Epidemiologist behind Imperial College coronavirus model revises model
dailywire.com
dailywire.com
"Correction: The original title of this article incorrectly suggested that Neil Ferguson stated his initial model was wrong. The article has been revised to make clear that he provided a downgraded projection given the new data and current mitigation steps."
If you can show me I'm wrong, please do.
https://www.newscientist.com/article/2238578-uk-has-enough-i...
I'm not sure you read your own link correctly.
"New data from the rest of Europe suggests that the outbreak is running faster than expected, said Ferguson. As a result, epidemiologists have revised their estimate of the reproduction number (R0) of the virus. This measure of how many other people a carrier usually infects is now believed to be just over three, he said, up from 2.5. “That adds more evidence to support the more intensive social distancing measures,” he said."
Where I got confused was attributing some of the things said by Gupta (behind the Oxford model) to Ferguson (behind the Imperial College model).
I have my doubts that the numbers would support this claim. And if so, then virtually everyone in Spain or Italy would already be a carrier.
The fact that cases were linked to known arrivals also is evidence against this hypothesis : if a high proportion of carriers were unwitting and asymptomatic you would expect many of those diagnosed to not have a link to someone previously diagnosed.
However, the test you need to run is an antibody test, since negative tests don't tell you whether you've already had it.
And this is a major revision. It drops estimated deaths in the UK from 500,000 to "20,000 or far fewer." It also estimates that the UK will not run out of ICU beds in the process.
The reason is that the transmissibility estimate has gone up, which implies that many more people have already had the virus than we realized. This, in turn, means that a much lower percentage are serious cases. It also means that we are much nearer to the peak than we thought.
Edited to add: He also credits the lockdown in the UK, but if you look at the previous model of how this plays out even with a complete lockdown, you see that the vast majority of the change must come from the change in estimate of transmissibility.
That'd be wonderful. But, doesn't that conflict with what we saw actually happen in Italy?
Edit: I think it is important to keep in mind that Italy's deaths per day are still going up. The growth has slowed and hopefully we are about to see the peak but it still hasn't come. Most western countries are on the same growth rate as Italy, there is no reason right now to think we won't see something similar happen all over the world.
Date Cases New Cases
March 10 9172 1797
March 11 10149 977
March 12 12462 2313
March 13 15113 2651
March 14 17660 2547
March 15 21157 3497
March 16 24747 3590
March 17 27980 3233
March 18 31506 3526
March 19 35713 4207
March 20 41035 5322
March 21 47021 5986
March 22 53578 6557
March 23 59138 5560
March 24 63927 4789
March 25 69176 5249
March 26 74386 5210Hopefully fewer visited the UK and even fewer had close contact with the locals.
A rational person can't possibly believe that this is the cause of their troubles, even if there were such a thing.
Proof: https://www.westernjournal.com/italian-mayor-starts-hug-chin...
The data out of NY that makes me most suspicious of this new model is actually the 72% negative rate on tests. I would expect that to be a lot lower. But that's just a gut feeling.
Italy has never run out of ICU beds. There have been sporadic and garbled reports of temporary shortages in Lombardy specifically, alleviated by patient transfers and ward conversions, and some of those reports were contradictory (e.g. doctors or mayors saying they were rationing care but other more senior healthcare leaders saying they weren't).
The UK had a hospital that hit capacity temporarily but it only lasted 12 hours before transfers reduced the pressure again.
We'll be seeing a lot of activity like that in the next weeks - reports that hospitals are full, then they stop being full as more capacity is added or patients are rebalanced onto other hospitals. The assumption of total ICU exhaustion seems to have been based on the assumption of uniform case growth everywhere which isn't happening, and perhaps also an inability to quickly add capacity.
What about the deaths caused by the wrecked economy?
Even putting things in simplistic terms, the 2008 crash is credited with 10,000 suicides. Following the chain of misery into homelessness, stress-induced illnesses, criminality (which leads to loss of life in multiple categories), and so forth leads me to question whether it is actually as black and white as people say it is.
Currently, we're on track to make this economic catastrophe bigger than the 2008 crash. I don't want to imagine what the fallout is going to look like in the future, but I already know a lot of people who are out of work and scared.
Mind you, I'm saying this as someone who has family in the 70+ high-risk category. I'm not oblivious to the value of human life. I am purely speculating about whether or not we're actually doing the right thing, and thinking about the consequences as thoroughly as we should.
