> Cough (7.5%), shortness of breath (1.4%), and fever (0.7%) were all uncommon among COVID-positive individuals
It seems the more important point here is that the majority of the COVID-positive individuals were asymptomatic, putting another datapoint towards the conclusion that there are orders of magnitude more people that have this disease than have tested positive.
We need more studies to gather data on these asymptomatic cases if we want to reopen the economy soon. Imagine if 10+% of the population already had COVID and where immune, we'd be much closer to heard immunity than we currently think.
We already have lots of data that says this is not true. For instance, on the Diamond Princess, widespread testing of a confined group initially found many asymptomatic infections, but the majority of those turned out to be pre-symptomatic, not asymptomatic.
Longer term follow-up found ~25% of infections were asymptomatic.
In the US, we're now doing significant amounts of testing, but we're still primarily testing only those with severe symptoms. Even limiting our sample to people presenting with severe matching symptoms of COVID-19, we're finding ~20% PCR-positive for SARS-CoV-2.
Beyond that, we also have good evidence that the Basic Reproduction Number is somewhere between 2-3. Given a seeding of cases in mid-to-late January, even without social distancing measures, it would be unlikely to have 10% of the population infected yet.
> In the US, we're now doing significant amounts of testing, but we're still primarily testing only those with severe symptoms
Its not a significant amount of testing if we're only testing those with severe symptoms
Here in SF at least there are scores of people I know who all were sick with a "weird flu" at some point from February to now who would all kill for a test to show that they have COVID immunity. These are people who could potentially go back into society and do more work / enjoy their lives instead of being shuttered inside with anxiety
It is currently _IMPOSSIBLE_ to get any kind of test to prove that
We're about as confident that people gain long-lasting immunity as we are that it's a simple respiratory tract infection. It's not beyond question, but to raise the questions without mentioning that we're pretty confident in the consensus answers is either misinformed or misleading.
For some people any statement which is not their preferred taste of doom, disaster and panic is considered misinformation. The post they are complaining about doesn’t even supply a concrete statement:
> These are people who could potentially go back into society
Nobody knows if they could go back or not but the statement that they might is somehow the most dangerous form of misinformation.
The US is doing a significant amount of testing compared to what's available almost anywhere else on the planet. I think Germany is still beating them, but they're basically the only large country that is at this point. Journalists here in the UK have actually been pointing to the US as one of the examples that proves we're the ones failing at testing for a while now.
It depends on where you draw the line for "large", but the US is really not doing all that great (though it has caught up significantly over the last month): https://www.worldometers.info/coronavirus/
Drawing the line at ~5M inhabitants, the following countries/territories have conducted more tests per million population than the US: The UAE, Norway, Switzerland, Germany, Portugal, Italy, Ireland, Austria, Hong Kong, Australia, Spain, New Zealand, Israel, Czechia, Singapore, Canada, Belgium, South Korea, and Russia.
The U.S. has more resources per capita at its disposal, but if everyone is starting at zero tests, I'd still expect smaller countries to be able to ramp faster relative to their population, and all the countries you've listed are indeed smaller than the U.S.
There's the old adage "nine women can't make a baby in a month". Sometimes there are real-world limits to scaling, and the U.S. does have an incredible amount of tests to create.
Yeah. One big reason I'm not counting small countries (that is, anyone much smaller than Germany) is that there's a limited global supply of pretty much every consumable you need to test for coronavirus, and smaller wealthy countries can get a major boost in per-capita testing by just buying up more of the supply. So there's a whole bunch of smaller countries with really substantial per-capita testing advantages over all the larger ones.
I'm not expecting the US to rise to the lofty levels of the Faeroe Islands, but it seems to me that you're grading on an excessively lenient curve here. Germany is the 19th most populous country in the world, and of the top 19, only 3 are bona fide first world countries (the US, Japan, and Germany) , and even one of the marginal candidates (Russia) is beating the US in tests.
And it's not like Portugal, Italy, Spain, or the Czech Republic are particularly small or particularly wealthy.
Where are you getting that data? The 'Our World in Data' site says reporting is inconsistent, but the data they do have shows per-capita Germany, Italy, Belgium, and Austria doing more than the US, and France, the UK, and the Netherlands doing less.
It's certainly possible other countries have passed the US since I last looked, the exact leaderboard does seem to vary depending on things like which country is hitting the biggest roadblocks in their test rollout right now. (Though Italy's lead in total per-capita tests is mainly because they were the first Western country to have their outbreak hit catastrophic levels and they ramped up testing fairly aggressively early on in response to that. I presume their testing wasn't so great prior to the collapse of their healthcare system, since they went from reporting only three cases to disaster alarmingly quickly.)
Are you using some metric other than "per-capita" for tests? I could understand something like "per-positive case", but it's unclear from your comments.
I ask because the data I have shows the US solidly in the middle for the big EU countries over the past month (Germany and France don't show up on the daily graphs)
Since they have coronavirus relatively under control and have been doing extensive testing and contact tracing for months, it's plausible that they've caught most cases. It's wishful thinking to believe the infection fatality rate is an order of magnitude lower.
Randomized testing was still finding 0.6% of the population (outside those otherwise quarantined already) actively infected. This means even in Iceland, less than half of infections were being caught.
Iceland's CFR right now is 0.74% using deaths/recovered (or if you use an ultimate 20% hospitalization fatality rate, around 0.87%). If they missed half of infections, you get an IFR down to under 0.5%, though I'll admit they are doing better by keeping their most vulnerable population from being infected. (note the low infection rate for people 70+ at https://www.covid.is/data).
That's a much more reasonable interpretation of the available data, although there are still plenty of unknowns here, as sibling comments are pointing out. My interpretations the last week are converging on the same ballpark figures.
I'd just like to point out that this is still a far cry from the wishful thinking a lot of people are putting forth, claiming an IFR of < 0.1%, that 30% of the population have already had the disease and that herd immunity is both imminent and feasible.
These interpretations seem less likely to be true today than they did two weeks ago, and even then they were making assumptions on the optimistic side.
An IFR of 0.5% and a hospitalization rate in the high single digits is still a big frickin problem for society, when the disease has a reproduction number greater than 2 (and in the absence of countermeasures, more likely in the region 2-5).
There is a very economical way to figure out this: test the majority of the US population. Sure, the government will have to spend a few billion dollars, but that would possibly save a few trillion dollars in GDP losses. The fact that people and businesses are not requiring this right now from the government makes my head explode!
