population. Individuals are assigned to each of these locations at the start of the simulation.”
The aim of such a simulation might be for effects such as those due to population density to fall out of the model i.e. to actually see different levels of immunity developing for people within cities. However, this would depend on realistic core assumptions, such as the type of illness varying with viral load. Different ways of living (e.g. urban versus rural) might then produce different levels of immunity once the epidemic was trailiing off. But if the core assumptions don’t contain those subtleties then I don’t think it will make any difference. If your definition of infection is the same regardless of viral load, for example, it won’t make any difference whether you catch the disease in a city hospital or from drifting too near to someone in a village shop.
At this stage I don’t think anyone knows how the illness varies with viral load or, for example, age, gender, ethnicity – which would all differ in their distributions in urban versus rural environments. One of the reasons for modelling could be to try some intelligent guesses and see whether it produces something approaching reality, but I don’t know whether anyone is doing that. If, as Hugh Osmond says, they are fixated on prescribed models such SEIR with a single value for ‘R0’ then presumably not..?
Thanks Caswell. Now that much more data is available there should be much more effort put in to developing models that conform to the data. As you say, instead there just seems to be a stubborn sticking with the seemingly simplistic models already developed. Or rather, if they are being refined or revised in light of the real world situation then I have not seen any evidence of that.
It looks to me from the antibody surveys (and antigen testing) that have come back so far that the basic assumption that herd immunity requires 60 per cent antibodies (and the virus will continue spreading in every community until it reaches that point) may be incorrect. Instead it may be that communities with different profiles tend to peak at different infection rates, varying between perhaps a few per cent up to, well, the highest result so far is around 33 per cent in a district in Boston I think. What would be helpful is if someone was collecting all the antibody results that come in along with the profile of the community they are from and trying to develop models that produce those levels of antibodies in those communities (taking into account at what stage of the epidemic the survey appears to have been done).
The model I developed does contain the idea of type of illness being related to viral load – but that’s because I didn’t ‘know any better’, having come to this without reading anything at all about epidemiological modelling.
It really does seem to me that there’s a barrier between the immunologists and the modellers. The immunologists know there’s a lot of unknowns, even regarding established viruses, and they may be all too pleased to look at the beautiful apparent certainty that the modellers produce. And the modellers need something definite to work with that can’t be challenged so they stick to the ‘tried-and-tested’ models.
Everyone seems happy with the arrangement.
Susceptibility!
If you assume that 1/3 of the population, i.e. young people, is naturally “immune” (or something approximating to immune); doesn’t develop measurable quantities of antibodies; and that this proportion is also not contagious when it gets exposed, then it follows that herd immunity is achieved at around 33% antibody-positive for an overall R0 of 3.
Yes indeed. And it could vary in different places depending on the conditions such as population density and age profile. But all I hear is people parroting the 60 per cent figure as though it is a solid datum rather than an estimate based on a model based on assumptions.
The 60% figure is just the estimate of the average herd immunity ceiling for a pathogen with a measured initial r0 of 2.6.
As per Caswell’s observation Ferguson’s model already varies the strength of the r0 for different parts of a country by taking detailed demographic data and modelling that those outside a city like NY would see lower penetration and a lower ceiling, but I’d imagine in NY city higher penetration and a ceiling more like 70%. Creating an average of 60%.
He seems to have data on things like classroom and office density. He has really gone to town on this.
Bear in mind this would also allow him knowing the demographics of these sub populations and the distribution of CFR by age to predict deaths in each setting in a similarly more accurate way.
I’d imagine this will have been fit out of years of research looking at antibody studies and the densities brought about by different pathogens in different settings. Getting that data and replicating would I’d imagine take years.
Add to that he can probably calabrate any r0 reading given to him based on the demographics of the sample, so he can probably even make sure he corectly interprets any r0 average given to him for a sample…
As a coder and quant I’m developing increasing respect for the complexity of the work he has done, shame his PR sucks.
Ferguson loving aside…
Given we know the de Gaulle has seen 59% infection, we already have evidence that populations can see really high infection rates, so we would be wise to not dismiss it can happen.
Like you I’m still not 100% happy to follow the models though, as we are all saying there may just be fewer people left to infect.
BUT, SteveB assumption is not strong enough for me. We simply don’t have scientific proof infections are happening without antibody response. Even more confusingly, even if there was evidence we’d need further evidence that as Caswell says viral load was not a factor. It seems entirely plausible to me that a second higher viral exposure might lead to death or an antibody response, when the initial one did not. A doctor I spoke certainly seemed to think this was conventional thinking for other viruses like flu.
I don’t understand why this doubt over the measure of infection is not seeing more open discussion amongst the likes of the CMO and CSA, it would certainly help all of us develop more faith in their response…
… any way Toby can get to ask this question at the daily breifing?
“We simply don’t have scientific proof infections are happening without antibody response”
I rather suspect we won’t get that proof (if ever) until long after the epidemic has passed, difficult to prove a negative etc. although we do seem to know 95% for sure that younger people don’t generally get ill and aren’t very contagious. What we might get is a much better idea which model variables fit the pattern.
My non-expert meta analysis of sero studies to date throws up lots of data suggestive of a significant proportion of prior immunity (either with or without antibodies) and very little data suggestive of anything approaching 100% susceptibility. This also fits with what we know about pretty much every other virus; even with HIV there’s a natural immunity of c. 10%.