[...] coronavirus will probably kill under 20,000 people in the U.K. — more than 1/2 of whom would have died by the end of the year in any case [because] they were so old and sick [...]
The last thing this is about is the Dow.
It's an unpleasant reality, but it's a question of degree--how many lives are worth saving the world economy for everyone else? If your answer is "zero", then why should we not apply that standard to every other disease?
"New data from the rest of Europe suggests that the outbreak is running faster than expected, said Ferguson. As a result, epidemiologists have revised their estimate of the reproduction number (R0) of the virus. This measure of how many other people a carrier usually infects is now believed to be just over three, he said, up from 2.5."
[0]: https://www.newscientist.com/article/2238578-uk-has-enough-i...
citation please.
My understanding is that it's because of the policy intervention that his team advised.
"He said that expected increases in National Health Service capacity and ongoing restrictions to people’s movements make him “reasonably confident” the health service can cope when the predicted peak of the epidemic arrives in two or three weeks. UK deaths from the disease are now unlikely to exceed 20,000, he said, and could be much lower."
and
"This measure of how many other people a carrier usually infects is now believed to be just over three, he said, up from 2.5. “That adds more evidence to support the more intensive social distancing measures,” he said."
So the article says EXACTLY THE OPPOSITE OF WHAT YOU CLAIMED.
This is disinformation.
So basically "Scientist revises model based on new conditions". Isn't that supposed to happen?
A successful prevention is going to feel like failure. It's going to prompt questions like "was this worth all the panic, and tanking the economy?" Bodies are easy to count, deaths prevented are invisible.
1. https://www.newscientist.com/article/2238578-uk-has-enough-i...
In other words, if this new R0 estimate is correct we were completely mislead about how big a deal this virus is, and comparisons to it "just" being like a bad flu year are more or less correct.
"New data from the rest of Europe suggests that the outbreak is running faster than expected, said Ferguson. As a result, epidemiologists have revised their estimate of the reproduction number (R0) of the virus. This measure of how many other people a carrier usually infects is now believed to be just over three, he said, up from 2.5. “That adds more evidence to support the more intensive social distancing measures,” he said."
https://www.newscientist.com/article/2238578-uk-has-enough-i...
We've never had to build temporary hospitals to house 4,000 patients before, even in bad flu years. It's more infectious than flu and it hospitalises more people than flu. People keep talking about the death rate: there are other important things. How many people does it hospitalise? What happens to the people who can't get hospital treatment if the hospitals are full?
From places like Spain and Italy we know it puts a lot of people in hospital, and we know when that happens it starts shifting the mortality from the old people who were going to die anyway to younger people.
It's not like flu.
https://twitter.com/iamyourgasman/status/1241267189048578048
https://twitter.com/neil_ferguson/status/1243294815200124928
By adjusting those two parameters (R0 and IFR) in opposite directions, you can come up with a whole gamut of scenarios that match the evidence pretty well.
I'm just disgusted that the authors are now saying it was just an abstract demonstration of different scenarios, and pretending they didn't actually make the claims about the real world that they did.
1. Any situation where the testing isn't limited to just the most critically ill showing much lower infection percentages than this model would predict. A lot of these are tests of every person in a risky situation, whether they had symptoms or not:
E.g. the evacuation flights from Iran to China tested every passenger and showed a 3% infection rate. T The village of Vo testing their entire population twice soon after starting a Covid quarantine, with a 3% infection rate.
Others were testing large amounts of people with no particular reason they had Covid, but still with a skewed sample:
The Swedish sentinel testing of random people with any kind of flu symptoms (1.5% of people with flu symptoms testing as positive for Covid, vs. 30% testing positive for Influenza A/B). Iceland testing IIRC a volunteer 1.5% of their population whether they had symptoms or not, and having something like a 1% infection rate.
The thing all of these have in common is that they happened at a time in their relative epidemics where this model should have predicted the majority of the population was currently infected.
In fact, it's basically impossible to explain any testing results, since even when they're biased to cases where Covid is strongly suspected, the ratio of positives is so low. If we test the 1000 of people most suspected of having Covid right now, and get 10% positives, how can it possibly be the case that half the non-suspicious population are carriers at the time of that test?