Half of Iceland's population lives in the Reykjavik area. The rest are thinly spread out over the country - which of which is still inaccessible due to ice (usually until April/May). Comparing this to a densely populated South Korea is incredibly difficult.
Also, different genetics, different climate, different culture, different population density, different connectedness to China, and a 2 orders of magnitude difference in population size. As a rule of thumb, whenever you want to compare Iceland to another country, you probably shouldn't.
The comparison between the two countries is so difficult. The population of Iceland is around 360K, while South Korea is over 51 million (~140x). The population density is in the same order, 3 per Km^2 to 500 per Km^2 respectively (~160x).
I'm extrapolating from hospitalization rates for that reason. Otherwise, you'd get a 0.45% CFR in Iceland.
Looking at the raw data, I don't think Korea ever had below 3% using deaths/recoveries. Iceland is at 0.75% right now. Two weeks after peak, Korea was at a crude CFR (deaths/total confirmed) at 1.1%
With a sample size of 8 deaths, 0.45% is easily comparable to 1.1%. We are talking different infected populations, different healthcare systems, and so forth it’s expected to see that kind of a range. The US had what ~19 deaths out a single nursing home.
Also, deaths/recoveries is the least useful metric to compare countries as people with minimal symptoms recover first, again look at the South Korea or China graphs of the number of infected over time.
But you just ignored the evidence from the diamond princess?
and meanwhile germany's data points toward a similar conclusion as the south korean data
singapore also has a 1.5% deaths/recovered... and it's been higher in the past if you've been following closely (as high as 2%... question is where have all the new cases been coming from?)
icelands data could easily be skewed if they avoided a nursing home getting infected, given their low number of cases, and even there 1% fatality seems plausible.
An average age of 59 on the Diamond Princess (1.8% CFR) is not representative of the general population. This is a substantially more at-risk group.
Germany and Singapore are also missing many infections. A recent serological study in Germany actually argued for 0.4%.
You are correct that IFR is skewed by who the population is and what interventions are done. But then again, so is the often cited flu benchmark (where we have targeted vaccinations of at-risk groups).
(And age is a huge thing to be aware of skewing the data. e.g. the pediatric IFR from covid is on the order of seasonal flu)
Yes, I agree it is preliminary and not too much should be drawn off it.
Every country is missing large numbers of cases, so CFR doesn't mean much - randomized testing is what is needed.
Imperial College's paper (linked above which gives a 0.7% population IFR) uses Diamond Princess as an input. The relative risk ratio they give for someone age 70 (mean age on Diamond Princess) is something like 4.5x (IFR ~3%), so you'd natively guess about 80 deaths from the 2,666 passengers.
The Diamond Princess likely had a very atypical (older) population than the general population though.
The CDC is currently estimating an R0 of 5.7, which has likely been repressed by shelter in place orders. But even if it was only 4 before any interventions, there's still a chance there there are closer to 20M cases in the US rather than the 660,000 now.
My point is just that there's still a lot we don't know, and small changes in our understanding could implicate massive changes in the reality on the ground.
5.7 in Wuhan. That could be true for NYC as well, but is far less likely to be true in the rest of the lower density country. That said I agree with you that infections are likely in the 10M range.
> The Diamond Princess likely had a very atypical (older) population than the general population though.
This would dovetail with the fact that places like sub-Saharan Africa and India haven't been hit very hard yet; they also happen to be _very_ young compared to Western countries.
I don't know if people are aware of this, but there's an increasing suspicion that countries still using the BCG vaccine for tuberculosis might be seeing [1]way lower infection rates.
If that's the case, it'd explain why developed countries are bearing the brunt of the pandemic. A very interesting example of similarly developed countries with comparable (but not equal) populations and population density, but different vaccination regimes are Portugal and Spain. Looking at their [2]respective [3]charts, the differences are stark. A similar difference can be seen between Ecuador and Argentina, even though the Greater Buenos Aires area is very population dense.
If this pans out, following the trends and [4] this map would validate that hypothesis.
The map in [4] suggests that every adult in France would have received the BCG vaccine, as they only stopped in 2017 (and receive it as an infant). There are so many confounding variables here, I don't think anyone should be deriving any evidence by looking at the case charts like that.
I agree. I'm not arguing this is 'fact', just an interesting correlation - until proven anything stronger than that - that would explain why some countries got hit worse than others.
As someone else pointed out, the BCG needs a booster. While growing up I was given the initial one and two boosters. Most countries stopped doing that, which could also explain the effect. There's also the matter of which strain was used to create the vaccine: apparently some strains were better at fighting tuberculosis than others, so the same might apply (if the BCG vaccine actually made a difference) to COVID-19.
Anyway, my objective wasn't to start a conspiracy theory. We already have enough of those going around.
Thanks for the 4th link, I couldn't really understand the really sad images and videos coming out of Guayaquil (Ecuador) when comparing them to the rest of South America. Apart from Buenos Aires there are also big metropolises like Sao Paulo or Rio de Janeiro where you couldn't see the same things that were happening in Guayaquil. That vaccination map for South America partly explained the difference for me, at least by correlation.
Yeah, what's happening in Ecuador is incredibly sad. My whole family is in Argentina and I was worried sick for them for a while... who knows, this BCG theory gives me some hope things will be better for them than they are here. Fingers crossed.
"there's an increasing suspicion that countries still using the BCG vaccine for tuberculosis might be seeing [1]way lower infection rates"
Obvious to me, perhaps dumb, question - if countries that were slower to change vaccination regimes have some advantage, then wouldn't we see older people in the countries that did change also at an advantage? Yet all reporting seems to be that older people are harder hit.
BCG vaccine requires a booster every few decades, so it becomes less effective over the years. Apart from health issues related to age, this would explain why old people are at higher risk.
>We already have lots of data that says this is not true... Longer term follow-up found ~25% of infections were asymptomatic.
Based on other available data the severity of the Covid symptoms are dependent on both the length of exposure and total exposure to the virus. A cruise ship with recirculated air likely means prolonged exposure to high concentrations of the virus that won't fit most other patients exposure.
"Initial estimates of the basic reproduction number (R0) for COVID-19 in January were between 1.4 and 2.5,[381] but a subsequent statistical analysis has concluded that it may be much higher.[382]"
> Study results revealed an overall pooled prevalence for asymptomatic carriers of 19.1% for any type of influenza, 21.0% for influenza A, and 22.7% for influenza A(H1N1). For subclinical carriers, the overall pooled prevalence of was found to be 43.4% for any type of influenza, 42.8% for influenza A, and 39.8% for influenza A(H1N1).
