This seems pretty hard to evaluate because with a large number of published pre-prints on the outbreak, it’s not very surprising that there would be many suggesting higher-than-expected spread. The question is how that weighs up against the opposing evidence, and to evaluate that I’d have to look at all the opposing evidence, which I don’t want to do. That being said, broadly I am unconvinced. Notes on some of the dot points:
10% of 650,000 UK users of their C19 symptom tracker app showed mild symptoms. Thus 6.5m people in UK are infected
Presumably some of these people are hypochondriacs or have the flu? Also, I bet people with symptoms are more likely to use the app.
IFR=0.12% (95%CrI: 0.08-0.17%), several orders of magnitude smaller than the crude CFR estimated at 4.19%.
This isn’t very important but 0.12 is only 1.5 orders of magnitude smaler than 4.19, which I wouldn’t call “several”.
High proportion of special populations are infected (celebrities, athletes and politicians).
Couldn’t this be explained by those populations travelling more, shaking more hands, meeting more people, etc.?
Widespread testing (which isn’t random) in Iceland suggests an even lower IFR [than 0.3%].
Iceland has 2 deaths and 97 recoveries. I would say that isn’t good evidence for an IFR of under 0.3%. Admittedly the number of deaths so far is 0.2% of the total number of cases, but given exponential spread most of the cases will be new and won’t have had time to die yet, so the deaths to recoveries ratio seems more important (although upward-biased given who gets tested).
There were a few dengue in Australia and Florida where it is unusual
Dengue “popping up in unusual places”, makes me think that it’s more likely that massive Dengue outbreaks in Latin America might have a high proportion of C19.
One person had persistent negative swab, but tested positive through fecal samples...
“Chinese journalists have uncovered other cases of people testing negative six times before a seventh test confirmed they had the disease.”
This is just to lend credence to the paper that shows there had been 2 million infections in China in January.
I find it very unlikely on the face of it that China, or any country for that matter, managed to suppress completely a disease so contagious that it’s now on almost every country on earth.
This seems pretty hard to evaluate because with a large number of published pre-prints on the outbreak, it’s not very surprising that there would be many suggesting higher-than-expected spread.
No, this is different. I’m not just cherry picking the tail-end of a normal distribution of IFRs etc. The Gupta study in particular and some of the other studies suggest a fundamentally different theory of the pandemic.
Presumably some of these people are hypochondriacs or have the flu? Also, I bet people with symptoms are more likely to use the app.
Yes, but similarly there are many asymptomatic people who do not use the app. The King’s Professor seems to find this number convincing.
Couldn’t this be explained by those populations travelling more, shaking more hands, meeting more people, etc.?
Tom Hanks, Prince Charles and Boris Johnson don’t talk meet more people everyday then your typical Uber driver cashier etc. There millions of people working in retail. We don’t see them all having it. My theory is that they’re tested often and not that “there’s a lot of C19 in Westminster”
Iceland has 2 deaths and 97 recoveries. I would say that isn’t good evidence for an IFR of under 0.3%.
Crucially depends on the asymptomatic rate, which might very well be very high.
I’m not just cherry picking the tail-end of a normal distribution of IFRs etc. The Gupta study in particular and some of the other studies suggest a fundamentally different theory of the pandemic.
The point remains: given that some people have such a different theory, it’s unclear how many supporting pieces of evidence your should expect to see, and it’s important to compare the evidence against the theory to the evidence for it.
The King’s Professor seems to find this number convincing.
With all due respect it’s not that hard to get data that you yourself find convincing, even if you’re a professor.
Tom Hanks, Prince Charles and Boris Johnson don’t talk meet more people everyday then your typical Uber driver cashier etc.
They do meet more different populations of people though. So if a small number of cities have relatively widespread infection, people who visit many cities are unusually likely to get infected.
Crucially depends on the asymptomatic rate, which might very well be very high.
Not likely. About 1% of Icelanders without symptoms test positive, and all the stats on which tested people are asymptomatic that I’ve seen (Iceland, Diamond Princess) give about 1⁄2 asymptomatic at time of testing (presumably many later get sick).
The point remains: given that some people have such a different theory, it’s unclear how many supporting pieces of evidence your should expect to see, and it’s important to compare the evidence against the theory to the evidence for it.
Yes, that’s what I’m trying to do here. I feel this is a neglected take and on the margin more people should think about whether this theory is true, given the stakes.
Presumably some of these people are hypochondriacs or have the flu? Also, I bet people with symptoms are more likely to use the app.
With all due respect it’s not that hard to get data that you yourself find convincing, even if you’re a professor.
“”Our first analysis showed we’re picking up roughly that one in 10 have the classical symptoms,” he said. “So of the 650,000, we would expect to see 65,000 cases.
“Although you can have problems of self-selection and bias, when you’ve got big data like this you tend to trust it more. What we’re seeing is a lot of mild symptoms, so I think having this data should help people relax a bit more and stop seeing it as an all or nothing Black Death situation.
“Other symptoms are cropping up. Thousands of people are coming forward to say they have loss of taste, and we may start to see clusters of symptoms.”″
They do meet more different populations of people though. So if a small number of cities have relatively widespread infection, people who visit many cities are unusually likely to get infected.
You’d expect to see people to many severe cases amongst people who travelled for business a lot in January and February.
Not likely. About 1% of Icelanders without symptoms test positive, and all the stats on which tested people are asymptomatic that I’ve seen (Iceland, Diamond Princess) give about 1⁄2 asymptomatic at time of testing (presumably many later get sick).
Yes, [comparing the evidence against the theory to the evidence for it is] what I’m trying to do here.