E.g. in the worst hit towns in Bergamo, approx 61% found to have antibodies but the area was absolutely overrun and the natural herd immunity threshold would have been overshot by a mile; it’s far from inconceivable that everyone who was susceptible was affected. Similar on the Charles de Gaulle, and in the hotspots in the US.
In other areas, a seroprevalance of c. 15% seems to lead to a plateau and 30% to a decline. All seems to fit with a level of natural immunity of somewhere around 30%.
Also see this article “Tests aimed at determining whether Britons have recovered from coronavirus may not be useful because younger people do not produce sufficient quantities of antibodies to the virus, early research suggests.”:
https://www.telegraph.co.uk/news/2020/04/15/uk-coronavirus-antibody-test-validated-results-show-under-40s/
I believe we can already calculate the IFR for healthy under 65s. And we can do so with a high degree of confidence, using evidence already published and with no need for further widespread antibody testing.
If I’m right – and I’d love people’s opinions on this – then the argument that this cohort – which also happens to be the economic engine room of any nation – should, with a few sensible precautions, be set free to go back to work and normal life, becomes powerful.
My approach is neither revolutionary nor particularly insightful, I just decided that rather than having to know the IFR for the general population I would simply choose one of the more pessimistic IFR estimates – the 0.9% IFR assumption of the Imperial college group. From there it is very straight-forward to calculate the IFR for under 65s.
Using data produced by Dr Ioniddis’ Stanford team on death’s of healthy under 65 in Netherlands, Italy and New York, and also the actual number of deaths in the UK of people with Covid-19 (itself an inflated representation of deaths from Covid-19) we can extrapolate the following:
As of April 23rd the number of deaths associated with C-19 in the UK stands at 18,738.
Assuming an IFR of 0.9% that means there must have been 2.08m infections in the UK to cause that many deaths. A quick check tells us that assumes only 3% of the population has already been infected, so no one can accuse us of putting a positive spin here.
Ioniddis calculates 0.3%, 0.7%, and 1.8% of deaths in the Netherlands, Italy and New York occurred in under 65s with no known pre-existing health conditions. I assume that this is an uncontroversial figure as it must simply be a count of deaths cross checked against age and medical records. Unfortunately, I don’t have the numbers of participants in each study, but let’s take a rough average of those figures and say 1% of healthy under 65s on average go onto to die from C-19 after they have contracted it.
1% of 18,738 is 187. This is the estimated number of healthy under 65s to have died in the UK from Covid-19
To calculate the IFR for healthy under 65s all we now need to do is place the 187 deaths against the 2.08m estimated infections, and we realise that the IFR for healthy under 65s is probably only around 0.009%.
It is tempting at this point to compare that to the IFR for flu which is estimated at around 0.1%, however we should be cautious here – if we took a similar subset of healthy flu sufferers we would expect also to see a figure lower than that for the general population.
In our case, anyway, we only want to ascertain whether it’s safe for healthy 65s to begin to go about their lives at something closer to normal. So let’s look at that:
Under 65s make up around 3/4 of the UK population, so that’s approximately 52m. I don’t know how to extract the healthy from the unhealthy but it doesn’t really matter. If we assume no more than 60% of them are likely to become infected (herd immunity should stop the spread after that), then we can now calculate the number of deaths to expect in healthy under 65s in such a scenario and the figure is 2,329.
But let’s not stop there. Let us take a ridiculously pessimistic IFR estimate for the general population – one we pluck from the ether – of 5%.
We repeat all of the above steps and we find that there are only 13,000 deaths amongst healthy under 65s. 13,000 more deaths than anyone would like, to be sure, but in a trade off against deaths caused by the lockdown itself, it is a no brainer.
The conclusion we should all draw from this (assuming I haven’t made some heinous error in my calcs; and I very well might of – please let me know), is that we need to move from dumb to smart social distancing as soon as possible. We don’t need to wait for widespread anti-body testing either. We just need an accurate tally of those who died under 65 with no underlying health conditions.
Please let me know what you think.
One factor I have not seen discussed anywhere is the effect of people being told there is a deadly virus circulating among us. Particularly for older people, who tend to be more likely to believe what the established public authorities tell them, relative to other demographic sub-groups, and equally more physically and mentally fragile, i.e. more likely to be strongly affected by such effects.
In the Chernobyl melt-down, for example, the fatality predictions ranged from thousands (immediate effects) to hundreds of thousands to millions of secondary deaths (CNN 1995: 3.5 million), whereas the actual deaths caused by that event was something like 56 (different sources make different claims – it’s not possible to be absolutely certain), and over the course of succeeding decades several thousands have died from the secondary effects of the event, e.g. the pollution of the local water and food supplies, and etc.
Whatever the actual numbers, what is reasonably well established is that:
1. the actual numbers are several orders of magnitude below the direst predictions. (Sound familiar?)
2. a significant proportion of the dead ended up that way because they had been told by public authorities that they were going to die. (This is more disputed, naturally.)
As I stated at the start of this comment, I have not seen any consideration of this effect in re corona virus. In the Chernobyl example, this effect has been claimed by some sources to be as high as 50%. If it is anywhere near that, certain parties have an enormous amount of blood on their hands.
Quoting from Michael Crighton’s speech on Fear, Complexity, and environmental Management in the 21st Century:
“… according to the UN report in 2005, is that “the largest public health problem created by the accident” is the “damaging psychological impact [due] to a lack of accurate information…[manifesting] as negative self-assessments of health, belief in a shortened life expectancy, lack of initiative, and dependency on assistance from the state.” … ”
Bad information killed a lot of those people. We’re getting a lot of bad information about corona virus but nobody is talking about this. Historical errors ought to stimulate both humility and caution. What are apparent in the public discourse on corona virus are the opposites.