What's "significant amount of testing"? In Santa Clara country, for example, 16585 tests have been done[1], that's less than 1% of the population. I know from personal experience of several people that it's next to impossible to get tested if you don't have very severe symptoms. Positivity rate is 10.8% - over people which are sick enough to get tested.
> Given a seeding of cases in mid-to-late January, even without social distancing measures, it would be unlikely to have 10% of the population infected yet
This sounds like circular logic - how we know it indeed started in January and not before? How do we know how many had it asymptomatically if we didn't test 99% of the population? We're making assumption based on knowledge gathered by select sample of 1% chosen by severity of symptoms - how can we make conclusion about how it works in the rest of the population and what's the dynamics there?
> This sounds like circular logic - how we know it indeed started in January and not before?
Indeed, without serological testing, we're necessarily making some assumptions. But there's other evidence that points this way, too.
Genetic analysis based on samples from around China and the world point to a single index patient in Hubei Province around late November. China's case tracing has not gotten to an index patient, but it appears to be pretty close, and also points to an initial case around the same time.
If there was a single case in late November, then we'd expect a few hundred cases by January. Some number of these will have traveled, but many would not.
While the US clearly had major blind spots in testing, we were explicitly looking for symptomatic cases from China. The first detected case was Jan 21. There may have been a few earlier, but genetic analysis of cases in Washington point to an index patient in the Seattle area around that time.
It's worth noting that the German study in Gangelt was done because it was considered a hot-spot - this wasn't an attempt to estimate the infection rate across the country. It does however demonstrate that even in places where lots of people have had it, herd immunity still looks a long way off.
However, if we apply the estimated mortality rate (0.37%) based on that to 11500 deaths in hot-spot New York, it would amount to over 3 million cases, again roughly 15% of the population.
Now perhaps Germans are so much healthier and their healthcare is so much better, making their real mortality rate so much lower. It's all speculation at this point, either way.
It wasn't a study of representative German households, but just those in a Gangelt. A small town that was basically the ground zero for the epidemic in Germany. Those numbers do not generalize to Germany as a whole, let alone other countries.
Also, the particular antibody tests they used appears to have a much higher FP rate than they claimed. Another study found 4% FPs rather than <1% like the press release for the Gangelt testing claimed.
It does generalize to my point, because we're talking about the basic reproduction number of the virus. It is not supposed to vary dramatically across populations.
If COVID-19 can spread to 15% of that particular population within that timeframe, and the estimate of a basic reproduction number of 2-3 predicts that this is not possible, then the estimate must be wrong.
> Also, the particular antibody tests they used appears to have a much higher FP rate than they claimed. Another study found 4% FPs rather than <1% like the press release for the Gangelt testing claimed.
Perhaps, but that doesn't put much of a dent into the results.
R0 isn't a fundamental constant, but just an estimated average. It's affected both by human behavior and random chance. In this case, the town had a very early superspreader event, which shifted them far ahead of the curve.
A FP rate that high will make a very significant difference in the results. (How large a difference depends on whether the 15% is a raw number or if it's been adjusted based on the expected test specificity and sensitivity. One would hope nobody would just report the raw numbers. But since all we've gotten from this study is basically a press release, expecting scientific rigor seems ill advised.)
> We need more studies to gather data on these asymptomatic cases if we want to reopen the economy soon.
I don't understand what's so hard about measuring population infection rate.
Assuming the population infection rate is between 1-10%, we would only need to do around ~500 randomized tests to achieve a 95% confidence interval of +/- 1%.
For example, let's say we tested 500 random NYC residents for COVID and found that 10 were positive -- a 2% infection rate. The standard error (binomial approximation) for this sample is 0.6%. So, by doing a mere 500 randomized tests, we have a 95% confidence interval of 0.8%-3.2%.
Given that overall population infection rate is so important in planning for re-opening, why are we not doing randomized testing in hotspots like NY on a regular basis? Am I missing something?
Ah, I see. Is it true that these tests are a work in progress and the false positive rate will drop in the coming weeks/months? If so, it seems like we should start doing random sampling and saving the samples for future testing (so we can see how infection rate trended). This could be invaluable data that we're losing.
It would be the test using QPCR to amplify the virus genomic material from samples collection. That test is a lot more specific though there can be issues with how the samples were obtained.
Great question, and the one I struggled with a lot. When I finally saw the light it was like find the glasses I have misplaced three days ago. Here:
1. Efficacy of a test can only be judged against specific priors, not any possible circumstance.
2. Likewise, the goal of a test is to assist in making a particular decision, not all possible decisions.
Specifically 9% false positive rate is not useful for testing general population where effect size is expected to be on the order of 1-10%. However it is very useful in testing groups where expected effect size is much larger for example "all symptomatic people who came to hospital" at 50% or "all contacts of a known case" at 20%. The numbers are made up to illustrate the math.
Importantly our goal is not to detect all infected people ending the epidemic in XYZ days flat, it is to reduce the viral spread factor below 0.5, halving the epidemic every XYZ days. Thinking in absolutes is counter-productive.
Bearing all this in mind, the test can be useful. For example let's take a pool of people who are symptomatic, and say we expect 50% were in fact infected. The false positives (9% of the healthy 50% == 4.5%) will be outnumbered by true positives (* 99% of the infected 50% == 49%). So now you're looking at 49% vs 4.5%, a respectable 11:1 accuracy under the given priors. Not bad, for some applications.
And here's one good application: test a group of people from the pool of the currently symptomatic, quarantine them for two weeks, then release them into the wild without self-isolation rules. They will all be healthy due to quarantine, and 10/11 will be immune. If we keep releasing 91% immune groups of people into the general population the virus will die off.
* the antibody test I was referring to has 99% true positive and 9% false positive rates.
I mean, it's not great, but imagine if it was so cheap and fast we could test the entire population on a consistent basis.
In a hypothetical scenario of 1% of the population actually having COVID and 9% testing false positive, you could ask all 10 positive results to self-quarantine and that'd probably be a pretty effective way of shutting down the virus without asking the whole population to stay home.
Once the virus spreads more, it gets even more reasonable. (If it gets crushed more and there are very few cases, it does get a little extreme to call it useful though).
