A New Hope?

 

How many people have been infected with SARS-CoV-2 without being detected?

As everyone knows, that is the most important unanswered question in this sordid pandemic to date. Infection with SARS-CoV-2 is often mild or even completely asymptomatic, and with the tremendous gaps in testing in the US it is self-apparent that many cases have gone undetected, both by the authorities and by patients themselves. But knowing this figure is crucial for two major reasons: first, anyone who has been infected with SARS-CoV-2 (and survived) is now presumably immune and can re-enter society with few worries. But second, the larger the percentage of undetected cases turns out to be, the less menacing the virus becomes. Yet despite well over 5,000 scientific papers on the novel coronavirus in the past four months, there has not been a single reliable “antibody study” to date. Until today.

As announced by Ricochet contributor Jay Bhattacharya on the Ricochet podcast several weeks ago, he and his Stanford Medicine colleague Eran Bendavid have been conducting an antibody study to determine what percentage of the population of Santa Clara County, CA is “seropositive” – meaning their blood contains antibodies indicating prior infection with and immunity to SARS-CoV-2. And today they announced their results, and they’re huge:

They report that the actual number of SARS-CoV-2 infections is 50- to 85-fold higher than the number of reported cases.

But let’s back up a second: how was this study performed?

To get this marquee number, the researchers first recruited 3,300 residents of Santa Clara County (one of the heaviest-hit counties in CA) through targeted Facebook ads. These volunteers then provided blood samples and filled out a questionnaire in early April. The blood samples were tested for the presence of antibodies to SARS-CoV-2 (which indicate prior infection and presumable immunity to the disease) using recently-developed, commercial, single-use rapid antibody tests.

Of the 3,300 tests performed, about 1.5% were positive for SARS-CoV-2 antibodies. However, there are two problems with this raw number: first, we don’t know how representative the study population was, and second, we don’t know how accurate the new (and unvalidated) antibody test was. Luckily, the team had both of these bases covered: the former using the questionnaires and the latter using an archive of samples from Covid-19-positive and -negative patients at Stanford Medicine.

Normalizing the data for these confounding factors yielded a range of 2.49-4.16% of the residents of Santa Clara County who have already been infected. Contrast that with the official rate (by PCR testing) of less than 0.05%, and the difference is formidable: namely, 50- to 85-fold greater.

So what does this mean? Very little and a whole lot.

As always, this is still a single study with the usual limitations. Because California has low testing rates but has had coronavirus cases since February, we would expect the gap between confirmed cases and actual cases to be greater in CA than in most other states. Furthermore, the results may also be skewed high since the study population was self-selecting, and people who think they might have had Covid-19 but never got diagnosed might logically have been more motivated to enroll.

But even taking those caveats into account, these results are huge. Even dropping their magnitude by half, they’re huge. For one, they clearly show how easily SARS-CoV-2 infection can go completely undetected. For another, the nominal infection fatality rate based on these figures is somewhere around 0.12-0.2%, which is obviously much lower than either the crude rates of 1% or more endlessly repeated by the media, or even rates like 0.66% calculated in a moment of reflection by Imperial College.

So does this mean we can drop the lockdowns and immediately return to normal? I’d say not too quickly. First off, even with these promising figures, Santa Clara County (and likely most of the West Coast) is still far from herd immunity. This data seems to lay the notion of a “good bug vs. bad bug” to rest. And perhaps most importantly, there has also been another study taking place in New York City with a lot more than 3,300 people: and the result is that nearly 0.14% of the total population seems to have died from the coronavirus within a month. The fact that the absolute fatality rate in NYC was higher than the coronavirus fatality rate suggested by Bhattacharya and Bendavid suggests we should still proceed with caution.

Why is there such a difference between NYC and Santa Clara county? The low fatality rate in Santa Clara County might be a reflection of a much healthier population than NYC. Perhaps there’s some other aspect that makes the virus more deadly we’re not yet aware of, like the route of transmission or infectious dose. Maybe the virus just hasn’t spread enough yet in Santa Clara County to hit the (few) nursing homes and assisted living facilities.

