The same source reports that in the province of Cremona (also part of Lombardy), 455 people had died of Covid-19 on 27 March. Cremona has a population of 360,000, meaning that 0.126% of the population of Cremona has died, according to official data.
Note also that there are reports of substantial under-reports of deaths in the Bergamo province. Some reports estimate that the true death rates in some areas may be as much as 1%. However, those reports are highly uncertain. And they may be outliers.
If the IFR is indeed .003% (the upper end of your range), then assuming the worst case scenario that 100% of the population of the UK gets infected eventually, only .003%*66.4 million = approx 2000 people will die total.
Would you consider the theory falsified if the death toll in the UK surpasses 2000?
No. My ambition here was a bit simpler. I have presented a rough qualitative argument here that infection is already widespread and only a toy model. There are some issues with this and I haven’t done formal modelling. For instance, this would be what would be called the “crude IFR” I think , but the time lag adjusted IFR (~30 days from infection to death) might increase the death toll.
Currently, also every death in Italy where coronavirus is detected is recorded as a C19 death.
FWIW, if UK death toll will surpass 10,000, then this wouldn’t fit very well with this hypothesis here.
FWIW, if UK death toll will surpass 10,000, then this wouldn’t fit very well with this hypothesis here.
If this update works then I feel like just looking at how the numbers in Italy came together would change your mind about the low-IFR hypothesis.
Alternatively, if the Covid-19 deaths in NY state go above 3,333 in the first week of April, that seems like it would also falsify the hypothesis. (NY state has fewer than one third the population of the UK.) Unfortunately I think this is >80% to happen.
Alternatively, if the Covid-19 deaths in NY state go above 3,333 in the first week of April, that seems like it would also falsify the hypothesis. (NY state has fewer than one third the population of the UK.) Unfortunately I think this is >80% to happen.
On April 4, the death toll in NY state surpassed 3,333. As of April 10, there are 7,844 deaths.
The death rate data coming in seems to be converging on a 0.5% to 0.7% death per infection rate. Multiple sources have estimated that weeks ago based on age-normalizing the Diamond Princess, and on testing evacuees from Wuhan.
Two serology surveys have now happened in Europe. One was in a hard-hit town in Germany, and one was in a hard-hit town in Italy at the epicenter of its outbreak. In both places, they got approximately a 15% seropositive rate. In Germany, we only have information on deaths with positive test rates and it comes to 0.35%. In Italy, total excess deaths over this time last year are about 2.5x the confirmed positive deaths and account for 0.1% of the population, giving an infection fatality rate of 0.7%. It is easy to imagine that some deaths did not get positive tests in Germany which along with a less-old population could make up for the difference.
From this, I estimate that at least 10% and possibly up to 20% of New York City has been infected, given the delay between infections and deaths. (100*8000 = 800,000, out of about 8 million)
One point of criticism is that the renowned German experts who were asked to comment on the study say they are skeptical about the antibody tests. They argue that to their knowledge, the only antibody tests widely in use in Germany at the time of the study can’t distinguish between SARS-CoV-2 and other coronaviruses responsible for a third of common colds. Because we are 1 month past the peak of cold season, they argue that the 15% could be largely picking up on false positives for SARS-CoV-2.
Given a 0.3% current acute infection rate and some epidemiological modeling they estimate 1% of their total population has been infected, with a death rate of 0.77%.
Yup. 0.77% is also what I keep stumbling upon when I look into various data points about the IFR! It’s my best guess about where Iceland’s IFR will end up, and very close to my best guess for proper age adjustment for the Diamond Princess.
Hardly an unbiased sample, but of 200+ pregnant women coming into a hospital to give birth that were blanket-tested, 15.3% tested positive.
Of this set of positive tests, only 12% of them were symptomatic on admission, and a further 10% developed symptoms over the course of their 2-day-long stays bringing it to a total of 22% symptomatic upon discharge or transfer. Presumably already-symptomatic women were more likely to be in the hospital already.