2. If herd immunity kicks in 14 days after the first death as implied by this model, why haven't any of the epidemics died down by now? Italy is on what, day 30?
3. How does a super infectious but low mortality model explain the geographic clustering of deaths? Sure, the geographic clustering of known cases could be explained by testing bias. But deaths don't have that bias.
4. Observed high CFRs in limited populations where we know the infection rate was high. E.g. Diamond Princess at what 1.4%, and still another 2% in critical condition. How many top ranking Iranian leaders died in short order of Covid, and how does that fit in with a mortality rate of 0.01%? Or the cases where most of the patients of a health care facility or nursing home got infected?
2. The progress of the disease takes time too, from infection to symptoms to detection/hospitalization/secondary infection. And efforts to "flatten the curve" will "slow the spread" too.
3. I'm not sure about this one, but shouldn't the infectiousness also vary quite a lot with different contributing factors (population density, air quality, etc)? The number used in the model is always just an "average" in a sense.
4. The cruise ship evidence is pretty significant, but it still has problems. Any passengers who had already recovered or did not show symptoms could have been missed. Plus I can name several factors in that situation that might increase mortality off the top of my head. It's not the best sample for drawing conclusions about people who are on average less old, not traveling, and so forth.
Admittedly, the extreme "50% infected" scenario has a risk factor (same as IFR?) of 0.001%, which my gut feeling says is too optimistic. But as far as I know none of the scenarios can be conclusively disproven (until they can do proper serological surveys).
2. Their model predicts that the peak of the epidemic in Italy should have been before March 5th (first death on February 22nd + 14 days, at which point easily more than half the population is infected). There should have been a sharp drop in new cases about a week later, as the virus burnt itself down. But here we are three weeks later, and it's still not entirely clear that the peak has been found.
Italy did not institute significant nation-wide measures until March 9th, so the "slowdown from measures" explanation makes no sense.
3. Agreed. But their entire model is predicated on treating the entire country as a single unit. That's probably a part of the reason why the results are so absurd. I don't think it's fair to excuse the model for regional differences, but require any criticism of the model to take them into account.
4. The difference between the model's prediction and apparent reality is likely to be about a factor of 200. Even assuming everybody on the ship was actually infected, that only cuts it to a factor of 40 difference.
And it's really not just that single case. Consider that infamous Washington state nursing home. 120 residents, 35 dead from Covid to date. Even if we assume that every single one of the 120 was infected despite not testing positive, that still an IFR of 30%. Sure, it's a high-risk segment. But it's also a large enough segment a 30% IFT for them makes it quite impossible for the population-wide IFR to be 0.01%.
(Re: your last point, they had two parameters. One for being at risk of becoming a serious case, and another of dying if serious. The two need to be multiplied to get their predicted IFR. They assumed that 0.1% of population were at risk to become severe cases, and 15% of the severe cases died. So about 0.01%).
On the other, I don't understand how after making so many (often reasonable) assumptions in your arguments, you can say that it's "quite impossible" for the total infected to be so high, or the population-wide IFR to be three orders of magnitude lower than a nursing home or any other special case that you do not fully understand.
Here are some of those assumptions, which again do not all seem unreasonable to me:
- A mild case would likely be detectable by a PCR test for 8+ days
- The PCR test does not have a high false-negative rate in mild cases (see https://www.researchsquare.com/article/rs-17319/v1)
- Italy has not already had a sharp drop in new infections/most new infections are being identified as cases within two weeks
- Italy did nothing to slow the rate of infection until the full lockdown was in effect (but slower spread would mean higher mortality, no matter the reason)
- COVID-19 was the only/main thing that contributed to mortality in the special cases
Has it even been peer reviewed yet?
Un-fucking-believable.
I think it would be helpful if I cleared up some confusion that has emerged in recent days. Some have interpreted my evidence to a UK parliamentary committee as indicating we have substantially revised our assessments of the potential mortality impact of COVID-19
This is not the case. Indeed, if anything, our latest estimates suggest that the virus is slightly more transmissible than we previously thought. Our lethality estimates remain unchanged.
My evidence to Parliament referred to the deaths we assess might occur in the UK in the presence of the very intensive social distancing and other public health interventions now in place.
Without those controls, our assessment remains that the UK would see the scale of deaths reported in our study (namely, up to approximately 500 thousand).
all shapiro's blog does is move the lede to the beginning and add contexr