Is this of interest?
“Memory T cells undergo different changes and play different roles in different life stages for humans. At birth and early childhood, T cells in the peripheral blood are mainly naive T cells. Through frequent antigen exposure, the population of memory T cells accumulates. This is the memory generation stage, which lasts from birth to about 20-25 years old, when our immune system encounter the greatest number of new antigen. During the memory homeostasis stage that comes next, the number of memory T cells plateaus and is stabilized by homeostatic maintenance. At this stage, the immune response shifts more towards maintaining homeostasis since few new antigens are encountered. Tumor surveillance also becomes important at this stage. At later stages of life, at about 65-70 years of age, immunosenescence stage comes, in which stage immune dysregulation, decline in T cell functionality and increased susceptibility to pathogens are observed…
…Currently, the mechanism behind memory T cell maintenance is not fully understood….”
https://en.wikipedia.org/wiki/Memory_T_cell
If I could bold the text, I’d be highlighting the bits that talk about three distinct stages of life: 0-25 years, 25-70, post-70.
I’m very ignorant of this stuff, but as I understand it, T-cells are the other side of immunity from antibodies..? But like antibodies, there is some sort of learned memory it seems. It has been observed that young people may not produce much in the way of antibodies to SARS-Cov-2. And Sarah Gilbert on the BBC observed that humans in general don’t produce a very strong, long-lasting antibody response to SARS-COV-2.
It’s my vague suggestion that maybe people in general are developing a degree of immunity to Covid-19 without necessarily developing antibodies (maybe it’s dependent on degree of exposure).
As I saw elsewhere on Lockdown Sceptics, there was a link to this study:
“Antibodies are not required for immunity against some viruses”
https://www.sciencedaily.com/releases/2012/03/120301143426.htm
The upshot is that the models don’t get anywhere near incorporating any of this stuff (they couldn’t be definite about it even if they did), but this kind of thing might be the difference between the model being about right, and it being disastrously wrong.
If, based on the value ‘R0’ that people seem to be assuming, we are holding out to see >60% of the population showing antibodies in order to determine ‘herd immunity’ then we may be disappointed.
I saw in the new interview with Michael Levitt that he prefers to use the word “saturation” rather than ‘herd immunity’ – indicating that the virus has thoroughly soaked into the population but not necessarily resulted in an easily-measurable ‘immune’ state.
Someone has finally done a model that includes susceptibility – and it confirms yours. From the Speccie:
Research: Covid-19 vulnerabilities
How many people would have to fight off Covid-19 to achieve collective (or ‘herd’) immunity? So far, we have heard that it’s around 60 per cent. But this assumes a uniform population with everyone mingling equally and being equally at risk of catching the virus. A new study https://www.medrxiv.org/content/10.1101/2020.04.27.20081893v1, led by academics at the Liverpool School of Tropical Medicine, adjusts for the fact that they don’t and they aren’t. The virus, they argue, could quickly infect the more susceptible part of the population – which will then become immune and stop spreading the disease. As Matt Ridley puts it, ‘if the virus runs out of highly-susceptible segments of the population (elderly, hospital settings etc), it may struggle to keep going in the rest of the population’. Adjusting for this, says the study, and the threshold for Covid-19 herd immunity falls to between 10 per cent and 20 per cent.
Hi Will. I just saw it in the Spectator and was about to post it here!
So it seems quite lot of people are now arguing that lockdowns are unnecessary but voluntary social distancing is what does the trick eg https://www.thepublicdiscourse.com/2020/04/62837/ https://arxiv.org/abs/2004.10324.
I looked at London and New York to test this. London deaths peaked on 4 April and began to plateau on 27 March. Assuming average 16 days from infection to death (I used to say 21 but I now understand the latest data suggests 16) that puts peak infection on 19 March and the beginning of plateau on 11 March. The thing is, that matches pretty well when social distancing began. Almost exactly in fact – see graph here https://dailysceptic.org/2020/05/02/.
New York deaths peaked on 6 April and hospitalisations peaked on 1 April. Hospitalisations began to plateau on 26 March so we infer infections began to plateau 11 days prior on 15 March. Again, that corresponds almost exactly to the beginning of social distancing – see the NYC graph here https://www.thepublicdiscourse.com/2020/04/62837/.
I had hoped this exercise would show that the peaking and plateauing of infections preceded social distancing and hence was due to some sort of saturation point/herd immunity. But it ended up strengthening the case for social distancing, leaving open the widely-believed possibility that once social distancing stops the epidemic will resume (the dreaded second wave).
The difficulty is that everyone has done social distancing, including Sweden, so how can this theory be tested? One thought is that if we look at other English regions, Manchester’s social distancing follows London’s almost exactly, but nonetheless its deaths begin to plateau on 3 April which means its infections begin to plateau on 18 March, which is well after social distancing began. However, it (or rather the north west region) then has a long plateau with the peak not occurring until 16 April. Does this mean the effect of social distancing when done earlier in the outbreak was to flatten the curve/stretch the peak? More generally does it show that it wasn’t the social distancing that stopped the epidemic in London as it didn’t have the same effect in Manchester?
Be grateful for any thoughts on this or if there is any other data that might show where epidemics have plateaued and peaked independent of social distancing.