Doing a little armchair epidemiology. Let’s assume that half of the deaths of currently infected people have happened, due to the lockdown extending the doubling time from three days to more than a week. We get:
~8000 deaths * 2 / (15.3% of 8 million) = 1.3% infection to mortality rate.
If we assume that there were more symptomatic women who didn’t show up to normal birthing due to going to the hospital for COVID symptoms, we get a lower death rate. If 20% of the total population is infected, we get a 1% mortality rate. Could go lower if the doubling time has slowed less than my assumption, or if people who have recovered constitute a large enough actual segment of the population. Probably can’t account for more than a factor of two though, given known recovery times.
Compare this to what I wrote 21 hours ago, based on serology data from Italy and Germany:
‘From this, I estimate that at least 10% and possibly up to 20% of New York City has been infected, given the delay between infections and deaths. (100*8000 = 800,000, out of about 8 million) ’
I think what ignoranceprior was originally asking was, given all the information you know, what is your best estimate of the infection fatality rate? Best estimate in this case implies adjusting for ways that some research can be wrong, and taking into account the rebuttals you’ve read here.
I’m confused why you assume that 36-68% of the population in the UK is infected. I thought, based on comments here, that those numbers were the output of a model that made highly optimistic assumptions about IFR, not an attempt at estimating the actual proportion of infections.
Do you think this is a realistic range for the proportion already infected in the UK?
I’m not impressed by the comment about this paper here on LW or the twitter link in it.
This paper was written by an international team of highly cited disease modellers who know about the Diamond Princess and have put their reputation on the line to make the case that this the hypothesis of high infections rate and low infection fatality might be true.
I think it is a realistic range that this many people are already infected and are asymptomatic. Above I’ve tried to summarize and review the relevant evidence that fits with this hypothesis.
But I’m not ruling out the more common theory (that we have maybe only 10x the 500k confirmed cases). I just find it less likely.
This paper was written by an international team of highly cited disease modellers who know about the Diamond Princess and have put their reputation on the line to make the case that this the hypothesis of high infections rate and low infection fatality might be true.
Yes, but when you actually read the paper (I read some parts), it says that their model is based on an assumption of low IFR, and in itself did not argue for low IFR (feel free to prove me wrong here).
That’s true and that’s what they were criticized for.
They argued that the current data we observe can be also be explained by low IFR and widespread infection. They called for widespread serological testing to see which hypothesis is correct.
If in the next few weeks we see high percentage of people with antibodies then it’s true.
In the meantime, I thought it might be interesting to see what other evidence there is for infection being widespread, which would suggest that IFR is low.
I really appreciate your attempt to summarize this literature. But it seems you still believe that the Oxford paper provides evidence in favor of very low IFR, when in fact others are claiming that this is merely an assumption of their model, and that this assumption was made not because the authors believe it is plausible but simply for exploratory purposes. If this is correct (I haven’t myself read the paper, so I can only defer to others), then the reputation or expertise of the authors is evidentially irrelevant, and shouldn’t cause you to update in the direction of the very low IFR. (Of course, there may be independent reasons for such an update.)
Thanks Pablo for your comment and helping to clarify this point. I’m sorry if I was being unclear.
I understand what you’re saying. However:
I realize that the Oxford study did not collect any new empirical data that in itself should cause us to update our views.
The authors make the assumption that the IFR is low and the virus is widespread and find that it fits the present data just as well as high IFR and low spread. But it does not mean that the model is merely theoretical: the authors do fit the data on the current epidemic.
This is not different from what the Imperial study does: the Imperial authors do not know the true IFR but just assuming a high one and see whether it fits the present data well.
But indeed, on a meta-level the Oxford study (not the modelling itself) is evidence in favor of low IFR. When experts believe something to be plausible then this too is evidence of a theory to be more likely to be true and we should update. An infinite number of models can explain any dataset and the authors only find these two plausible.
By coming out and suggesting that this is a plausible theory, especially by going to the media, the authors have gotten a lot of flag for this (“Irresponsible”—see twitter etc.). So they have indeed put their reputation on the line. This is despite the fact that the authors are prudent and saying that high IFR is also plausible and also fits the data.
