Covid-19: The T Cell Story
berthub.eu
berthub.eu
This is not really true.
Bergamo had a 58% infection rate as of last month: https://bergamo.corriere.it/notizie/cronaca/20_maggio_22/ber...
Several prisons have seen infection rates in the 70-80% range: https://www.npr.org/sections/coronavirus-live-updates/2020/0...
The USS Theodore Roosevelt had an infection rate of 60%: https://www.cdc.gov/mmwr/volumes/69/wr/mm6923e4.htm?s_cid=mm...
A better explanation for the leveling off is simply that social distancing and lockdowns have reduced the spread of the virus.
https://www.livescience.com/covid-19-superspreader-singing.h...
This article is just wishful thinking.
The point is however that it is impossible that the choir selected their members based on the criteria of "members can be only these who will be easily infected with the at the moment still unknown disease." There the existence of the superspreading event to "80% of some random selection" disproves the hypothesis that "on average much less than 80% of the population can be infected at once". That's obviously not true.
If it is true that say 50% have a harder time catching covid then given that there is a superspreading event at a choir to 80% you need to take into account the others choirs at the other end of the tail.
I.e. given 100 choirs there will be a distribution of "not easiely infected" individuals with choirs with few off them.
I am not saying anything of the validty of the claim, just that the choir doesnt disprove it.
That's a valid point -- one choir alone would not be enough to be the proof, it could still be an accident that such individuals happened to be in one particular choir, but it's not the only such "random sample." Given that we have more "random samples" of different sizes, they can be observed as contributing to the evidence, the logic of the proof is there, and the evidence accumulates, especially as it can be observed that no specific "traits" of the infected could be recognized to bring "the difference" between "less" and "more" easy infections.
One of the arguments supporting the significance in the choir is -- the earlier in the spread of infection we observe such choirs, there's less chance that "100 choirs" from your example were even exposed to the virus, and less chance that that specific choir was exceptional. Similar spreads early in epidemics would also point in non-exceptionality of that one.
The same is true of any other sub-population this is not a true random sample of the global population, like prisons, care homes, even settlements like towns and villages. If you can make any prediction about their members with greater confidence than you could with a random member of the whole population, then they are not random in the sense that matters.
Explaining how and why the characteristics of those sub-populations could correlate with greater natural immunity is certainly a challenge to those advocating for the idea of some level of latent immunity. But, likewise, substantially different outcomes in sub-populations that engaged in similar interventions is a challenge for those advocating that only interventions matter. For example, I've not heard a convincing explanation for Germany's relative success based only on the quality of its healthcare system and interventions. That isn't to deny that they played an important role, just that they may not have been the whole story.
Of course "its healthcare system" is not the whole story alone. "Its healthcare system and interventions" is a good enough precondition, combined with the timing: it was since long obvious that the difference in timing of introducing the interventions immensely changes the number of deaths in the first peak: first order approximation: if the doubling time is 3 days, interventions of 2 weeks earlier compared to some other country could result in 2 ^ 4 = 16 times less deaths per capita. It's very primitive approximation but good enough to make such an argument (even if it's significantly off, it is to show how little time result in big changes). For more detailed elaboration there was recently a paper calculating the difference with more exact models, I believe for the UK. (edit: found one for the US https://www.medrxiv.org/content/10.1101/2020.05.15.20103655v... )
So the story for Germany is: luck, in having the early warning and the capability to act on it: Italy have had its spread earlier and it gave Germany enough of "early warning" which was effectively used. The bigger story is the failure of other countries who also practically had the same "early warning" and remained blind to it, until they recognized that they had do "do something" but with the resulting cost of more deaths.
Europe is a good enough field where a lot of effects could be clearly observed, all countries having in some important aspects significantly more sane health system than the U.S. We also know for sure that the excess deaths due to the all causes together can't be hidden in these countries (see https://euromomo.eu/graphs-and-maps/ ), so we now know exactly how bad which country was hit. Comparing the rich European countries and knowing how they function all the differences among them are indeed very explainable, I don't see any surprises: it's the interventions and learning from the experience of other countries that consistently worked.