A new interview with Hendrik Streeck that’s very relevant to what we’re talking about here.
https://www.youtube.com/watch?v=vrL9QKGQrWk
It’s all good, but very interesting from my point of view at about 5.15 and several other places where he’s talking about the possibility that lower exposure (and lower viral load) is likely to produce fewer symptoms and lead to partial immunity/resistance, possibly being based on T-cell response.
Did you see that the final results for NYC found 19.9% had antibodies? Within the Liverpool model range of 10-20%. https://www.governor.ny.gov/news/amid-ongoing-covid-19-pandemic-governor-cuomo-announces-results-completed-antibody-testing
Have you had the chance look at the spreadsheet that someone made of the surveys done so far? https://docs.google.com/spreadsheets/d/1zC3kW1sMu0sjnT_vP1sh4zL0tF6fIHbA6fcG5RQdqSc/edit#gid=0
Interesting that most are within the Liverpool model range, except for a few that are of smaller communities such as prisons or schools that had a bit (or a lot) higher. Four that stood out to me were number 9 in Iran (33%), but that turned out to use households rather than individuals so that would inflate it (they’ll catch it off each other), number 11 in Italy (67%) but that seemed to be an estimate based on assumptions rather than an actual survey, number 44 also in Italy (70%), which I’m not sure about as I can’t read the article but seemed odd and to do with blood donors (were the tests accurate?), and number 28 in Chelsea in Boston (33%) which does seem to show a badly affected neighbourhood with a higher proportion infected.
It doesn’t have the surveys from boats so it’s not complete.
What are your thoughts?
I’m puzzled.
Some here are suggesting that a large proportion of the population have been quietly infected without adverse effects.
Others are proposing an ‘infection ceiling’ which blocks further spread … a sort of super herd immunity.
I have seen serological based immunity estimates for various countries ranging from 1% – 15%. The top values were for mega hot spots : one in Germany and NYC. Certainly nothing like 30% or more immunity has been reported as far as I have seen.
As for the idea of a magic infection ceiling, this site is the only place where I have seen this discussed.
I suspect that a lot of wishful thinking is going on.
Actually there’s been a recent paper from Liverpool which deals with exactly the sort of questions we have here. Populations are not homogenous, diseases do not spread through everyone equally. There may be a number of reasons for this, prior immunity, genetic immunity – those we just don’t have any data on. But social behaviour is a big part of this. Liverpool basically argue that we only need 10-20% infection because ‘superspreaders’ and the vulnerable – in care homes and hospitals where it’s hard to prevent spread – get it first. After they have got it, the effective reproduction rate drops much faster.
My understanding, such as it is, is that most people just have sufficient infection resistance to fight off covid 19 before ever it gets established. Even the black death only killed half the population I think. I can’t imagine that half of them were somehow never exposed to it.
Yeah it’s all speculation by the ill-informed. We don’t know how many ppl have had it, because we can’t know: current numbers are based on hospital admissions, then hospital staff, then care home data. We don’t know how many ppl have antibodies, because the tests are still being refined. We don’t know what proportion of ppl have immunity. We don’t know whether antibodies, if they can be detected, confer immunity. We don’t know whether asymptomatic infection leads to immunity. My contribution to the speculation: 5% of ppl have had it and most will be immune. 95% of ppl still to have it.
It’s a stretch to argue that this virus will evolve to lesser virulence on Darwinian grounds, a) because most infections are minimally symptomatic, b) because even for more serious infections it’s not the virus that kills you it’s the day 8 immune response, and by then you have passed the infection to 3 or 4 others. It’s not like flu, or plague.
My guess is any release of lockdown will lead to resurgent cases – duh – we just need to decide as a society whether we want to succumb to the consequences of lockdown (economic meltdown, indirect health hits etc) or to the consequences of covid (lots of at-risk people dying, a small number of not-obviously-at-risk people dying, ICUs overwhelmed). I’d go the latter.
One of the questions that everyone should be asking is how effective is the lockdown? I guess it is hard to answer as we don’t yet really know quite how fast covid 19 would have spread if the lockdown had not happened. But we do know how fast normal flu viruses spread from many years of experience. If I am right in thinking that the transmission method of covid 19 is similar to ordinary flu viruses could we not check the success of the lockdown by following the incidence of ‘flu’? It looks almost unchanged to me on the ONS page, although the exact numbers are hard to seperate out from the data we are given. I have looked quite hard on the internet and I havn’t found any comment on this yet, has anyone else?
We really do now have enough clues to estimate the number of people infected in UK.
I could go on. But surely now all the clues and evidence point to probably most of us having already been exposed (whether or not infectable), and many having been infected to the degree that the virus can be detected. Add in the fact that typically 1.5 tests need to be done to detect an infection, then we are way past the peak of this disease – even if we end the lockdown now.
And then one has to ask – why is the government not releasing the results of the >30k serology tests they have already done? If they feel they are unreliable, then release the data and explain the uncertainties to us at the same time. Hiding the key data that relate the number of people ever-infected, given its relevance to the lockdown question, seems suspicious to say the least.
Do you think that when Prof Sunetra Gupta makes assumptions, considering that she has published over 100 papers since 1994, that the difference between her assumptions and those of the less experienced members of the epidemiological academitribe, would be a bit like the difference between the assumptions I make with 35 years driving experience, and those of a learner driver
Errors hopefully become less over time, as the assumptions become more useful and applicable
https://www.zoo.ox.ac.uk/people/professor-sunetra-gupta
I don’t understand why Ferguson would be invited to be a member of SAGE and not her. Having watched one of her lectures on Youtube I think she has the exactly the right insights for the job.