But even with those grains of salt, we can make several confident conclusions:

a) perhaps we shouldn’t take the numbers in this study literally, but we should definitely take them seriously: under-ascertainment is likely rampant throughout the US, even if to differing degrees.

b) the virus is much more complicated than just “this is the deadliest thing since sliced heads!” While it has shown some teeth, it has also now given us multiple signs of not being the monolithic killer it has been portrayed to be.

c) we desperately need similar studies to confirm these results in other regions – and soon. The CDC and several other institutions have already started doing so, but on timelines lasting months. This is too slow in any case, but especially if it turns out that the virus is considerably less dangerous than originally believed. The present study demonstrates that speed is not incompatible with scientific quality if done right.

d) the results of this study need to be broadcast throughout the country – intelligently. Simply proclaiming “it turns out the coronavirus is no more dangerous than the flu after all!” is ignorant and counterproductive. The last 3 weeks in NYC have shown that statement to be untrue. But we can still vociferously point to this study as a sign that lockdowns need to be reconsidered.

d) every policymaker in the US needs to start drawing up much more aggressive plans to re-open their jurisdiction pending other indications that the virus may be less of a threat than previously believed.

If you’ve made it this far, thank you for your perseverance!

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There are 38 comments.

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  1. Full Size Tabby Member
    Full Size Tabby
    @FullSizeTabby

    Stina (View Comment):

    Mendel: “this is the deadliest thing since sliced heads!”

    I giggled… and then I couldn’t stop laughing.

    I liked that line too. Most mass media presents this virus as though it has a near 100% fatality rate. 

    • #31
  2. Instugator Thatcher
    Instugator
    @Instugator

    Stina (View Comment):

    Hammer, The (View Comment):
    I am hopeful that a few things together may change the scene. Showing a relatively low IFR is one, ramping up treatment options if their trial results actually are successful is another.

    I thought empty hospitals would make a difference (at least regionally/locally), since the original goal was to not overwhelm the healthcare system.

    But that goalpost definitely moved.

    Yes, the analysis of hospital beds should have included distinct geographies, as opposed to the entire country at large. Perhaps the number of hospital beds within some distance of the regular census statistical regions. 

    Although, since health insurance is restricted to the state level, counties would probably do as a measure of where people are going to recieve care. So X number of hospital beds/ICU by county analysis would better serve the country.

    No one is going to medivac a Covid patient from NYC to say, Eu Claire Wisconsin. Which is what the national bed analysis implicitly assumes.

    • #32
  3. Mendel Inactive
    Mendel
    @Mendel

    Danny Alexander (View Comment):

    So, this has been happening in my neck of the woods:

    https://www.bostonglobe.com/2020/04/17/business/nearly-third-200-blood-samples-taken-chelsea-show-exposure-coronavirus/

    (And yes it’s The Boston Globe, but the article does manage to do a competent job and keep the progressive axe-grinding to a minimum.)

    This thing is widespread, and grinding along, and it’s premature at best to chalk that up as a positive.

    Thanks for sharing that, I hadn’t been aware.

    There’s far too little information provided in the article to put much confidence in the raw numbers cited. As this entire post shows, just reporting the raw number of positive antibody tests is not even half the battle.

    All that being said, I’ll throw scientific caution to the wind and say: this finding seems much more in line with what I would expect than the numbers provided by Bendavid and Bhattacharya.

    If 1/3 of the population of Chelsea was already seropositive, that would mean they have made it about halfway to herd immunity while having lost about 1 in 500 (80 in 40,000) residents to the virus within a month. That’s a crude infection fatality rate of 0.3%, which is more than twice as high as the upper bound of the crude IFR in the Bendavid/Bhattacharya study yet obviously much lower than many of the rates we’ve seen published elsewhere.

    In general, it conforms with a degree of pathogenicity that’s well above the hysteria promulgated by much of the media, yet still much higher than risk suggested by Jay Bhattacharya and many other prominent dissenters (mostly on the political right).

    I do fear that we’re being unnecessarily hampered in our ability to respond to this virus because our collective psychology is unable to conceive of a risk category between “just a severe case of seasonal flu” and “ZOMG it’s 1918 all over again!”.

    • #33
  4. Steven Seward Member
    Steven Seward
    @StevenSeward

    Mendel (View Comment):

    In general, it conforms with a degree of pathogenicity that’s well above the hysteria promulgated by much of the media, yet still much higher than risk suggested by Jay Bhattacharya and many other prominent dissenters (mostly on the political right).

    “Pathogenicity.”  What a cool word!