You need to also take into account the false positive too (1% iirc). Under your priors a group of 100,000 people will have 1000 infected and 99,000 not infected. Specifically:
So going through your plan will isolate 8,910 + 990 = 9900 people (9.9% of the population), catching 990 actual cases and letting loose 10 cases.
In other words we can isolate 10% of the population and reduce the number of carriers by 990:10 ratio. This seems more effective than the current 100% isolation. All we need is the tests now.
There's good reason to suspect strong regional variation, which means that finding a representative sample of 500 is hard. But that would still be technically possible (look at South Korea and China); we clearly lack to political will to do proper testing in USA.
Well we need to measure if people have it and if people have had it. The have it test is currently $3000 a pop, and the had it test is hitting accuracy road blocks [1].
Amen. The whole scenario seems like a bad statistics lesson.
Happy to change my mind -- but there simply hasn't been any effort to use population testing in this way -- which is one of the only useful forms of testing. Otherwise, why test people in the hospital? It doesn't change treatment. Finding asymptomatics is actually useful -- and random sampling seems critical for understanding whether we are simply fucked or actually fubar'd.
Agreed generally, but my understanding is that testing people in the hospital does have value. Just for one example, if you're negative, then you can be placed in a non-Covid wing (otherwise you're liable to catch the coronavirus on top of whatever flu or whatever you actually have), staff don't need to use precious PPE when treating you, etc.
Another explanation is that the exact infection level isn’t important when the observed all-causes fatality rate is double [0] its seasonal average and everyone is therefore busy firefighting the immediate and obvious problems.
I see plenty of motivation in some states to try to minimize the potential magnitude of the problem, or avoid "bad" numbers of infection rates.
I don't see a motivation for "milking" the crisis. The current actions by governments are restricting economic activity and revenue streams. Many states only make money on sales tax and that's probably not going super well right now.
I agree that there is hesitation to do widespread testing, but I don't know which government would intentionally cause a recession using this virus as an excuse.
A month ago I would’ve agreed with the article you linked to. I’m suggesting that right now there’s probably enough information in merely the death count to say “we need to do something! Argh, panic!” — and governments, regional and national, are doing just that worldwide.
What would a government, and specifically a ruling party gain from slowing down the economy and alienating its people (which always happens to some non-zero extent when you quarantine and constrain people)?
Milk it by tanking GDP? I'm sure that will do well for government revenue this year.
I'm sure Trump was just itching to shut down international travel, recommend reduced productivity, and mail everyone checks. He just needed a good excuse.
My bet is that it's much more than 10%. In NYC I think it's at least 20%-30% by now. Obviously, I don't have any more data than what you can find online, but anecdotally, I think one of my sons had it; he most likely got it from his piano teacher who exhibited all the Covid symptoms (fever, dry cough, loss of smell), but was never tested. I think at least myself and my wife got this from him as well; likely his two siblings, but they were completely asymptomatic. Our kids' nanny has Covid now (tested at the hospital), and her son too (he's in ICU). I have other anecdotes, and no, I don't have systematic data, but I believe at least 20%-30% of NYC has had Covid by now.
Another point of reference is this: according to [1], a back-of-the-envelope estimate of the number of Covid-infected is 100 times the number of dead. It's about 8000 now in NYC, so about 800k infected. That's about 10% of NYC's population, exactly the number you mentioned.
15% of pregnant woman in NYC that delivered had active Covid infections (https://www.nejm.org/doi/full/10.1056/NEJMc2009316) detectable by PCR. It's highly unlikely under 20% of the population was infected. I'd bet closer to the 30% in fact.
Does this study change your mind on that 20-30% figure? After all, the study measured a 35% infection rate for people living in close quarters with other infected individuals. Surely, the overall population would yield a much lower infection rate than this?
As a counterpoint, I saw a lot of people supporting the idea (with similar anecdotes) that there was a huge outbreak in California in December before we were even testing for COVID at all. Which was empirically demonstrated to be untrue by the later Seattle Flu Study testing of the viral genome.
That said, it's a whole lot more likely that you're right than the December-California-epidemic folks, just saying that that type of anecdotal evidence is fairly easy to come by in the winter.
Also from bk. I'll anecdotally second this sentiment. I even got sick with a terrible cough for 2 weeks in the end of February and lost my sense of smell for 2-3 days. My roommate didn't get sick, but I just spoke to my neighbors and they mentioned similar symptoms the week after me.
Do you think this happened in early February? If so, I agree. Sort of.
The problem is that according to the data, none of us that had the "weird cold" in February ever went to the hospital and tested positive for COVID-19. So it was probably something else, but it is weird that there was a widespread "weird cold" in New York right before the COVID-19 cases, isn't it?
Or the majority were presymptomatic. They don't give a timeline of how long after the first cases in the cluster were detected that the testing took place, so these individuals might have been in the first week of the infection.
> Imagine if 10+% of the population already had COVID and where immune
By some estimates we need over 80% of the population to get it in order to achieve herd immunity, so if only 10% of the population has it we're not very close at all to this being over.
The first 10% to be infected, are also likely to be the highest spreaders (people in close contact with a high amount of strangers). The effect should hopefully be higher than 10% reduction in the reproduction rate.
There are several variables here but it's also important to keep in mind that the 36% positive rate in this cohort presumably does not include people who had the virus and recovered.
A Diamond Princess ex-passenger died as late as March 24. The first patient was tested positive on February 1, and the ship was quarantined on Feb 4, and everyone left the ship on March 1. It doesn't seem unlikely you can die from this virus a month (or even two months?) after testing positive. Or maybe I'm missing something?
Why? 23 days for the longest time to succumb isn't surprisingly long. small exposure, healthy person / lucky genes, good medical treatment, but not quite healthy/lucky/good enough.
Well the longest possible according to this data would be 54 days. We don't know the identity of patients who died, could be one of the people who tested positive on Feb 1 (or sometime in early Feb).
It is very important to distinguish asymptomatic (does not have symptoms and will never develop them) from presymptomatic (does not have symptoms yet, because the test caught the infection sufficiently early). The people in this study are mostly presymptomatic, and will develop symptoms later.
Asymptomatic means the person had no symptoms at the time of the test, and possibly never developed symptoms. They could also have been presymptomatic, in which case they subsequently developed symptoms after the test. This study didn't do any follow-ups to differentiate.
That makes sense and that's a useful word, thanks!