Someone has confirmed the Liverpool study:
https://judithcurry.com/2020/05/10/why-herd-immunity-to-covid-19-is-reached-much-earlier-than-thought
“Incorporating, in a reasonable manner, inhomogeneity in susceptibility and infectivity in a standard SEIR epidemiological model, rather than assuming a homogeneous population, causes a very major reduction in the herd immunity threshold, and also in the ultimate infection level if the epidemic thereafter follows an unconstrained path. Therefore, the number of fatalities involved in achieving herd immunity is much lower than it would otherwise be.”
Of course, when I say “confirmed”, I don’t mean that they have confirmed that real people’s susceptibility or infectiousness are more variable than previously thought. Nor that the biological mechanisms involved have been identified. All that has been established is that modelled variability changes the model’s dynamics.
But I think this is showing modelling at its best. Where detailed biological mechanisms are almost impossible to examine, measure and quantify quickly (not least because a virus and people are moving targets), all we have are snippets of population-level information. Here, the model is modified in a perfectly plausible manner and shown to match those snippets of information. The result is counterintuitive and would not have been apparent without running the model. The model has served its purpose of testing out a hypothesis using bits and bytes instead of real biology. It strongly suggests a possible mechanism for explaining results that have been found in the real world.
It would also offer the government a way of justifying easing the lockdown that is at least as persuasive as the original Imperial doomsday model. The Imperial model could be modified to reflect these changes and run again. Unless Ferguson could justify his original model more strongly than the modified version, the government would be armed with genuine new information that would give them plausible reasons for easing off the lockdown without losing face. Even Ferguson could come out of it OK.
Yes, I believe the research showing about one third of humans are immune to C19 (due to T cell immunity from prior corona virus infections) and the models are very important. Especially when combined with the fact that the IFR of C19 is only about 0.2%.
This is because this scientific evidence and the modelling together confirm that herd immunity can be achieved with a low percentage of infected and with low, flu level, mortality. Here in New Zealand, I am expecting a fresh outbreak this winter or next as we have hermetically sealed our border and it is now summer. This bug does not like UV.
The evidence and models are clear. We could have left our border wide open, like Belarus, and had about 60 deaths with a quick eight week outbreak, as Wittkowski recommends.
Instead, we have had 20 deaths now, and will probably have 40 deaths in the future. We have thus unnecessarily destroyed our economy. Saint Jacinta, our PM, is not going to open our tourism based economy for months.
The bug, I believe, will return when more susceptibles are “created” ie the very old with co-morbidities. The lockdowns do not, I think reduce the death toll much as they increase “prolonged close exposure” and do not affect care homes. I think the enforced social distancing may (temporarily) save some 75 year old’s lives. This then increases the chance of a second wave. The 76 year olds will be reaped next year…
Lockdowns just prolong the inevitable and destroy the economy and freedom. The lunatics are in charge of the asylum…
Re today’s announcement that 0.27% of UK were carrying the virus in last weeks or so:
* This survey only tested people from families who have previously volunteered to be part of ONS research. So probably not the poorer or less well educated members of society. And it excluded all health care families, and all key worker families. I.e., those tested were not typical individuals, but the kind of people who would be able and likely to keep themselves very protected throughout this lockdown.
* The test is not highly sensitive (i.e., misses many affected) and samples were taken at home by test subjects without expert help, so could easily have been done badly (missed virus carry material).
* Indeed, the government openly admit the 0.27% is ‘test positives’ not infection prevalence, because of the above reasons.
* Given the above, I’m going to assume the more general recent infection rate is about 1%
* Subsequent to lockdown starting (23 March) and R0 falling to 0.5-0.9 (governments numbers) the percent of the population would fall daily. Assuming 2 week or so is needed to clear the infection, then the infection prevalence would fall by as much as 50% every 2 weeks. So the peak of prevalence must have been about 10% around end March.
* Assuming exponential increase up to that peak, and exponential fall since, the sum of all people ever infected comes out to be about 1/3 of the population. – consistent with lots of other clues I summarised in an earlier post
* As others have noted in this chat forum, given realistic structures in society and a degree of natural disease resistance, then herd immunity can be reached when as few as 15% have had the virus. So we may well already have passed that point of safety!
* The idea that herd immunity is achieved after a month or two of disease spread is fully supported by the fact that ALL countries/situations (whatever their approach to lockdown) see the infection prevalence start falling after 1-2 months of infection growth.
SO PLEASE BORIS… END LOCKDOWN NOW. SHIELD THE VULNERABLE. SAVE THE ECONOMY AS WELL AS THE 100K-200K LIVES THIS LOCKDOWN WILL OTHERWISE COST
Even more evidence…
Infection prevalence is now LOWER in London that elsewhere in the UK, even though it started off FAR higher in London when lockdown began and we’ve all been under the same lockdown regime.
The ONLY way to explain this is that HERD IMMUNITY WAS ACHIEVED IN LONDON SOME TIME AGO (a few weeks ahead of other places) and so the virus is naturally fading away there – and almost has disappeared. Elsewhere, the level of infection needed to rise some more during the early lockdown period before herd immunity was achieved. It may or may not quite have been achieved elsewhere, but probably has been by now given that infection prevalence is now falling everywhere.
