I will repeat a question I would love an epidemiologist to reply to: Where is the random background testing? Not testing "I feel unwell" presentations at random, truly going into the field and testing the wider population at random, healthy (believed) or not?
You cannot model the IFR accurately from skewed samples and it feels like we aren't even doing skewed sampling.
Testing resources are in short supply, so testing is being performed to guide clinical decisions (ie sick people) rather than public health/science (ie random sample).
The handful of serosurveys that have been performed have been quite valuable, but there aren't nearly enough.
Hopefully this changes as testing capacity ramps up.
Santa Clara county did surveillance testing which likely saved California https://www.cdc.gov/mmwr/volumes/69/wr/mm6914e3.htm
despite the shortage of test kits. Just imagine if the nation had sufficient tests available.
This, unless we do that there is no way to establish true IFR so we wont know until its in hindsight how deadly the covid is? I dont know why this is not the priority 1 for the CDC.
on the similar lines, Germany is doing something clever, they are mixing samples to do a kind of binary search with covid tests so they can clear a large number of people with few tests. we should do that without waiting for antibody tests etc. it should be noted that this approach would only work if the infected sample set is very small so the window of doing this is going to close soon.
Both Munich and Prague have started such studies with several thousand random people in each. That should give us a much better estimate for the number of "silent" cases.
This is a useful albeit very small data point, because Iceland received its cases from both Europe (alpine ski cluster) and the US. So it puts a very loose upper bound on the incidence in the US up to the point travel to Iceland was effectively halted.
So far only PCR tests are available at scale. Those PCR tests are very sensitive (e.g when detecting traces of long broken virus on surfaces) but even they are only reliably positive for a few days into the infecting. Apparently, the immune system wins the battle for the upper throat quite easily, even in patients who later die from pneumonia. If you did a random sample study with a test that only identifies a positive for a short time you'd still have a huge uncertainty from not knowing reliable averages about how long someone infected "lights up" in the test. There just isn't much information upside from background sampling PCR, particularly when serological tests (those than detect who already had the virus) will soon be available. Those are not useful at all to combat spread anyways, so they can be used for background testing without competing for more immediate use cases.
Dr. Fauci addressed that the other day when asked about when schools might reopen. He hedged a lot, but basically said he thought it could happen for the Fall. At the same time he was talking about that as the time frame for wide-spread anti-body testing that would show just how far the virus had penetrated. (And that such testing and continued contact tracing etc. would be what facilitated cautious steps towards reopening society)
There was a town in Italy, roughly 10k population, where they tested every single person regardless of symptoms. The result highlighted how many asymptomatic people tested positive. AFAIK the test was not repeated so there was no data on evolution / spread. But it was still an eye opener.
(My apologies for not citing the article, I had read it in the paper edition of the NY Times, and failed to find it online.)
At the moment, that data would not make any significant difference on the actual decisions that are made today. Or can you think of anything you would change with the knowledge that 20% of the population have been infected, versus 2%? As long as thousands die each day, there really is no alternative to the current strategy.
It would possibly help planning for the next phase. But mostly it would feed our curiosity.
Contrast to using limited tests to find the greatest number of infected people: every additional person knowing without a doubt that they are infected is one more person doing their best not to spread it further.
It’s wasteful to test randomly until we are able to test high risk groups every day or so. Cashiers, for example are at high risk of infection, and ha e possibly hundreds of contacts per day.
For population-randomized testing, antibody tests should be far superior anyway. They allow detection of. It just active cases but also past infections.
Stanford just tested 3200 people in Silicon Valley for antibodies at a drive-through location.[1] There are similar group tests being run in LA and Colorado. In a few days there should be more solid info on how many people have had the disease without symptoms.
(As someone who knows next to nothing about medicine or microbiology) I’m curious how they already know which antibodies to look for? Are antibodies unique to each virus? Is it possible that some people have stumbled upon antibody X at some point in their lives that maybe isn’t perfect for fighting off this new coronavirus but still helps? Along the same lines, is it possible some people have antibodies IgM and IgG from some other illness they’ve encountered in their lives? Or is the presence of those antibodies conclusive evidence that they’ve been infected with COVID-19?