I've heard it takes about a week before you get symptoms and then they last for 2 days on average. So even if everyone developed symptoms only 22% of people with the disease would have symptoms on a given day...
0.2% of nyc has already died from Covid-19. That will probably get to around 0.4% or so. This is a lower bound for lethality of the disease.
We also know from contact tracing and mass testing instances that only something like half of cases stay asymptomatic forever. It is likely that the people in this study are either asymptomatic because they no longer have the disease or because they haven’t developed symptoms yet. Also, they might just not be recognizing their symptoms. Even without covid-19, way more than 7.5% of homeless people are typically coughing.
> Imagine if 10+% of the population already had COVID and where immune, we'd be much closer to heard immunity than we currently think.
We’d be about 1/7 the way there. Meaning we’d see another 6x current deaths to get through this. That’s not even close to acceptable.
And the 10% aren’t realistic, anyway. At least nationally. Assuming nationwide rates of 10%, then scaling up by death numbers, would put infection rates far above 100% in New York.
Why would the rest of the country have the exact same percentage of infected as New York City, the only place in the country where most people don't own cars, and almost everyone takes cramped public transportation. NYC clearly has a MUCH higher infection rate than anywhere else in the country.
> Why would the rest of the country have the exact same percentage of infected as New York City?
I didn't say that. In fact, I said the exact opposite: to get to 10% nationwide while explaining the rather large per capita differences in deaths, you'd need either negative infection rates in some places (say, Alaska), or >100% infection rates in hotspots. Neither is possible.
(Regarding the Headline) Surely you mean 35% of homeless who were tested. We can't say for certain that the sample is an exact representation of the general population of the homeless in Boston.
"Upon observing a cluster of COVID-19 cases from a single large homeless shelter in Boston, Boston Health Care for the Homeless Program conducted symptom assessments and polymerase chain reaction (PCR) testing for SARS-CoV-2 among all guests residing at the shelter over a 2-day period. Of 408 participants, 147 (36.0%) were PCR-positive for SARS-CoV-2"
The key thing here is they tested people from one single homeless shelter. Is this one homeless shelter in Boston representative of all homeless shelters in Boston? There is not enough info in this article to make that assumption. Nor is it enough information to make generalizations about the populations of the homeless who do not live in shelters.
> We can't say for certain that the sample is an exact representation of the general population.
I actually think we can say for certain this is NOT a good representation of the general population. The general population has not been living in close quarters with multiple COVID positive people.
This is fair feedback on the headline, as the linked article does NOT make that claim. Instead, it makes the same point as @zadkey that "illustrate the rapidity with which COVID-19 can be widely transmitted in a homeless shelter setting and suggest that universal PCR testing, rather than a symptom triggered approach, may be a better strategy for identifying and mitigating COVID-19 among people experiencing homelessness."
The Boston Globe this morning ran an op-ed advocating testing a random sample of residents to get a better sense of community-wide infection rates.[1] It's amazing that this needed to be stated in a major market newspaper four weeks after most business and school was stopped due to the virus. Goverent is doing some interesting things, random sampling apparently isn't one of them, for reasons it's hard to understand.
Everybody knows this, but the US healthcare system was so unprepared for this that there simply weren't (aren't) enough test kits around to do a proper study. Even people who have the symptoms struggle to find a place that will test them in some cities.
It's probably low prevalence in the population based on our understanding of when covid-19 first appeared.
The UK has some randomized seriological surveillance and it's about 13% prevalence. That matches with the pregnancy study of 15% prevalence (which may be enriched due to hospital visits).
That's not inconsistent with the Seattle timeline. But for sure genetic analysis of the viruses would reveal the source.
"Stanford’s virology lab, looking retroactively at some 2,800 patient samples collected since January, did not find the first COVID-19 cases until late February — from two patients who were tested Feb. 21 and Feb. 23"
The idea that nobody has thought to do them seems absurd. I'm getting more and more suspicious the longer we see no results from randomized studies in US cities - it's getting more and more likely that either the tests are turning out to be inaccurate, or the results are turning out to be problematic.
But either way folks are probably being very careful because the results will have significant impact.
In the developing countries without universal health care, there are government health workers, who are into vaccination, prevention, etc. In situations like this, these workers are mobilized for random testing. However, in the states, I don't see such a workforce. It is mostly done by volunteers, such as that Stanford drive in testing.
NYC has something like 15x the death rate per capita of the the US ex-NYC. Even if you think that NYC is approaching herd immunity (debatable), that means the rest of the country is only 5-10% of the way there.
Many of those people testing positive were presymptomatic (some even developed symptoms during the follow-up period). It typically takes several days to develop symptoms.
Also worth considering is the period mentioned in the correspondence is one in which there was very fast exponential growth. When more of the positive cases are recent, it's only normal that they don't show symptoms yet.
Besides the test used showing a lot of false positives, the population of pregnant women looked at had a much higher rate of symptomatic infections 1 month ago than the entire population does today... we should be very cautious about concluding much from that sample.
That is mind-boggling. You don't test positive if you've cleared the virus, either, so that 14% is actually a lower limit because some portion of them might have had it long enough ago to clear. I don't know what pregnancy does to the immune system -- but we do know that these women were all young enough to give birth (likely <40 years old).
That's really not that much, I would expect the infection rate in NYC to be anywhere between 10-20%, that makes sense given the numbers we're seeing in hospitals.
I really don't understand why people immediately want to discredit the idea that US patient zero was not the first in US. Or that this virus was around earlier and that there are limitations in contact tracing capabilities.
This seems to have the toughest aspect of gaining consensus.
Need antibody tests now, and ones that work reliably.
first covid death (feb 6) is nearly a month earlier than previously official first US covid death (feb 26), and also from community spread. suggesting it was in the bay area at least half a month earlier than that.
Which is something people have been discussing for weeks here in San Francisco, including RNs that have been thinking back to early January.
So how do we move this from a collection of anecdotes and immediate dismissal to prioritizing some antibody tests to the bay area. Possibly reopening it faster and keeping it open.
Just acknowledging the possibility can influence public policy, and the research that some other people want to see before they can acknowledge it.
first covid death (feb 6) is nearly a month earlier than previously official first US covid death (feb 26), and also from community spread. suggesting it was in the bay area at least half a month earlier than that.
Why is that the most preposterous hypothesis that doesn't warrant any review?
We have no reliable antibody tests, and then won't give the unreliable ones people because they are so rare. So where does the confidence come from that we can rule out this possibility?