Yes, great second to last paragraph. In Wuhan the peak was Feb 12 – two weeks before their lockdown. IMHO we need legislation to forbid lockdowns as in Japan.
Something I’d like to run past you.
_____________________________________________________________
“The R0 for novel influenza A (H1N1) has recently been estimated to be between 1.4 and 1.6”
https://pubmed.ncbi.nlm.nih.gov/19545404/
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“The novel 2009 H1N1 influenza strain has been found to have… an R0 of 1.3….
Seroprevalences against pandemic 2009 H1N1 influenza varied by age group, with children age 10-19 years having the highest seroprevalence (45%), and persons age 70-79 years having the lowest (5%). The baseline seroprevalence among control samples from 18-24 year-olds was 6%. Overall seroprevalence against pandemic H1N1 across all age groups was approximately 21%.”
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2828126/
_____________________________________________________________
“one may predict the final size of epidemic, z, the proportion of those who will experience infection by the end of epidemic, by using the final size equation (based on a homogeneous mixing model),
z = 1 – exp(-zR0)
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2821365/
(Neil Ferguson’s model estimated 81% for Covid-19 at R0 of 2.4 (I think). The formula gives 88%.
For an R0 of 1.5, the formula says that 59% of the population should be infected and showing antibodies.
For an R0 of 1.3 it is 42%).
_____________________________________________________________
MY POINTS AND QUESTIONS
So for H1N1, the above study found that the actual measured seroprevalence averaged at 21%, which is half of what the standard final size formula would indicate for an R0 of 1.3 (if I’m understanding the formula correctly!). However, for this particular virus in children, it was just about spot on at 45%. This was an epidemic that had ‘run its course’, and no artificial changes of behaviour (social distancing, lockdown) had been imposed.
The paper goes on to discuss how resistance to previous viruses may produce immunity and/or resistance to the latest strain, and this is why older people aren’t producing the H1N1 antibodies – they have been exposed to more in the past, including the 1918 variant.
In the case of Covid-19, it’s the other way round, with, it seems, younger people not producing antibodies when infected.
Either way, this is reinforcing our previous suspicions that seroprevalence is not a reliable measure of ‘who has had the disease’ (maybe it is in one sense, but not in the sense of who has been exposed and, one way or another, fought it off). So if we find that only 5% of a population has antibodies, it doesn’t mean that the epidemic hasn’t run its course nor that 95% of the population are just ripe for the picking.
So do we have a breakdown by age for the latest Spain seroprevalence study? I can’t seem to find it.
I do think that the significance of this is that it’s an actual example where the seroprevalence results don’t match up to the estimated R0. It happened with H1N1; it could happen with SARS-Cov-2. Why are people so sure that we are not at herd immunity at, say, 15% in some populations? Possibly even 5%.
And if the actual results don’t match the R0 figure, doesn’t that tell us that R0 is wrong? That is, it’s one thing to try to estimate R0 from the start of the epidemic where all sorts of issues may distort it (including viral load), but you would think that after the epidemic has run its course without any artificial interventions, you can be much more sure of your R0 figure for your real population and its real circumstances. In the H1N1 case, an R0 of 1.12 would fit the average seroprevalence result.
In the case of Covid-19, unfortunately we will never be able to do that because of the claim that imposing social distancing and lockdown half way influenced the shape of the epidemic (whether it did or not). But the question remains: why are we so sure of our R0 of 2.4? And what does such a figure mean if some people have innate immunity, and then not everyone produces antibodies when infected? I suggest that it probably needs adjusting downwards. And in doing so, that takes the herd immunity threshold down with it. However, a low R0 suggests a disease that ‘doesn’t spread very much’, whereas this may be misleading. I suggest that Covid-19 spreads very nicely; it’s just that most people are resistant to it when exposed to it in low doses.
R0 cannot be employed so simplistically. The ‘R’ number of people on average that an infected person infects changes dramatically over time and in different situations. E.g., for the same virus this number could 0.5 in the general population but 10 in a prison population. Also, as more people become infected there are less people to infect, and so this number decreases from above one and goes below one. If people change their behavior, this too affects the number, as will things like demographics, average health status, degree of inherent immunity in a population. A typical virus will also mutate with time to become more infectious but less sever, again affecting the R number.
R0 (note the 0) is the R number when the virus first starts spreading. For SARS-CoV-2 R0 was about 3 or 4. The current R number has now fallen to something way below 1 – because it has by now infected many/most (who are hence immune), because there is a significant degree of inherent immunity in populations (e.g., infected prisoners at Marion Correctional Facility have 98% zero symptoms), because we are now socially distancing etc, and therefore, overall, because herd immunity has been achieved (infection prevalence has fallen everywhere regardless of lockdown or no lockdown, and continues to fall even as lockdowns are lifted).
All the above would be verified if the government would release their antibody test results – which they has so far failed to do for some ‘strange’ reason. Next week they will release some such data showing 17% sero-positivity in London …amongst blood donors!!! (who by definition must have had no recent illness!!!), so you can be damn sure the true figure is far higher. Plus we must remember that (a) many people fight off the virus using their T-lymphocytes rather than their B-lymphocytes (which produce antibodies), and (b) there is significant inherent immunity due to past exposure to the common cold – and neither of these immune groups would have detectable SARS-CoV-2 antibodies, So the percentage of individuals now immune must be >>>50% and probably closer to 100% than 50%.
HERD IMMUNITY HAS BEEN ACHIEVED, AND THE GOVERNMENT AND THE POPULATION ARE NOW SCARED TO LOSE THEIR FEAR OF THIS VIRUS.
