This seems like it could just be measurement error. Because cases cluster, someone who is in contact with an asymptomatic case is likely also in contact with symptomatic cases. But if you aren’t testing everyone, then you don’t know who’s sick.
Example: A and B, who are both infected, go to a party. A becomes symptomatic a few days later, B never does. Seven people who go to that party later get sick. Now, let’s say in reality 5 of those people were infected by B. He’s a loud talker and a bit of a spittle sprayer.
But B never gets tested because he never gets sick. A eventually gets tested, as do some of the people at that party.
If you do contact tracing with the data you have, you’re going to end up attributing 7 cases to A, and 0 to B. The only people you’re going to attribute to asymptomatic patients are people who only came into contact with asymptomatic patients.
He says 12.5% of the Roosevelt tested positive. That is 12.5% of the total on the ship. But only 94% were tested at the time of the recording. To assume the other 6% are negative seems wrong.
He says this data shows the beauty of having full testing. He claims that we’ll know exactly who to separate and quarantine. But that assumes a perfect test, which we know we don’t have. Allowing someone who tested negative back to normal duty could be doing nothing but exposing the other 4200 on the ship to the infection. This is a big reason why I don’t see how more testing is going to save us and allow the country to open back up, even if we could test everyone everyday.
If they are testing for active virus, there could be many more that are no longer shedding. So the infected rate is, at a minimum, 600/(0.94*4800) = 13.2%
One would want to know why the 6% were not tested. But assuming they were just random people that somehow were not able to be tested, one would indeed assume that 12.5% of them would also test positive.
The thing is - we don’t have to be perfect here. The point is to get R down really low. Test and quarantine can do this. We don’t expect R to be zero, we expect that it will be driven very low because we don’t give hot spots time to grow. That will contain, or even snuff out the contagion, even if we miss some.
On the subject of the unexpectedly high numbers for previous Covid-19 infections, Medscape has this to say: https://www.medscape.com/viewarticle/928954
(Not sure of you need to register to read it, registration is free anyway.)
Lots of questions about the false positive rate of the serology test, even a small glitch will produce significant overestimates. Serology test used was a Chinese one that claimed specificity between 98.3 and 99.9%. 98.3 would account for pretty much all of the 50 people detected. Also questions about using Facebook to recruit subjects skewing the survey.
On a survey that tried to estimate Covid-19 infections on the basis that during three weeks of March, anything 'flu like that wasn’t tested as 'flu was Covid-19, they have already revised their estimated number down from 28 million to 8.7 million after discovering the CDC pooled some numbers, invalidating the first calculation.
False positives estimating a small population is always going to be a problem. With testing using whatever test kit that can be found, with no proper validation yet done, we might expect to see some wild numbers for a while yet.
Good points. Rather than derail the OP further than I have continuing this discussion, I think it important to note that the information in the video about the difference in asymptomatic infection rates between the two groups probably the most relevant part of the video to this discussion. So we’ll just leave it at that in this discussion. But you do make great points. Thanks.
Yeah…I got sucked down the rabbit hole in looking at that Santa Clara County study the other night and I think we can say with pretty strong certainty that they screwed up the low side of their confidence intervals by not properly considering the uncertainty in the actual false positive rate based on what the manufacturer found for false positives in the sample that the manufacturer had studied. So, while it is true that their data is compatible with a prevalence of a few percent (and, hence, much higher than the known infection rate there), it is also compatible with an infection rate of 0% within 95% confidence intervals. So, in other words, it tells us very little about how much higher the prevalence of the virus might be compared to the known cases.
If 21% of NYC has had the virus and its already killed 0.1% of the city, then thats an IFR of about 0.5%. So if the virus has a high R0, and you need 80% infected to reach herd immunity (and we dont’ get a virus or any effective medical treatments), thats about 1.3 million Americans who will die in the US from it.
I’m assuming blacks and latinos have double the infection rates of whites due to them having less liquid assets to withstand unemployment and not having jobs that you can work from home as often.
If you do the math and assume NY state has 6x higher infection rate (since their NY state death rate seems to be 6x higher than the death rate for the country as a whole) then that means the national infection rate is closer to 2-3% of the total population who have had it?