What if this is already the "second wave". Something with lower symptoms wouldn't have warranted checking for a new strain, complications and deaths would fit into the normal distribution of last fall's flu season with no outlier spikes. We would then be deep into the second wave and can't even test most for people currently exposed to it, much less having already been exposed. This is enough not to dismiss the hypothesis.
> Why is that the most preposterous hypothesis that doesn't warrant any review?
It's not. What is preposterous is advancing the hypothesis without any meaningful evidence, to the detrement of more useful topics, or even useful discussion of the same topic.
It's one thing to be an epidemiologist saying "hey, let's see how we can double check the exact origins of this", which is a potentially useful line of inquery. It's another to take some hypochondriac's third hand retelling of someone else's flu symptoms back in December and jump to the same untested unproven unscientific fear-driven conclusions they did on little more than their wild speculation. Even if they do somehow end up being right, they'll be right in the "broken clock is right twice a day" sense rather than a bringing anything useful to the table sense.
So far, alternative suggestions as to the virus's origins have looked more similar to the latter - with perhaps some undertones of (completely understandable and expected) attempted political diversion (I sure wouldn't want fault for this mess hanging on my neck!) If you have some epidemiologist's proposed study that you're trying to drive funding towards - something that even remotely looks like the former - it'd be a welcome breath of fresh air on the topic, and I'd suggest sharing that as a far more useful and constructive way of advancing the hypothesis.
People don't advance a hypothesis without enough anecdotes to say "look over there"
People like you can help fill gaps of savants saying "this isn't a constructive methodology but it isn't inherently without merit" versus "I don't want to believe that because thats not consensus right now" versus "hm thats interesting maybe worth a look sometime before the 2022 congressional committee to fund the epidemiologists?"
> We would have seen a higher death rate if the virus was here earlier.
> What if this is already the "second wave". Something with lower symptoms wouldn't have warranted checking for a new strain, complications and deaths would fit into the normal distribution of last fall's flu season with no outlier spikes.
I think you are misunderstanding what wave means here. It means an earlier not as deadly strain/conditions had already occurred, just like in the Spanish Flu in the spring versus the fall deadly resurgance, but at lower orders of magnitude. The strain itself doesn't have to be different if the co-morbidities were different - such as different opportunistic viruses or bacterias being present.
I specifically wrote "/conditions" to predict that specific rebuttal.
The virus could be the same, the opportunistic additional virus/bacteria could be different.
just like HIV causes no symptoms, until your immune system is down and a different infection (caused by bacteria or virus) kills you. Maybe even a normal "gut" bacteria, or something in your body usually present, is what kills you.
There is research pointing to Sars-Cov-2 attacking T cells directly. Instant AIDS.
In the fall and early winter, there could have been different variables that made it less debilitating and deadly than the spring variables. And in that case the fall and early winter deaths and pneumonia would have blended in to normal distribution.
> People don't advance a hypothesis without enough anecdotes to say "look over there"
They do so all the time. They'll advance hypothesis even without ancedotes on the vaguest of hunches, and bias towards Type I pattern recognition errors (https://www.youtube.com/watch?v=1AjLmU0Sfu4). This is in fact half the problem - why anecdotes alone aren't terribly useful, and must be treated with so much caution and skepticism. Needless to say that primary caregivers and epidemiologists are already drowning in such ancedotes as well, they're not exactly aided in us adding more noise to the discussion where they're not even looking.
There is such a thing as "enough anecdotes" - when they're numerous enough to rise to the quantity of being actual statistically measurable data. But you yourself practically admit such ancedotes fail to meet that bar by suggesting some secret first wave that's indistinguishable from the regular flu season in the data. Either the ancedotes form useful data or they don't - you can't have it both ways.
"Enough ancedotes" led us to our current conclusions, not this hypothetical secret first wave stuff you suggest. "Enough ancedotes" say "don't bother looking over there, we've figured it out", on account of failing to rise to the standard of useful statistical data to contradict our current conclusions. We've been checking, and are going to continue to check, for contradictory evidence anyways - despite the lack of usefully contradictory ancedotes - just for thoroughness, given the size, scope, and impact of the epidemic.
> People like you can help
...by spending my time embracing social distancing, masks, calming the histronics of hypochondriacs, and by guiding doubt towards those who deserve it the most and where it's going to be the most useful, actionable, and effective. We have a number of botched pandemic responses - exacerbated by censorship, political manuvering, misinformation, and so much more - with direct lesson to teach us about how to properly react next time, and parts of our government and geopolitics we need to fix. There are ongoing problems like the lack of randomized testing to give us an idea of where we're at now. Things we have hard data for. Things we can change by voting or lobbying our congresscritters or influencing debate or through direct individual action to support those in need, or helping the helpers.
But entertaining the conspiracy theorist fueling armchair "hypothesizing" about alternative virus origin stories? Does our political and epidemic policy really change if, say, technically this started several months earlier than we realized in Russia? This "maybe hypothetically a second wave!" still kicked off in China, regardless. I don't see the hypothetical policy changes. I don't see the upside. I don't see how this hypothesizing "helps". I think, in fact, that it hurts - by further stressing people out when they're already stressed out (which has real health impacts), and driving them towards unreasonable levels of distrust in the scientific method and community (which has been very upfront about the many things that are actually properly unknown about this epididemic). I think it's the same nonsense that leads to the antivax mindset. It reminds me of gaslighting.
And so, on this topic, I think helping means poking all the obvious holes I can in said "hypothesizing" - as a perhaps useful example to follow - as to how one might differentiate this armchair debate from actual reasonable doubt. On the off chance that I'm wrong, it invites a proper useful rebuttal or counter-argument. On that note, I again emphasize:
>> If you have some epidemiologist's proposed study that you're trying to drive funding towards [...] it'd be a welcome breath of fresh air on the topic, and I'd suggest sharing that as a far more useful and constructive way of advancing the hypothesis.
But since you seem to be hypothesizing about 2022 congressional committees instead of any of the stuff actively being looked into while half the bloody economy has been put on hold, I won't hold my breath.
> On the off chance that I'm wrong, it invites a proper useful rebuttal or counter-argument.
Thanks for the invitation. Lets see where the goal post is here
> Either the ancedotes form useful data or they don't - you can't have it both ways.