I've seem people theorize about this before saying maybe it's good news because that means it's less fatal than we thought and therefore maybe we're closer to herd immunity. But I saw an epidemiologist talk about how an R0 of 2 meant you needed 50% of people to be immune for herd immunity, and an R0 of 3 meant you needed 66%. This would mean it'd need to be even higher - the article says 82%. We're nowhere close to that now - even northern Italy, they're estimating that maybe 10% have caught it.
R0 estimated to be 2 - 4. Fatality rate: 0.1 - 3%.
This was the first information I saw that helped me understand why this was more than just a bad flu. Still didn't truly understand it at that point, at least the way I do now that we're experiencing the consequences of failing to contain it.
Because the IHME model is based on taking China's data at 100% face value and applying it to the US. It over-predicts in the short term and under-predicts in the medium to long term. They keep revising the overall number down to line up with an epidemic peak in 3-4 days in the US, which is ridiculous.
Taking your post at face value, I would say we know it’s twice as infectious due to more testing and/or better data. So we know that more people have been infected.
We didn’t discover a bunch of dead people that were infected. So the denominator increased relative to the numerator.
Being highly infectious is one thing but a virus’s deadliness is another. Something can have a really high infectiousness rate but not be very deadly, like common cold. The infectiousness they are citing her is the infectiousness that exists under normal conditions. So if you social distance, you can lower the infectiousness. Projected death rates are going down because distancing is working and we are lowering the virus’s infectiousness.
> Can anyone explain how the virus is twice as infectious as we thought, but we're consistently lowering our death projections everyday?
This article is talking about R0-- a measure of contagion without considering interventions like increasing mask wearing, quarantines, or physical distancing.
The higher the R0 the harder we have to work to push the effective R below 1, as is required to stop the spread... and the wider and faster the spread will be if we don't mitigate it.
"but we're consistently lowering our death projections every day?"
'The first casualty if war is the Truth'.
Whatever the government tells you during a time of crisis is a form of propaganda, 'for your own good' so to speak.
Even if individuals are intelligent, crowds are not.
Whatever they say is very calculated and controlled, as if to achieve a specific outcome. So imagine a very cynical view of political messaging in normal times, but now tilt that towards a more truly civic situation wherein maybe it doesn't seem quite so cynical because, well, there is actually a crisis.
The 'numbers' projected a few weeks ago will have been constrained by a) what people could handle without panicking, b) what kind of numbers might get them to actually behave properly, c) what will save every political leaders skin (i.e. give bad news then everything after that seems like good news), d) how much we can bend reality without hurting their own credibility by being perceived as lying.
It's extremely hard and politically risky to 'shut down a country' and get millions of individual actors to 'buy-in' to behave as we want especially if it means annoyance or personal hardship. Political leaders are used to acting in a very populist way, and basically right now they are doing the extreme opposite. Every day, politicians have to act against their best populist instincts. It's hard to overstate what a sensitive time this is, it could go sour very quickly.
So take everything with a grain of salt, knowing that whatever is being said is calculated and 'all projections' are filtered somewhat. Ostensibly 'for our own good'.
I think there are often more qualified numbers published out there, but you have to go right to the source, if available.
Edit: the 'masks' PR and policy is probably the best example of that. In reality, there's not much harm, and likely a little bit of good that can come from masks. But the strategy was to get PPE to the front-line health workers who have a greater need and were in a real crisis, so, the public messaging was 'no masks'. But the 'truth' of masks started to creep to the fore, more were asking questions, moreover, the PPE situation started to stabilize a little bit and 'poof' all of a sudden 'masks are good'. Now they are telling us? The Canadian Chief Medical Officer literally did a 180 on that, sounding a lot like Donald Trump in his total about turns. The communications strategy early on was fairly clear and it made sense, but it's a little uncomfortable to see your so-called leaders only make decisions 'after everyone else' so as to avoid taking and risky or blame (Canada wouldn't budget on it until most other countries did first). If you read the fairly confident communications about masks from several weeks ago and compare them to the messaging now - you see a problem that very only makes sense in the context of "purposeful misdirection for the 'public good' ". Masks did not 'get safer' and nor did our understanding of them change. What changed was who ostensibly needed them the most.
This seems a bit odd. If it was my job to estimate R₀ for the coronavirus, I'd look at Singapore, Korea, Australia, New Zealand and so on, where there are thousands of patients, and almost all of them know when they caught it and who they caught it from. Why would you choose to study a place where people don't know those things?