In Italy, with almost 10k deaths it would be 0.02%-0.04%
There’s an Italian village where 0.1% of the population already died with a confirmed diagnosis of Covid-19. Inferring from typical monthly death rates it’s also estimated that the twice as many people died from Covid-19 in that village without an official diagnosis. There’s a bunch of uncertainty about those additional 0.2%, but it would put the fatality rate at 0.3% already. And those figures are from 4 days ago (edit: 6 days ago actually).
This is a non-random village in Italy, so of course, some villages in Italy will show very high mortality just by chance.
It’s extremely implausible that it would be 10x or 15x higher than what’s expected for the typical Italian village. Besides, other villages like Cremona or Bergamo also seem to be close to those numbers. Smoking or age structure or air pollution doesn’t give you a 10x update.
UPDATE: Wow, I was totally wrong about those being villages. As Stefan Schubert pointed out, those are cities and provinces with tens and hundreds of thousands of inhabitants!
Thanks, Lukas. I only saw this now. I made a more substantive comment elsewhere in this thread. Lodi is not a village, it’s a province with 230K inhabitants, as are Cremona (360K) and Bergamo (1.11M). (Though note that all these names are also names of the central town in these provinces.)
If the Gupta study is true, then a rough approximation (ignoring lag) would be that it’s:
IFR = Number of UK deaths (~750) / 36-68% of the UK population (66 million).
So 0.002% to 0.003%.
In Italy, with almost 10k deaths it would be 0.02%-0.04%
In the province of Lodi (part of Lombardy), 388 people were reported to have died of Covid-19 on 27 March. Lodi has a population of 230,000, meaning that 0.17% of _the population_ of Lodi has died. Given that everyone hardly has been infected, IFR must be higher.
The same source reports that in the province of Cremona (also part of Lombardy), 455 people had died of Covid-19 on 27 March. Cremona has a population of 360,000, meaning that 0.126% of the population of Cremona has died, according to official data.
Note also that there are reports of substantial under-reports of deaths in the Bergamo province. Some reports estimate that the true death rates in some areas may be as much as 1%. However, those reports are highly uncertain. And they may be outliers.
https://www.facebook.com/stefan.schubert.3954/posts/1369053463295040
These numbers support my suspicion that >10% of North Italy has already been infected, with a death rate of ~1%.
If the IFR is indeed .003% (the upper end of your range), then assuming the worst case scenario that 100% of the population of the UK gets infected eventually, only .003%*66.4 million = approx 2000 people will die total.
Would you consider the theory falsified if the death toll in the UK surpasses 2000?
No. My ambition here was a bit simpler. I have presented a rough qualitative argument here that infection is already widespread and only a toy model. There are some issues with this and I haven’t done formal modelling. For instance, this would be what would be called the “crude IFR” I think , but the time lag adjusted IFR (~30 days from infection to death) might increase the death toll.
Currently, also every death in Italy where coronavirus is detected is recorded as a C19 death.
FWIW, if UK death toll will surpass 10,000, then this wouldn’t fit very well with this hypothesis here.
The UK death toll currently stands at 10,612 according to:
https://www.worldometers.info/coronavirus/country/uk/
Boy there was a lot of desperate motivated cogniton around a few weeks ago...
@Hauke Hillebrandt
If this update works then I feel like just looking at how the numbers in Italy came together would change your mind about the low-IFR hypothesis.
Alternatively, if the Covid-19 deaths in NY state go above 3,333 in the first week of April, that seems like it would also falsify the hypothesis. (NY state has fewer than one third the population of the UK.) Unfortunately I think this is >80% to happen.
On April 4, the death toll in NY state surpassed 3,333. As of April 10, there are 7,844 deaths.
The death rate data coming in seems to be converging on a 0.5% to 0.7% death per infection rate. Multiple sources have estimated that weeks ago based on age-normalizing the Diamond Princess, and on testing evacuees from Wuhan.