In this case they do if you test for antibodies. We can't test for antibodies. It has been gaslighting from you to willingly ignore what the limitations of data are and how to solve them, if this is a term that bothers you make sure to look at it from my perspective as well, are you recycling this argument for everything epidemiologists aren't currently doing and everything that doesn't currently have consensus? Or is it just for me, either way I don't think you have factored in the exact argument here and I'll get to that:
> Does our political and epidemic policy really change if, say, technically this started several months earlier than we realized in Russia?
Yes it does, because it means the bay area is safe and can change its own policy. I don't see how you missed this in your effort to convert the word hypothetical into a pejorative.
> But since you seem to be hypothesizing about 2022 congressional committees
This was hyperbole but also likely what is going to happen. After the dust has cleared, Congress makes committees to see what exacerbated dysfunction - and they may then notice this discrepancy in the bay area as well. Meaning that it has nothing to do with a national policy decision because my hypothesis is relevant on a local level for the Bay Area as this whole thread made abundantly clear. For everywhere else it is merely interesting.
The difference is that one major economic center of the US can resume with a level of certainty and forward guidance.
My primary point of this exercise is that you are rejecting this possibility out of principle, recycled from rejecting a wide universe of possibilities, without factoring in what this one actually could change. So I hope thats clear now.
>> Either the ancedotes form useful data or they don't - you can't have it both ways.
> In this case they do if you test for antibodies
I'm not sure if this is a disagreement in framing or what. I encourage retroactive testing for antibodies, however, I do so in spite of ancedotal data failing to form useful data that suggests such tests will contradict the currently understood origins of the virus. Finding antibodies for samples taken in December, for example, sounds like it would be interesting and potentially useful science - for the very reason that it contradicts the data formed by said ancedotes.
>> Does our political and epidemic policy really change if, say, technically this started several months earlier than we realized in Russia?
> Yes it does, because it means the bay area is safe [...]
Wait what? How the heck would that conclusion follow? If there was some safer first wave originating from outside of China, and we're now in the middle of a dangerous second wave, then we're still dealing with a second dangerous wave! Are you... assuming some confounding variable is what's causing this to be dangerous, and assuming said confounding variable isn't present in the bay area? On what basis? For how long? There are confounding variables - the age of the victims, societal mask use, possibly weather conditions, and more to boot I'm sure... but I'm completely unaware of any that would suggest the bay area is somehow "safe", regardless of the origin of this thing. If anything, it might suggest that the confounding variables are changing to make this thing more dangerous.
The worldwide spread is giving us a huge sample size under varying conditions, the better to understand said confounding variables. It seems like a bit of a stretch to assume that any additional hypothetical early sample points are going to give us terribly much more insight as to what said confounding variables are, when we've turned half the planet into an involuntary testbed already - if there are indeed such confounding variables that would make parts of the world "safe" - and a stretch beyond the breaking point of common sense to assume that those insights will automatically make the bay area specifically safe when this thing is killing people just up the coast.
> [...] and can change its own policy. I don't see how you missed this in your effort to convert the word hypothetical into a pejorative.
Hypothesizing is great, and a fundamental part of the scientific process - which is why I don't wish to sully the term by conflating it with biased wishful thinking and conclusion jumping. I also haven't missed e.g. the bay area and California at large joining the Western States Pact, and their ability to make/change their own policy.
What I have completely missed is even the hypothetical logic train to "the bay area is safe". I have attempted to guess at it above, but it has so many holes that I fear I must be (unintentionally, I promise!) strawmanning you - and that you clearly must have some other train of logic leading to that conclusion that I'm simply failing to synthesize on your behalf. Perhaps you stated it elsewhere and can simply point me to the right part of that thread?
To recap, a lot of people think the bay area should have more deaths and more cases, similar to Seattle. The proactive stay-at-home orders are interesting but still seem to be too effective - as if maybe more people are immune already. California has failed at testing for active cases, but there also aren't many deaths in the bay area. Is it completely from the proactive lockdown, or maybe the anecdotes about similar symptoms earlier than expected have merit.
"Safe" meant "maybe we don't need a lockdown as long either" or that "a larger portion of the population than you might think doesn't need to be in lockdown" and that it loses utility to keep them in lockdown. At the very least, it would allow for the Mayors offices in bay area counties, and the Governors offices to take a holistic view at the blanket order - or FUTURE blanket orders when this flares up again throughout the year. But only after antibody tests occurred.
It seems to indicate this disease infected a lot more than what we see, and maybe mortality rate is lower than what we currently assume. Maybe it's around 0.1% to 0.5%.
So is it possible this disease appeared in China way before December, maybe a few months back. Then in December it reached a saturation point (i.e. infected hundreds of thousands of people, so deaths started to get noticed by respiratory specialists). Then around January/February this disease spread to the world, but it went unnoticed. Then in March it reached the same saturation point that the deaths became too many not to notice.
We know from genomic analysis [1] that "The common ancestor of circulating viruses appears to have emerged in Wuhan, China, in late Nov or early Dec 2019."
It's fun to speculate about other possibilities, but I personally like to stick to what the science tells us.
Very interesting, thanks for the link. In that case, how should that change our mental model? It seems like more people in Western countries are infected than visible, doesn't that mean the same would apply to China? How would it spread so fast in December?
US is generally not testing people with mild symptoms to significant symptoms. We are mostly telling people to stay home until you have difficulty breathing.
That’s why are deaths to cases ratio is so high before the new infection rate has dropped to near zero for weeks. Adjusting for infection vs death curve in China US deaths from people currently infected are likely to hit in the ballpark of 50k to 100k. Assuming we get new infections under control in the next few days and keep it low for the next month.
That report appears to based on genomic analysis, which obviously only includes cases that were tested, not undocumented transmissions before the discovery.
The article you cite says:
"The common ancestor of circulating viruses appears to have emerged in Wuhan, China, in late Nov or early Dec 2019. Accordingly, the majority of sampled and inferred ancestral cases were located in Asia during this early period. "
It samples cases that are detected after the fact, from which they are able to reconstruct the phylogeny with impressive clarity. If you are not familiar with the work of nextstrain.org, I highly recommend it, they are brilliant.
Even if I were inclined to believe such statements without a link, this doesn’t make any sense.
For Google searches to spike, information about the disease would need to be public knowledge. And at that point, you wouldn’t need Google search traffic to prove anything.