Unless my math is wrong.
The rest of NY state outside of a few highly populated counties is about 3.6%.
So I’m wondering if the national rate of those who have been infected is only about 3% or so.
Interesting results and I must say I was expecting a larger number, like 50% plus.
The various test products have different performance metrics so between that and the usual sample of convenience concerns of course it is another bit to be interpreted as part of a whole. More complicated as infection and death rates are just peaking now so knowing what portion of the curve this reflects is unknown.
My understanding is that the tests in general reach best reliability days 11 to 24 after infection and that false negatives are overall about 13%. But who knows for this specific test?
Given that deaths lag too it might be reasonable to consider deaths as reflective of these number of infections. Depending on the performance of the product. Both possibly representing the number of infections and the number who would die from those infections of a bit more than two weeks ago.
So with those caveats -
21.2% of the 8.6 million NYC residents with a current deaths number of 11,267 would be a surprisingly high 0.6% IFR if indeed it was a representative sample, if they represented the same points of the still rapidly moving curve, and the product’s false negative rate was reasonable.
That is a scary number if true.
And if true then despite the fairly rapid drop in daily death rates (the 7 day moving average dropping 5d in a row and the one day dropping from a peak of 165/million/day on 4/16 to 16/million/d on 4/22) unlocking should be very cautiously done, as the odds would be that herd immunity is likely a ways away. Since two weeks ago infections have roughly doubled (by confirmed infection rate numbers) to tripled (by death rate numbers) … so the number who will be immune by the time they fight off current infections by this preliminary data is somewhere between 28 to 42% based on this snapshot. Although no one knows how much is needed for herd immunity in any specific population, it’s a guess … 80% is a much higher guess than is generally made … but 28% would be lower than generally believed.
40 to 60 is what is the usual guess range. What need be emphasized though is that while these are educated people making educated guesses they are still guesses based on very little to base a guess off of. One of the big problems has been the tendency of some of those doing the modeling to fail to communicate the huge uncertainties of their guesses.
Another way to think of this information. Again antibody positivity now most likely captures infections up to about two weeks ago, some more some less. Two weeks ago the confirmed case number was about 150K in New York. This study says in fact 2.7M were infected at that time. That would mean that, according to this result, even with ample testing there were 18 times more infections than identified.
Not just contagiousness, but also the number of asymptomatic carriers. With most really virulent diseases they tend to be self-limiting just because infected people don’t feel like going to work or socializing, and so the group of people who are most likely to be infected are in the household. If there are a large percentage of people who can actively shed the virus without recognizing symptoms and displaying clear signs like fever or sneezing, then the virus can continue to circulate unseen (without comprehensive testing), and the standard surveillance methods and self-isolation cannot be relied upon.
Herd immunity should be thought of not as a barrier or a point at which the virus stops spreading, but a threshold at which the rate of spread is consistently declining which requires that most of the people an infected person comes into contact with have immunity. In some cases, like influenza, a herd immunity through vaccination can be effective even with only about a third of the population getting inoculated (and the vaccination being less than perfect). For poliovirus, the initial vaccine was only about 70% effective, and only about 70% of the population was vaccinated, so the effective immunity was about 50%, but that was sufficient to limit outbreaks of polio to the point that it did not achieve epidemic proportions. For Varicella zoster, the virus that causes chickenpox, we never had effective herd immunity prior to a vaccine even though the vast majority of the adult population was exposed and could no longer express the virus in significant quantity; its prevalence in human populations was persistent enough that it infected nearly every person on the planet.
In addition to asymptomatic human carriers, we know for certain that the virus can infect a number of different cat species (but apparently not dogs) and potentially other animals that people are in regular contact with, which means it may have a persistent reservoir even if we could eradicate it from the human population. And of course, if the immunity conferred by exposure or vaccination only last a few years or less, the virus may be sufficiently endemic in other populations to spillback into the developed world, which means we need to think of vaccination in terms of both a global program and surveilling domestic animal populations for indications that the virus is circulating.