Two serology surveys have now happened in Europe. One was in a hard-hit town in Germany, and one was in a hard-hit town in Italy at the epicenter of its outbreak. In both places, they got approximately a 15% seropositive rate. In Germany, we only have information on deaths with positive test rates and it comes to 0.35%. In Italy, total excess deaths over this time last year are about 2.5x the confirmed positive deaths and account for 0.1% of the population, giving an infection fatality rate of 0.7%. It is easy to imagine that some deaths did not get positive tests in Germany which along with a less-old population could make up for the difference.
From this, I estimate that at least 10% and possibly up to 20% of New York City has been infected, given the delay between infections and deaths. (100*8000 = 800,000, out of about 8 million)
It’s worth noting that the German serology study (it was in the town Gangelt) has been criticized for being poorly presented: https://www.sueddeutsche.de/wissen/heinsberg-studie-herdenimmunitaet-kritik-1.4873480?fbclid=IwAR1mpGCPj21bffeXBe1fGJVeEWc7UlO2DkEP9-XrSCi4sJeh2-Ri_Cahwrw
One point of criticism is that the renowned German experts who were asked to comment on the study say they are skeptical about the antibody tests. They argue that to their knowledge, the only antibody tests widely in use in Germany at the time of the study can’t distinguish between SARS-CoV-2 and other coronaviruses responsible for a third of common colds. Because we are 1 month past the peak of cold season, they argue that the 15% could be largely picking up on false positives for SARS-CoV-2.
Some non-serology blanket RNA tests coming out of Austria.
https://www.theguardian.com/world/2020/apr/10/less-than-1-of-austria-infected-with-coronavirus-new-study-shows
Given a 0.3% current acute infection rate and some epidemiological modeling they estimate 1% of their total population has been infected, with a death rate of 0.77%.
Everything seems to be converging...
Yup. 0.77% is also what I keep stumbling upon when I look into various data points about the IFR! It’s my best guess about where Iceland’s IFR will end up, and very close to my best guess for proper age adjustment for the Diamond Princess.
New, amazing data from New York, as of April 13. https://www.nejm.org/doi/full/10.1056/NEJMc2009316
Hardly an unbiased sample, but of 200+ pregnant women coming into a hospital to give birth that were blanket-tested, 15.3% tested positive.
Of this set of positive tests, only 12% of them were symptomatic on admission, and a further 10% developed symptoms over the course of their 2-day-long stays bringing it to a total of 22% symptomatic upon discharge or transfer. Presumably already-symptomatic women were more likely to be in the hospital already.
Doing a little armchair epidemiology. Let’s assume that half of the deaths of currently infected people have happened, due to the lockdown extending the doubling time from three days to more than a week. We get:
~8000 deaths * 2 / (15.3% of 8 million) = 1.3% infection to mortality rate.
If we assume that there were more symptomatic women who didn’t show up to normal birthing due to going to the hospital for COVID symptoms, we get a lower death rate. If 20% of the total population is infected, we get a 1% mortality rate. Could go lower if the doubling time has slowed less than my assumption, or if people who have recovered constitute a large enough actual segment of the population. Probably can’t account for more than a factor of two though, given known recovery times.
Compare this to what I wrote 21 hours ago, based on serology data from Italy and Germany:
‘From this, I estimate that at least 10% and possibly up to 20% of New York City has been infected, given the delay between infections and deaths. (100*8000 = 800,000, out of about 8 million) ’
I think what ignoranceprior was originally asking was, given all the information you know, what is your best estimate of the infection fatality rate? Best estimate in this case implies adjusting for ways that some research can be wrong, and taking into account the rebuttals you’ve read here.
This is indeed what I meant. Also I was thinking about once-the-dust-settles IFR, not “crude IFR”.
I’m confused why you assume that 36-68% of the population in the UK is infected. I thought, based on comments here, that those numbers were the output of a model that made highly optimistic assumptions about IFR, not an attempt at estimating the actual proportion of infections.