Because that is the definition of R0. Once people are aware and change their behavior R changes as well and is not readily translatable to a different setting.
Also the Bios of a couple of the authors at the end:
"Dr. Sanche is a postdoctoral research associate at Los Alamos National Laboratory, Los Alamos, New Mexico, USA. His primary research interest lies in complex disease dynamics inferred from data science and mathematical modeling. Dr. Lin is also a postdoctoral research associate at Los Alamos National Laboratory. His primary research interest lies in applied stochastic processes, biological physics, statistical inference, and computational system biology."
If people weren't dying, it would be hilarious had badly understood this epidemic is. Months since the start, we have no idea how infectious the virus is, how many people have been infected, how deadly it is, how effective (or not) containment is, how many people will need to be hospitalised or how long for.
It's actually incredible. I'm very cynical, but I wouldn't have believed Western governments could have floundered so badly if I'd been told even a month ago...
I hear a lot about how we may be seriously underestimating the CFR denominator, due to undiagnosed mild cases. Ok I get that.
What I don’t hear much about is that the numerator is also probably understated at any point in time for several reasons.
First I don’t trust the China data I think it’s understated and that’s the oldest, most mature data we have.
Second, in an exponentially growing disease with something like a 6 week course from infection through to mortality/recovery, we will always have diagnosed cases that are 6 weeks, or whatever the true course is, ahead of the final death tally and as the diagnosed cases are rising so rapidly, including those weeks and weeks worth of diagnosed but unresolved cases could add up to a huge amount of error to a simple deaths/cases CFR analysis that would significantly understate what the final CFR will look like. Cohort analysis does not appear to have caught on in the CFR calculation world from what I’ve been reading.
Third, I’ve read that there are likely a significant number deaths that are probably attributable to COVID-19 that are not being counted as Covid-19. The death rate in northern Italy over the last month, even when all the COVID-19 attributed deaths are removed, is significantly higher than it has been in similar periods in the past I have read. I believe the same is true in New York City. So if these stories are correct, there are likely more COVID-19 deaths than are being counted.
So while it is probably true that the denominator is understated, it seems to me that it’s also very likely true that the numerator is also understated making it very difficult for me to believe any of these estimates are very accurate until both these issues are addressed.
Has anyone seen anything that explains some of these issues and calculates a cohort-based death rate, which somehow estimates or adjusts were incorrect, time shifted or under counted Fatality data?
No one thought that it depends on people's habits, say common social norms? Say in Asia it was commonplace to wear masks even before epidemic and it is considered polite to stand far away from one another, and people bow instead of shaking hands. Quite clearly there, or say in Sweden with their tradition of having a large privacy distance, R0 is much lower than in Italy where people hug and kiss seeing each other.
There are plenty of anecdotal stories that show that it spreads very quickly. A highly publicized story in Germany was about one of the first ten cases here. This guy merely sat behind a patient and got asked for a salt shaker. That was his only contact.
And I know of a company where someone who returned from a vacation in Italy was in a meeting with 6 people. 5 of them got infected.
And now we wait for the other shoe to drop: serosurveys showing that at least one-third of the US population has already caught COVID-19 and recovered.
This means that “herd immunity “ is a non-starter: natural immunity through infection/recovery requires large fractions of the high-risk population to be infected, and vaccines are too far out (economic destruction would occur before vaccines may exist).
We must pursue large-scale testing on a “total war” basis, with the goal of containment and extinguishing the virus.
[+] [-] ggm|6 years ago|reply
You cannot model the IFR accurately from skewed samples and it feels like we aren't even doing skewed sampling.
[+] [-] XFrequentist|6 years ago|reply
Testing resources are in short supply, so testing is being performed to guide clinical decisions (ie sick people) rather than public health/science (ie random sample).
The handful of serosurveys that have been performed have been quite valuable, but there aren't nearly enough.
Hopefully this changes as testing capacity ramps up.
[+] [-] fspeech|6 years ago|reply
[+] [-] DesiLurker|6 years ago|reply
on the similar lines, Germany is doing something clever, they are mixing samples to do a kind of binary search with covid tests so they can clear a large number of people with few tests. we should do that without waiting for antibody tests etc. it should be noted that this approach would only work if the infected sample set is very small so the window of doing this is going to close soon.