There so many unknowns about how the SARS-CoV-2 virus operates in the human body, but here’s some additional bad news: Washington Post: “A mysterious blood-clotting complication is killing coronavirus patients”. This new effect explains some of the odd behaviors and may be a clue as to why the presentation of COVID-19 is so severe in a small segment of the population and has little effect on others, but the worrisome observation is this:
*Increasingly, doctors also are reporting bizarre, unsettling cases that don’t seem to follow any of the textbooks they’ve trained on. They describe patients with startlingly low oxygen levels — so low that they would normally be unconscious or near death — talking and swiping on their phones. Asymptomatic pregnant women suddenly in cardiac arrest. Patients who by all conventional measures seem to have mild disease deteriorating within minutes and dying at home.
With no clear patterns in terms of age or chronic conditions, some scientists hypothesize that at least some of these abnormalities may be explained by severe changes in patients’ blood.
Autopsies have shown some people’s lungs fill with hundreds of microclots. Errant blood clots of a larger size can break off and travel to the brain or heart, causing a stroke or heart attack. On Saturday, Broadway actor Nick Cordero, 41, had his right leg amputated after being infected with the novel coronavirus and suffering from clots that blocked blood from getting to his toes.
Lewis Kaplan, a University of Pennsylvania physician and head of the Society of Critical Care Medicine, said every year doctors treat people with clotting complications, from those with cancer to victims of severe trauma, “and they don’t clot like this.”
“The problem we are having is that while we understand that there is a clot, we don’t yet understand why there is a clot,” Kaplan said. “We don’t know. And therefore, we are scared.”*
So not only may there be many asymptomatic people and animal reservoirs, but even if we have an effective antibody test that allows us to identify people who are (presumably) immune we cannot necessarily predict who will have a benign response to infection and who may be likely have profound illness or mortality. I don’t think we can assume any level of herd immunity and testing will protect vulnerable people until we have a better understanding of how the virus functions in the body and how long any conferred immunity will last.
These do seem to be truly asymptomatic rather than presymptomatic individuals, but I am not sure if that can be stated with complete confidence. Also unclear the age range of those on the ship … my WAG is that a cruise of the Antarctic Peninsula following “a route similar to that taken by the British explorer, Ernest Shackleton, in 1915–1917” might have a different demographic than a Carnival Cruise. Also noted that antibody studies done during an acute phase were falsely negative but follow up testing in not reported.
2) Wuhan. Population 11M. Workers required to be tested before being able to return to work. Very useful as this study was done not during a rapid rate of rise. Studies done while a population is on the curve are problematic as antibody positivity lags infection by 2 weeks or more.
If one assumes that the 9.6% with past asymptomatic rate that means the true number of infected individuals in Wuhan was about 1.1M and the death rate (using the number quoted in the article) was 2,579/1,100,000 = 0.2%. Double it for assumed under-reporting of deaths maybe? Asymptomatic infections are being reported as 21 times more than “confirmed cases.”
3) Spain study done 4/27 to 5/11. Information from reading the tables put into a Google translator and doing some simple calculations (40202 asymptomatic of which 2.5% were positive, so on). Positive IgG in: 1005 asymptomatic individuals, 46% of all who were positive; 568 with 1 to 2 symptoms, 26%; so on.
Very few had been confirmed by PCR infections (247 of the over 60K).
Using 5% with a deaths/million at the same time of about 530/million (0.05%) and IFR was running, very crudely about 1%.
4) England.(Page 22.) Current week is week 22.
Mid-April numbers for confirmed cases was about 1500/million (0.15%) and total deaths 2 weeks later (presumptively from infections up to that date) about 327/million (0.03%). Plugging in that’s 99 times more infected than confirmed cases and an IFR of 0.2%.
Confirmed cases in Sweden in mid April about 1200/million (0.12%) and deaths 2 weeks later about 250/million (0.025%). About 59 times more with evidence of infection than confirmed infections and an implied IFR of 0.3%.
The bottom line though is less interesting than the whole review inclusive of their speculation that very high rates of asymptomatic infection in a prison population might be due to protective effects from recent common cold causing coronaviruses.