U.S. was of course much less ready to learn from anybody. In spite of that, the deaths per million in the U.S. is still 362 while it's 500 in Sweden -- so we all have a good "negative example" from Sweden. Note also that Sweden did close universities and older classes in schools, and suggested everybody who can to work from home, and still got to be that bad -- only because their interventions were by design less strong compared to other European countries.
Reported daily deaths per million in USA, Sweden, Germany and Italy, "by number of days since 0.1 average deaths (per million) first recorded" compared:
https://ig.ft.com/coronavirus-chart/?areas=usa&areas=swe&are...
Note that "it lasted longer in Italy" for almost 20 days -- that was the early warning available to other countries. Some used it. Pity that FT doesn't display simply all the curves based only on the dates. Also, too big entities like the whole USA or China aren't a good comparison, the big less infected areas (due to them being simply less reachable) move the averages down too much -- a better base for comparison would be the entities on the order of 10 millions. E.g. in the USA, NY is definitely a phenomenon that is worth observing separately etc.
But, the belief that herd immunity = 1 - (1 / R) is based on the assumption that those infected have equal susceptibility and equal probability of spread. This is a fair assumption when it comes to a vaccination program which will administer doses not correlated to susceptibility and contact network structure. It is not a fair assumption when it comes to actual spread of disease in the wild, which will preferentially spread in the more susceptible networks.
Reality is certainly better than this; the question is how much better. There's been some quality analysis of time-series data that implies the threshold may be 20-30%.
I believe that New York City's quick decline in death rates compared to jurisdictions with similar or stricter controls but lower seropositivity may imply that ~20% exposure is enough to significantly attenuate spread.
Please provide the sources. I however don't expect it can be that good, and I'm quite sure it will be proven that it's impossible to expect for the epidemics to stop once 20 or 30% of the population is infected, if that is your claim. If your claim is that once that "threshold" is reached the speed of the spread changes, well that speed changed already much earlier: most people just don't have any motive to sacrifice for the "economy" or the rich or whatever. You don't need the laws for the people to figure out that much.
https://www.medrxiv.org/content/10.1101/2020.04.27.20081893v...
> well that speed changed already much earlier
Your comment is a bit self-contradictory and muddled. That is, we're talking about the herd immunity threshold under baseline behavior; what percentage of the population needs to have been infected to result in infections decaying with original behaviors.
The point I was making is that it seems like case counts are decaying much quicker in regions with high seropositivity than other regions with similar regulations and similar empirical measures of mobility. This would imply that under current conditions even the modest immunity reached seems to make a bigger difference than naive assumptions about immunity and resulting Rt imply.
We already know that contact networks are not uniform (source: duh); further, individual susceptibility apparently varies significantly (from genetic studies). These factors significantly change the percentage that must be organically infected to reach herd immunity.
It's worth noting that this difference has both optimistic and pessimistic implications. Optimistic: regions that have high seropositivity are more likely to have the worst behind them. Pessimistic: the amount of vaccination to have equal effect probably far out-strips current seropositivity rates, because it can't effectively be targeted based upon susceptibility and network structure.
There was very little data on overdispersion and differential susceptibility for SARS-CoV-2 at the time that paper was written, but there was some. What existed at the time was in line with the better estimates from SARS-CoV-1, etc, that the paper also used. Further evidence has emerged since, both of variable susceptibility and exposure and of actual mechanisms of variable susceptibility-- some surprising like https://www.medrxiv.org/content/10.1101/2020.04.08.20058073v...
But, again: it's pretty much settled science that Rt = 1 when 1-(1/R0) is infected is a worst case not very often attained, and the evidence so far with COVID-19 (looking at time series data, evidence of non-uniform susceptibility, clear evidence of non-uniform contact networks, significant evidence of overdispersion, etc) leans strongly that way.