[+] [-] mmmrtl|6 years ago|reply
[+] [-] petschge|6 years ago|reply
[+] [-] neffy|6 years ago|reply
https://grapevine.is/news/2020/04/06/random-sampling-reveals...
This is a useful albeit very small data point, because Iceland received its cases from both Europe (alpine ski cluster) and the US. So it puts a very loose upper bound on the incidence in the US up to the point travel to Iceland was effectively halted.
[+] [-] usrusr|6 years ago|reply
[+] [-] ineedasername|6 years ago|reply
[+] [-] athenot|6 years ago|reply
(My apologies for not citing the article, I had read it in the paper edition of the NY Times, and failed to find it online.)
[+] [-] gavreh|6 years ago|reply
[+] [-] rubber_duck|6 years ago|reply
[+] [-] IAmEveryone|6 years ago|reply
It would possibly help planning for the next phase. But mostly it would feed our curiosity.
Contrast to using limited tests to find the greatest number of infected people: every additional person knowing without a doubt that they are infected is one more person doing their best not to spread it further.
It’s wasteful to test randomly until we are able to test high risk groups every day or so. Cashiers, for example are at high risk of infection, and ha e possibly hundreds of contacts per day.
For population-randomized testing, antibody tests should be far superior anyway. They allow detection of. It just active cases but also past infections.
[+] [-] zouhair|6 years ago|reply
[0]: https://www.sciencedaily.com/releases/2020/03/200331092713.h...
[+] [-] Animats|6 years ago|reply
[1] https://www.stanforddaily.com/2020/04/04/stanford-researcher...
[+] [-] elliekelly|6 years ago|reply
[+] [-] droidist2|6 years ago|reply
[+] [-] tunesmith|6 years ago|reply
[+] [-] fspeech|6 years ago|reply
[+] [-] klenwell|6 years ago|reply
https://www.nytimes.com/interactive/2020/world/asia/china-co...
R0 estimated to be 2 - 4. Fatality rate: 0.1 - 3%.
This was the first information I saw that helped me understand why this was more than just a bad flu. Still didn't truly understand it at that point, at least the way I do now that we're experiencing the consequences of failing to contain it.
[+] [-] chickenpotpie|6 years ago|reply
[+] [-] jboggan|6 years ago|reply
[+] [-] FPGAhacker|6 years ago|reply
We didn’t discover a bunch of dead people that were infected. So the denominator increased relative to the numerator.
Not stating this as fact.
[+] [-] unsrsly|6 years ago|reply
[+] [-] lindenksv85|6 years ago|reply
[+] [-] tunesmith|6 years ago|reply
[+] [-] nullc|6 years ago|reply
This article is talking about R0-- a measure of contagion without considering interventions like increasing mask wearing, quarantines, or physical distancing.
The higher the R0 the harder we have to work to push the effective R below 1, as is required to stop the spread... and the wider and faster the spread will be if we don't mitigate it.
[+] [-] jariel|6 years ago|reply
'The first casualty if war is the Truth'.
Whatever the government tells you during a time of crisis is a form of propaganda, 'for your own good' so to speak.
Even if individuals are intelligent, crowds are not.
Whatever they say is very calculated and controlled, as if to achieve a specific outcome. So imagine a very cynical view of political messaging in normal times, but now tilt that towards a more truly civic situation wherein maybe it doesn't seem quite so cynical because, well, there is actually a crisis.
The 'numbers' projected a few weeks ago will have been constrained by a) what people could handle without panicking, b) what kind of numbers might get them to actually behave properly, c) what will save every political leaders skin (i.e. give bad news then everything after that seems like good news), d) how much we can bend reality without hurting their own credibility by being perceived as lying.
It's extremely hard and politically risky to 'shut down a country' and get millions of individual actors to 'buy-in' to behave as we want especially if it means annoyance or personal hardship. Political leaders are used to acting in a very populist way, and basically right now they are doing the extreme opposite. Every day, politicians have to act against their best populist instincts. It's hard to overstate what a sensitive time this is, it could go sour very quickly.
So take everything with a grain of salt, knowing that whatever is being said is calculated and 'all projections' are filtered somewhat. Ostensibly 'for our own good'.
I think there are often more qualified numbers published out there, but you have to go right to the source, if available.