It looks more like you listed all the evidence you could find for the theory and didn’t do anything else.
Although you can have problems of self-selection and bias, when you’ve got big data like this you tend to trust it more.
I don’t think this is actually how selection effects work.
You’d expect to see people to many severe cases amongst people who travelled for business a lot in January and February.
Those people are less famous so you wouldn’t necessarily hear about them.
I don’t quite understand what you’re saying here.
That the asymptomatic rate isn’t all that high, and in at least one population where everybody could get a test, you don’t see a big fraction of the population testing positive.
It looks more like you listed all the evidence you could find for the theory and didn’t do anything else.
That was precisely my ambition here—as highlighted in the title (“The case for c19 being widespread”). I did not claim that this was an even-handed take. I wanted to consider the evidence for a theory that only very few smart people believe. I think such an exercise can often be useful.
I don’t think this is actually how selection effects work.
The professor acknowledges that there are problems with self-selection, but given that there are very specific symptoms (thousands of people with loss of smell), I don’t think that selection effects can describe all the the data. Then he just argues for the Central Limit Theorem.
That the asymptomatic rate isn’t all that high, and in at least one population where everybody could get a test, you don’t see a big fraction of the population testing positive.
There’s no random population wide testing antibody testing as of yet.
This seems pretty hard to evaluate because with a large number of published pre-prints on the outbreak, it’s not very surprising that there would be many suggesting higher-than-expected spread. The question is how that weighs up against the opposing evidence, and to evaluate that I’d have to look at all the opposing evidence, which I don’t want to do. That being said, broadly I am unconvinced. Notes on some of the dot points:
Presumably some of these people are hypochondriacs or have the flu? Also, I bet people with symptoms are more likely to use the app.
This isn’t very important but 0.12 is only 1.5 orders of magnitude smaler than 4.19, which I wouldn’t call “several”.
Couldn’t this be explained by those populations travelling more, shaking more hands, meeting more people, etc.?
Iceland has 2 deaths and 97 recoveries. I would say that isn’t good evidence for an IFR of under 0.3%. Admittedly the number of deaths so far is 0.2% of the total number of cases, but given exponential spread most of the cases will be new and won’t have had time to die yet, so the deaths to recoveries ratio seems more important (although upward-biased given who gets tested).
I’m particularly unimpressed by the dot points noting things that happened to very few people:
Dengue “popping up in unusual places”, makes me think that it’s more likely that massive Dengue outbreaks in Latin America might have a high proportion of C19.
This is just to lend credence to the paper that shows there had been 2 million infections in China in January.
I find it very unlikely on the face of it that China, or any country for that matter, managed to suppress completely a disease so contagious that it’s now on almost every country on earth.
No, this is different. I’m not just cherry picking the tail-end of a normal distribution of IFRs etc. The Gupta study in particular and some of the other studies suggest a fundamentally different theory of the pandemic.
Yes, but similarly there are many asymptomatic people who do not use the app. The King’s Professor seems to find this number convincing.
Tom Hanks, Prince Charles and Boris Johnson don’t talk meet more people everyday then your typical Uber driver cashier etc. There millions of people working in retail. We don’t see them all having it. My theory is that they’re tested often and not that “there’s a lot of C19 in Westminster”
Crucially depends on the asymptomatic rate, which might very well be very high.
The point remains: given that some people have such a different theory, it’s unclear how many supporting pieces of evidence your should expect to see, and it’s important to compare the evidence against the theory to the evidence for it.
With all due respect it’s not that hard to get data that you yourself find convincing, even if you’re a professor.
They do meet more different populations of people though. So if a small number of cities have relatively widespread infection, people who visit many cities are unusually likely to get infected.
Not likely. About 1% of Icelanders without symptoms test positive, and all the stats on which tested people are asymptomatic that I’ve seen (Iceland, Diamond Princess) give about 1⁄2 asymptomatic at time of testing (presumably many later get sick).
Yes, that’s what I’m trying to do here. I feel this is a neglected take and on the margin more people should think about whether this theory is true, given the stakes.
“”Our first analysis showed we’re picking up roughly that one in 10 have the classical symptoms,” he said. “So of the 650,000, we would expect to see 65,000 cases.
“Although you can have problems of self-selection and bias, when you’ve got big data like this you tend to trust it more. What we’re seeing is a lot of mild symptoms, so I think having this data should help people relax a bit more and stop seeing it as an all or nothing Black Death situation.
“Other symptoms are cropping up. Thousands of people are coming forward to say they have loss of taste, and we may start to see clusters of symptoms.”″
https://www.telegraph.co.uk/news/2020/03/25/monitoring-app-suggests-65-million-people-uk-may-already-have/
You’d expect to see people to many severe cases amongst people who travelled for business a lot in January and February.
I don’t quite understand what you’re saying here.
It looks more like you listed all the evidence you could find for the theory and didn’t do anything else.
I don’t think this is actually how selection effects work.
Those people are less famous so you wouldn’t necessarily hear about them.
That the asymptomatic rate isn’t all that high, and in at least one population where everybody could get a test, you don’t see a big fraction of the population testing positive.
That was precisely my ambition here—as highlighted in the title (“The case for c19 being widespread”). I did not claim that this was an even-handed take. I wanted to consider the evidence for a theory that only very few smart people believe. I think such an exercise can often be useful.
The professor acknowledges that there are problems with self-selection, but given that there are very specific symptoms (thousands of people with loss of smell), I don’t think that selection effects can describe all the the data. Then he just argues for the Central Limit Theorem.
There’s no random population wide testing antibody testing as of yet.