Do you think this is a realistic range for the proportion already infected in the UK?
I’m not impressed by the comment about this paper here on LW or the twitter link in it.
This paper was written by an international team of highly cited disease modellers who know about the Diamond Princess and have put their reputation on the line to make the case that this the hypothesis of high infections rate and low infection fatality might be true.
I think it is a realistic range that this many people are already infected and are asymptomatic. Above I’ve tried to summarize and review the relevant evidence that fits with this hypothesis.
But I’m not ruling out the more common theory (that we have maybe only 10x the 500k confirmed cases). I just find it less likely.
Yes, but when you actually read the paper (I read some parts), it says that their model is based on an assumption of low IFR, and in itself did not argue for low IFR (feel free to prove me wrong here).
That’s true and that’s what they were criticized for.
They argued that the current data we observe can be also be explained by low IFR and widespread infection. They called for widespread serological testing to see which hypothesis is correct.
If in the next few weeks we see high percentage of people with antibodies then it’s true.
In the meantime, I thought it might be interesting to see what other evidence there is for infection being widespread, which would suggest that IFR is low.
I really appreciate your attempt to summarize this literature. But it seems you still believe that the Oxford paper provides evidence in favor of very low IFR, when in fact others are claiming that this is merely an assumption of their model, and that this assumption was made not because the authors believe it is plausible but simply for exploratory purposes. If this is correct (I haven’t myself read the paper, so I can only defer to others), then the reputation or expertise of the authors is evidentially irrelevant, and shouldn’t cause you to update in the direction of the very low IFR. (Of course, there may be independent reasons for such an update.)
Thanks Pablo for your comment and helping to clarify this point. I’m sorry if I was being unclear.
I understand what you’re saying. However:
I realize that the Oxford study did not collect any new empirical data that in itself should cause us to update our views.
The authors make the assumption that the IFR is low and the virus is widespread and find that it fits the present data just as well as high IFR and low spread. But it does not mean that the model is merely theoretical: the authors do fit the data on the current epidemic.
This is not different from what the Imperial study does: the Imperial authors do not know the true IFR but just assuming a high one and see whether it fits the present data well.
But indeed, on a meta-level the Oxford study (not the modelling itself) is evidence in favor of low IFR. When experts believe something to be plausible then this too is evidence of a theory to be more likely to be true and we should update. An infinite number of models can explain any dataset and the authors only find these two plausible.
By coming out and suggesting that this is a plausible theory, especially by going to the media, the authors have gotten a lot of flag for this (“Irresponsible”—see twitter etc.). So they have indeed put their reputation on the line. This is despite the fact that the authors are prudent and saying that high IFR is also plausible and also fits the data.
There’s an Italian village where 0.1% of the population already died with a confirmed diagnosis of Covid-19. Inferring from typical monthly death rates it’s also estimated that the twice as many people died from Covid-19 in that village without an official diagnosis. There’s a bunch of uncertainty about those additional 0.2%, but it would put the fatality rate at 0.3% already. And those figures are from 4 days ago (edit: 6 days ago actually).
Edit: It’s a province and city(!), not a village.
I do not think that can be used as decisive evidence to falsify wide-spread.
This is a non-random village in Italy, so of course, some villages in Italy will show very high mortality just by chance.
That region of Italy has high smoking rates, very bad air pollution, and the highest age structure outside of Japan.
It’s extremely implausible that it would be 10x or 15x higher than what’s expected for the typical Italian village. Besides, other villages like Cremona or Bergamo also seem to be close to those numbers. Smoking or age structure or air pollution doesn’t give you a 10x update.
UPDATE: Wow, I was totally wrong about those being villages. As Stefan Schubert pointed out, those are cities and provinces with tens and hundreds of thousands of inhabitants!
Thanks, Lukas. I only saw this now. I made a more substantive comment elsewhere in this thread. Lodi is not a village, it’s a province with 230K inhabitants, as are Cremona (360K) and Bergamo (1.11M). (Though note that all these names are also names of the central town in these provinces.)