Edit: the 'masks' PR and policy is probably the best example of that. In reality, there's not much harm, and likely a little bit of good that can come from masks. But the strategy was to get PPE to the front-line health workers who have a greater need and were in a real crisis, so, the public messaging was 'no masks'. But the 'truth' of masks started to creep to the fore, more were asking questions, moreover, the PPE situation started to stabilize a little bit and 'poof' all of a sudden 'masks are good'. Now they are telling us? The Canadian Chief Medical Officer literally did a 180 on that, sounding a lot like Donald Trump in his total about turns. The communications strategy early on was fairly clear and it made sense, but it's a little uncomfortable to see your so-called leaders only make decisions 'after everyone else' so as to avoid taking and risky or blame (Canada wouldn't budget on it until most other countries did first). If you read the fairly confident communications about masks from several weeks ago and compare them to the messaging now - you see a problem that very only makes sense in the context of "purposeful misdirection for the 'public good' ". Masks did not 'get safer' and nor did our understanding of them change. What changed was who ostensibly needed them the most.
[+] [-] guscost|6 years ago|reply
[+] [-] thisrod|6 years ago|reply
[+] [-] fspeech|6 years ago|reply
[+] [-] unknown|6 years ago|reply
[deleted]
[+] [-] mmmrtl|6 years ago|reply
[+] [-] Scipio_Afri|6 years ago|reply
"Author affiliations: Los Alamos National Laboratory, Los Alamos, New Mexico, USA"
[+] [-] Scipio_Afri|6 years ago|reply
"Dr. Sanche is a postdoctoral research associate at Los Alamos National Laboratory, Los Alamos, New Mexico, USA. His primary research interest lies in complex disease dynamics inferred from data science and mathematical modeling. Dr. Lin is also a postdoctoral research associate at Los Alamos National Laboratory. His primary research interest lies in applied stochastic processes, biological physics, statistical inference, and computational system biology."
[+] [-] LatteLazy|6 years ago|reply
It's actually incredible. I'm very cynical, but I wouldn't have believed Western governments could have floundered so badly if I'd been told even a month ago...
[+] [-] bbeez|6 years ago|reply
What I don’t hear much about is that the numerator is also probably understated at any point in time for several reasons.
First I don’t trust the China data I think it’s understated and that’s the oldest, most mature data we have.
Second, in an exponentially growing disease with something like a 6 week course from infection through to mortality/recovery, we will always have diagnosed cases that are 6 weeks, or whatever the true course is, ahead of the final death tally and as the diagnosed cases are rising so rapidly, including those weeks and weeks worth of diagnosed but unresolved cases could add up to a huge amount of error to a simple deaths/cases CFR analysis that would significantly understate what the final CFR will look like. Cohort analysis does not appear to have caught on in the CFR calculation world from what I’ve been reading.
Third, I’ve read that there are likely a significant number deaths that are probably attributable to COVID-19 that are not being counted as Covid-19. The death rate in northern Italy over the last month, even when all the COVID-19 attributed deaths are removed, is significantly higher than it has been in similar periods in the past I have read. I believe the same is true in New York City. So if these stories are correct, there are likely more COVID-19 deaths than are being counted.
So while it is probably true that the denominator is understated, it seems to me that it’s also very likely true that the numerator is also understated making it very difficult for me to believe any of these estimates are very accurate until both these issues are addressed.
Has anyone seen anything that explains some of these issues and calculates a cohort-based death rate, which somehow estimates or adjusts were incorrect, time shifted or under counted Fatality data?
[+] [-] anovikov|6 years ago|reply
[+] [-] yters|6 years ago|reply
[+] [-] guscost|6 years ago|reply
[+] [-] DesiLurker|6 years ago|reply
[+] [-] AzzieElbab|6 years ago|reply
[+] [-] tjansen|6 years ago|reply
[+] [-] Medicalidiot|6 years ago|reply
[+] [-] guscost|6 years ago|reply
[+] [-] unknown|6 years ago|reply
[deleted]
[+] [-] cryptonector|6 years ago|reply
[+] [-] _yid9|6 years ago|reply
This means that “herd immunity “ is a non-starter: natural immunity through infection/recovery requires large fractions of the high-risk population to be infected, and vaccines are too far out (economic destruction would occur before vaccines may exist).
We must pursue large-scale testing on a “total war” basis, with the goal of containment and extinguishing the virus.
[+] [-] bwandu|6 years ago|reply
[deleted]