    • #34
  5. The Reticulator Member
    The Reticulator
    @TheReticulator

    Wrong thread

    • #35
  6. Gumby Mark (R-Meth Lab of Demo… Coolidge
    Gumby Mark (R-Meth Lab of Demo…
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    Gumby Mark (R-Meth Lab of Demo… (View Comment):

    Two questions occurred to me today. I’ve figured out the answer to one, but was wondering if anyone knew about the other.

    The first was whether flu infections are asymptomatic like COVID? The answer, acccording to a 2014 article in The Lancet is yes, and like COVID the number is substantial.

    The second is whether the CDC includes an estimate for asymptomatic flu cases in its annual model of flu cases? I haven’t been able to find an answer to that.

    I’m trying to figure out if we are making apples to apples comparison regarding infection and CFR between the flu and COVID.

    I think I found the answer on the CDC website.  The CDC does not count asymptomatic cases of flu.

    A 2018 CDC study published in Clinical Infectious Diseasesexternal icon looked at the percentage of the U.S. population who were sickened by flu using two different methods and compared the findings. Both methods had similar findings, which suggested that on average, about 8% of the U.S. population gets sick from flu each season, with a range of between 3% and 11%, depending on the season.

    Why is the 3% to 11% estimate different from the previously cited 5% to 20% range?

    The commonly cited 5% to 20% estimate was based on a study that examined both symptomatic and asymptomatic influenza illness, which means it also looked at people who may have had the flu but never knew it because they didn’t have any symptoms. The 3% to 11% range is an estimate of the proportion of people who have symptomatic flu illness.

    This is also reflected in a chart on the CDC website which shows symptomatic cases as the foundation for its estimates.  I can’t tell from the website whether all of the annual estimates from 2010 on are based on just symptomatic illness but, if so, it potentially changes the CFR if you are using it to compare with all COVID cases including those that are symptomatic.

    For instance, the year with the highest number of flu cases (2017-18) shows a CFR of 0.137 which would be closer to 0.080 if asymptomatic cases were included.  The year with the highest CFR (2014-2015) would go from 0.170 to about 0.097.

     

    • #36
  7. Chris Gregerson Member
    Chris Gregerson
    @ChrisGregerson

    @Danny Alexander:  Unless and until there’s either a proven vaccine or a proven palliative or both, this thing will keep spreading and ravaging.

    Exactly, so what will be different a month or more from now? If the answer is nothing, or close to nothing in terms of a vaccine or cure, then why not take action to relax self imprisonment now. The new protocol should be to isolate those susceptible to the disease and let the rest of the population on with what they want to do. 

    • #37
  8. Hang On Member
    Hang On
    @HangOn

    Mendel (View Comment):

    4. Fatality rates in affected areas:

    As I mentioned in the original post, the infection fatality rate (IFR) of 0.12-0.2% proposed by Bendavid and Bhattacharya doesn’t completely square with the fact that places like NYC and Lombardy, Italy have seen absolute fatality rates of over 0.1% in the course of a month. In other words, in the areas most heavily affected, not just 0.1% of SARS-CoV-2 infected patients have died, but over 0.1% of the entire population has died. Since the maximum percentage of the population that can be infected is well below 100%, these absolute fatality rates point to an IFR considerably higher than 0.12-0.2%.

    This real-world sniff test is an important sign that the IFRs derived by Bendavid et al. should be considered somewhat speculative. However, there’s an equally important lesson: for all of the navel-gazing surrounding the mythical “IFR” of SARS-CoV-2, we need to remember that IFR is not some figure set in stone. We know for a fact that different demographic groups have wildly different fatality rates. We know for a fact that different regions define Covid-19 deaths differently. We can presume that fatality rates will vary based on how overwhelmed a local healthcare system is.

    We can also presume (based on other viral diseases) that more subtle factors like infectious dose and direct route of transmission may play a major role in outcome. In other words, getting the virus by rubbing your nose after touching an infected doorknob might lead to a different course of disease than sharing a bedroom with somebody constantly coughing the disease straight down your throat.

    So the differences in real-world fatality rates should make us skeptical about Bendavid and Bhattacharya’s conclusion, but are not a show-stopper in my opinion.

    I agree it’s not a show stopper. But what is going to be quoted is the 50-85 number and then people are going to run with it and it’s likely high.

    • #38
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