So there was 0 searches, then ~90 searches suddenly? Yeah no sounds like just noise. There will always be some people searching about corona viruses, they always existed and we always knew of them.
to respond to everyone at once:
I didnt include the link because this is public knowledge anyone can access. It was provided below but the original search doesnt work anymore. it literally worked till today. I do have a picture of it I had taken, but HN doesnt provide the ability to share pictures.
It was a search for 'coronavirus' in china between 8/1/2019 and 01/01/2020. It showed a spike of 100 on September, 21, 2019 and another in December of 2019. However, this search no longer works. All of the traffic came from Hubei Province.
what gnulinux said about '90' searches is wrong. This is 'Google Trends is a search trends feature that shows how frequently a given search term is entered into Google's search engine relative to the site's total search volume over a given period of time.'
IAmEveryone comments are just all wrong. this doesnt mean 'it would have had to been public knowledge'.
It means searches on google were performed on that term, specifically in Hubei Province. and yes, google is 'blocked' in china. That doesnt mean its not used there, just less often.
No for several reasons. The Dimond Princess had over 100 crew of young healthy adults test positive and only 25% where asymptotic long term. Additionally South Korea has a massive effort into contact traction which demonstrated just how rapidly the disease spread. Their mortality numbers looked to be about 0.6% early on, but eventually hit 2% a month after the peak with people spending weeks in critical condition.
On top of that it’s spread rapidly across every country outside of China and there uncontrolled growth rate showed a similar doubling every few days.
>> Their mortality numbers looked to be about 0.6% early on, but eventually hit 2% a month after the peak with people spending weeks in critical condition.
I see this repeated often but this is a misrepresentation of the statistics. Case fatality rate (CFR) is not the same as mortality rate. The CFR is the ratio between deaths and confirmed cases, mortality rate is the ratio between deaths and total number of people infected. The former number is 2% in South Korea now, the latter can not be measured directly without testing 100% of the population continually during the entire timespan of the outbreak, but it is (practically speaking) by definition significantly lower than 2%, unless there is a huge number of unaccounted Covid-19 deaths, which is very unlikely.
Agreed, although a nitpick: I think what you call "mortality rate" is called "infection mortality rate". (Totally agree with your important point that IFR < CFR.)
I think it’s fair to assume most people are mostly interested in statistics that give an indication of the likelihood of dying from a Covid-19 infection, which means you would have to look at the infection mortality rate and not the CFR. So in that sense I think it actually would be somewhat comforting if if it turns out many more people have already been infected than previously assumed. But just to be clear, I don’t want to pretend anyone can reliably claim this considering we still don’t have enough data to create models that can be used to approximate infection rates.
When we say long term here, do we mean within the 14 day incubation period that's been going around? Or does this imply incubation period is likely a lot more than 14 days, maybe in the order of months?
Within 14 days, but most people where evacuated to different countries almost immediately after infection. So, it took longer to gather the data on that population.
Iceland’s peak was significantly more recent than China or South Korea which continued to have regular deaths over a month after their peak. So, my expectation is their deaths will likely double in the next three weeks. Which would be consist with ~1/2 their ICU cases dying. That said it’s a small sample size so significant variability is possible.
On the positive side, they seem to have contained the virus extremely successfully which was likely aided by a low population density, early reaction, and good testing.
Just a point on the comment "~1/2 their ICU cases dying".
The University Hospital handles most of the cases (and all except two deaths). According to their statistics* a total of 25 have been in ICU ("Á gjörgæslu frá upphafi"), total of 13 on ventilators from the beginning ("Í öndunarvél frá upphafi"), with as-of yesterday 3 still on ventilators ("Í öndunarvél") and 6 deaths ("Andlát samtals vegna Covid-19"). So that's less than a quarter of ICU cases dying and half of those on vents.
Interesting, putting that may people in ICU that don’t need vents likely relates to a less stressed medical system. But the numbers are low enough it could just be statistical noise.
I wish the media would focus more on the outliers of the doomsday narrative, as well as the long term consequences of the lockdown solutions being pushed.
covid-19 is bad, but seems nowhere near comparable to what much of the developing world faces on a daily basis (4000 kids die a day from malaria?!?)
I have almost exclusively used Wikipedia for keeping track of the outbreak. Media has become really bad at communicating complex issues that are rapidly changing over time especially as different groups are trying to shape messages to the public.
Face masks for the general public being the most obvious example.
Hospital Patient Zero has been traced back to Dec. 1.
Given that most ill people don't get lung complications, that likely means Nov. was the real patient zero. This is close to 100% certain. It should be verified by looking at Chinese sites.
Regarding the title "Covid+", it seems that most people (even on the news) don't care about the difference between SARS-CoV-2 (the virus) and COVID-19 (the disease). Is this distinction being abandoned? (I'm not trying to be pedantic here; I'm just curious about the common usage.)
Nobody ever cared. The names are confusing (the virus is named after the disease, and the disease after the virus), and the virus name obtuse. The obvious consequence is that nobody ever used the virus name, and never will.
The best you can hope for is for people to call it the "COVID19 virus".
As always, the distinction between two tightly bound things is not important when you talking about features common to both, such as which people have them.
Humans tend to migrate toward shorter more pronouncable, easier to type terms. Language evolves like a virus ;-)
"I am realizing more and more how unusual, unscientific, unmedical, and counterproductive it is for WHO to select the name #COVID19 and reject SARS2. In fact it would be most consistent with medical practice to just call it SARS. Here's why..."
People were never good about making a distinction here. HIV and AIDS are synonymous in popular culture, even though HIV is the virus and AIDS is the disease. No reason to think that SARS-CoV-2 and COVID-19 are going to receive different treatment.
I certainly don't have a better read on the numbers than anybody else. But I think that the rising number of reported cases and deaths is going to make it harder and harder to think that there is a huge hidden pool of asymptomatic or immune people in the population.
For instance as of today, the number of unreported to reported cases in the US can't be more than about 500.
The time period when the disease could have been prevalent in the US can't have been very long, because at its present level it kills about 15000 per week -- a number that could not have gone unnoticed even a month ago.
Eventually, the curves of hopes, reported data, and reality will all have to intersect, I just don't know where or when.
It seems the more important point here is that the majority of the COVID-positive individuals were asymptomatic, putting another datapoint towards the conclusion that there are orders of magnitude more people that have this disease than have tested positive.
We need more studies to gather data on these asymptomatic cases if we want to reopen the economy soon. Imagine if 10+% of the population already had COVID and where immune, we'd be much closer to heard immunity than we currently think.