Re-enforcing the comment made above re downward bias of running serological tests in blood donors, this report (https://www.medrxiv.org/content/10.1101/2020.05.10.20097451v1 – download the pdf) shows that for the same region of France and using the same antibody test, the blood donor individuals demonstrated 10-fold lower sero-positivity that the general population.
In many situations in different countries, many tens of percent of the general population people are sero-positive. Then one must add those that fought the virus via T-cell rather than by creating many antibodies, plus there are those that have inherent immunity.
So herd immunity must now be working to largely eradicate this virus. Ignoring this blatant evidence whilst ruining lives and economies by maintaining draconian lockdowns (with the hundreds of thousands of excess deaths that will cause) is surely immoral unjustifiable if truly “being led by science”.
This is rather dated now. In fact, it was dated when it was published a month ago.
Look, this is a trans-species virus. We would not expect it to be well adapted to a human host as the flu virus is, for example. We would expect that small changes in cell proteins due to genetic variations in humans would make a big difference in whether the virus can actually lock onto and infect those cells or not. With that in mind, we can see that the big mistake in the Imperial College model was to assume everybody was susceptible to the disease, whereas in fact that is not the case.
Models need an additional silo of people who will never be infected simply because the virus cannot infect them. A number of studies on ‘petrie dish’ cases like the Diamond Princess and the USS Theodore Roosevelt (where there was substantial testing done and where respectively eight died and one died in a population of around 3500 who where exposed to the disease) and others show that about 80% of us will never get infected because we are simply not susceptible.
In consequence, the famous “R zero” number defining the likelihood of passing the disease on is actually about 0.43. This means the disease will burn itself out. There will be no rebound, there will be no spike, there is no need for a vaccine….. there never was a case for a lockdown.
I disagree on a few details: (1) Original ‘R’, i.e., R0, was greater than 1 or the disease would not have spread as it did, and it was about 3-4 whereas ‘R’ has now fallen to <1; and (2) lab evidence indicates this virus infects human cells more efficiently than any other species, which is good evidence that it was selected for in a lab somewhere, whether or not it was manipulated by genetic engineering.
But the general thrust of your argument is certainly correct, that – for genetic diversity, antibody cross-reactivity, and other reasons – many people will not be infectable and/or will clear the virus almost immediately via T-lymphocytes without generating detectable antibodies. This is not theory, a wide range of evidence now exist to prove all this. Hence as you say – the disease will burn itself out, there will be no rebound, there will be no second spike, there is no need for a vaccine….. there never was a case for [such a draconian] lockdown.
Today (28 May), as I warned last week, the government announced that “Of those individuals providing blood samples, 6.78% tested positive for antibodies to COVID-19“
[I was actually told this number was going to be nearer 4%, but no matter]
What they DID NOT make clear, is that this finding relates ONLY to BLOOD DONORS. To quote ONS…
“Public Health England also publish an estimate of the prevalence of antibodies in the blood in England using blood samples from HEALTHY ADULT BLOOD DONORS.”
https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/bulletins/coronaviruscovid19infectionsurveypilot/28may2020
In France, they previously ran serology tests on healthy and on people going to the supermarket. The percentages were 3% and 30% respectively
So this 6.78% figure is a complete curve ball, intended to mislead. Extrapolating from the French data, the real fraction of UK population likely to have been infected by now is >>50%
I’m only aware of one study of archived U.S. blood samples (collected by the Red Cross over 4 days in mid-December 2020 and then six other states in mid-January 2021). This seroprevalence study found that abut 1.47 of the 7,000 or so units of blood tested “showed signs” of previous COVID infection. The blood collected in mid-December from just California, Oregon and Washington showed that 2 percent of the archived blood vials showed signs of likely previous infection. Well, extrapolating to the USA, this would mean that somewhere between 4.5 and 6 million Americans had probably already been exposed to the virus by mid-January. Of course the CDC study said that results “may not” be “generalizable” to the population. But “may not” also means “may be.” I don’t know why the results wouldn’t be generalizable to the population at large as blood donors would seem to be a good, representative sample. This study probably UNDERESTIMATES prevalence as blood donors tend to be much older … and those most likely to contract the virus would be younger.
Anyway, this “Red Cross” blood study – and the inferences which can be drawn from same – didn’t get nearly enough attention. Also, it’s strange that other studies – from other points in time and other regions of the country – have yet to be done. If such studies had been done we’d have more evidence of “early spread” and how prevalent the virus was at different points in time. For example, if the CDC tested stored blood from, say, Dec. 1st 2019 what would that study have found? What about more tests in more states from, say, January 25th 2020?
I personally think the reason such studies have NOT been commissioned is that authorities and scientists know what these studies would reveal.
I have a contact in one of the closed down antibody testing labs who says they have several 1000 results sent in by people who bought kits online and 14% are positive. The population might be a bit biased but it seems surprisingly high.
…and even the CDC admits those tests are wrong half the time! So double that 14%
https://www.zerohedge.com/health/cdc-admits-covid-19-antibody-tests-are-wrong-half-time-virus-isnt-deadly
Professor Karl Friston, ranked by Science magazine as the most influential statistician in the world says: (a) social distancing rules are unnecessary; and (b) “the true portion of people who are not even susceptible to Covid-19 may be as high as 80%” https://unherd.com/2020/06/karl-friston-up-to-80-not-even-susceptible-to-covid-19/
Science magazine reported two studies that “found some people never infected with SARS-CoV-2 have these [T cells], most likely because they were previously infected with other coronaviruses.” https://www.sciencemag.org/news/2020/05/t-cells-found-covid-19-patients-bode-well-long-term-immunity
“Importantly, we detected SARS-CoV-2-reactive CD4+ T cells in ∼40%–60% of unexposed individuals, suggesting cross-reactive T cell recognition between circulating “common cold” coronaviruses and SARS-CoV-2.”
https://www.cell.com/cell/fulltext/S0092-8674(20)30610-3
Science Magazine (11 Nov 2016) ranked Prof. Friston as the most influential neuroscientist, not statistician. More to the point, his model postulated an unknown and unknowable form of protection. It may be true that 80% of people in the UK are not susceptible, but how would we know? That 80% figure certainly cannot have been true in New York City, where the death rate has reached 0.2% of population, and 0.6% in one zip code, unless the IFR were at least 3%.
We are living through Covid Hysteria. I lived through Hong Kong flu in 1968 – killed 80,000 in UK, no lockdown – and checked with contemporaries that no one remembers any ‘lockdown’ then. The big difference now with Covid is that politicians have got involved, hence the media too, and none have any grasp of evidence-based thinking, so act only to spread panic and make ruinous decisons.
Research has moved on, as has testing. We now know that about 60 to 75% of cases are broadly asymptomatic. These people can get infected and test positive for a while; but don’t get ill and such antibodies they have don’t seem to last that long, except possibly with repeated exposure. There is no good reason however to doubt that future exposure will have the same nil effect. Nor is there any sign that such asymptomatics infect others to any significant degree. So then? Antibody tests suggest 7 % actually getting ill enough to get them. 4 to 5 million then of which perhaps 10 % needed intense medical intervention and 30 to 45,000 died. How many more to herd immunity? If that is 80%, then the asymptomatics have to count to that total whether they have been infected or not. So we aren’t quite there yet but still close enough for this to be the aim of Government policy with a vaccine a suitable back up for the most vulnerable.
We have a massive government department (Office for national statistics) crawling all over data and telling us daily how many people are being tested positive, deaths, etc.
) reason don’t want to give us this figure….
They apparently test 20, 000 people a week to see how many people really have it, not just those going for tests. However, can anyone please find an up to date figure for how many people have actually had the disease??
If half the population have had it, then how bad can it now get? There would then be no need for any of this control over the population?? For some (strange
Btw… only 7% of people going into hospital with this disease are now not surviving (how poorly do we think this 7% are already?)
This won’t stop until we say NO!
Here’s an epidemiological study that hasn’t been done that should be done: Simply survey all of the people who think they may have have had COVID before, say, March 1st 2020. What percentage of this group did NOT subsequently “test positive” for COVID? If this percentage is significantly higher than the population at large this would strongly suggest these people DID have COVID (before it was said to be spreading) and had developed some kind of “natural immunity.”
BTW, as others have pointed out, the population that thinks they may have had COVID undoubtedly numbers in the MILLIONS. Comment sections of articles are full of people who report they were sick with “signature” COVID symptoms and, went to a doctor, and, significantly, tested “negative” for influenza.
I’ve done my own amateur study in my hometown (via personal inquires and Facebook solicitations) and quickly found about 50 to 75 people who think they may have had COVID before the virus was supposed to even be in America. I’ve actually followed up with about 40 of these people. Only one or two later came down with (confirmed) cases of COVID. I’m not saying everyone who thinks they had COVID did have COVID (the majority probably/maybe didn’t). But I think a fair percentage of these individuals likely did. Of the group whose cases did seem to most resemble COVID, none subsequently tested positive for COVID – despite many of these people having very close contacts to people who did develop the disease.
Again, I think it would be interesting to see if some real scientists conducted a study of the people who strongly believe they had COVID. I believe the results of such a “real” study would support my thesis that this virus WAS spreading widely in the world well before March 1, 2020.
No worse than flu yet Bozo and his globalist mates are deliberately treating it as if it was the Black Death to terrify the population. Nothing short of the death penalty for these monsters will do!
I was diagnosed with extremely low vitamin D levels on December 19, 2019. I was tired and aching all the time and I’d been travelling 200 miles up and down to London for a month. I picked up a six month course of prescription vitamins from the chemist that afternoon. The morning of December 20, I woke up with a ferocious migraine and my 82 year old Dad was coughing his lungs out. That evening, I started coughing. I was ill in bed for three weeks. I couldn’t taste my Christmas dinner. I started shivering right afterwards and went to bed. I said at the time that it was the weirdest ‘flu’ I’d ever had.
In terms of after effects, I couldn’t watch TV for more than half an hour a day without getting a headache for the next nine months. I had a two day migraine every ten days for about nine months. I used to do HIIT workout videos before I’d go to work and would feel energised by them. I started doing them again in February 2020, only I had to sleep for two hours after the workout. I’ve only really shaken off the after effects of the virus in 2021.
My Dad never shook off the effects of his virus and spent the second week of lockdown in hospital with pneumonia. He didn’t test positive for COVID-19 by then, of course. There was no antibody test at the time and,18 months on, the antibodies wouldn’t show up anyway. Anecdotally, I know loads of people had a similar illness at the same time as me. The first person I know to have symptoms is a lady who came back from an international conference in November 2019. My view is that the virus was building up for months under cover of the seasonal flu outbreak and loads of us have had it.