Bigger issue is: if 25% infected yields expected Rt of under 1 (the threshold for herd immunity, and I think this is likely)... you'll still have a fair number of cases, because people will come from other jurisdictions with the disease and it'll trigger chains of spread that only slowly decay / peter out each time. If you're one of the other 75%, you're hardly safe, because you can be exposed to one of these chains. Only vaccination can address this, and it's not even a complete fix.
That's what I'm missing, what are your sources to think that? I somehow haven't seen that in the links you gave.
> The herd immunity threshold (HIT) defines the percentage of the population that needs to be immune to reverse epidemic 15 growth and prevent future waves. Figure 3 shows the expected downward trends in the HIT for SARS-CoV-2 as the coefficients of variation of the gamma distributed susceptibility or exposure are increased between 0 and 4 (to assess robustness to changing the type of distribution see Figure S22 for equivalent plots with lognormal distributions). While herd immunity is expected to require 60-70% of a homogeneous population to be immune given an R0 between 2.5 and 3, 20 these percentages drop to the range 10-20% for CVs between 2 and 4.
Curvefitting from COVID-19 incident waves, past SARS experiences, surveying of contact tracing data, pre-epidemic mobility data in the population, etc, all point to CVs around 3 which corresponds to a herd immunity threshold of 15% or so (hence the paper's 10-20% range). I think this is optimistic and a threshold of more like 30-35% is likely, which with durable aspects of behavior change might really end up being ~25%.
Just reading, in one meat processing plant in Germany, from 3000 workers tested they have 1000 PCR positives (Rheda-Wiedenbrück meat processing plant, 1,029 cases so far (1)). It's already 30% of the tested and almost sure an "infection in one wave" (after some weeks a lot of people are PCR negative again). If it stays at 1000 (they will maybe test more: "On Friday morning, addresses of around 30% of the workers were still missing") it wouldn't mean it would have stayed at 1000 hadn't they closed the whole plant.
1) https://www.dw.com/en/coronavirus-german-slaughterhouse-outb...
> Just reading, in one meat processing plant in Germany, from 3000 workers tested they have 1000 PCR positives
This makes me think you don't understand any of the argument. A) CV includes things like contact network structure. OK, meat plants are an unfavorable contact network structure: this proves the point! If we have an observed R0 of 2.5 or 3.0, it includes (disproportionately) people who spend time in places with unfavorable conditions and contact others with unfavorable network structure. If there's a CV, what that means is that in some subgroups of contact structure and individual susceptibility (e.g. meat plants) we have an R0 much higher than 3, and in the bulk of the population we have an R0 much lower than 3.
B) Even ignoring this, there's nothing to say you won't overshoot a herd immunity threshold. The herd immunity threshold is just the threshold where each infection results in less than 1 new infection, on average: it isn't a place where infection magically stops, but instead where the number infected can be expected to naturally decrease. Obviously it's advantageous to have the infected count as low as possible when this happens, because it's only a slow decay from that point.
https://www.facebook.com/photo.php?fbid=10157283645163015
https://www.youtube.com/watch?v=8aHrx68IT7o
There is no constant CV>1 - it all depends on the situation.
Also update from yesterday: in Germany's plant today's score is "more than 1500"
https://www.dailymail.co.uk/news/article-8450221/1-500-worke...
https://www.ecdc.europa.eu/en/publications-data/download-tod...
You need nothing but Excel/LibreOffice and a few simple formulas to get this picture.
Germany has a heavy 7 day rhythm, because they're doing their reporting in a very strange way. Using 7 day averages helps a lot to get rid of the reporting-noise.
You need: Daily death (averages over 7 days) Total death (Sum) (averages over 7 days)
daily/total
Just use SUM() and /
If you subtract the trend of the first outbreak from the main trend it's R²>0,995 from the beginning down the point where old people started to wear masks and go to church. Here things get worse - as expected.
New trend is still collapsing exponentially - numbers now are so low that the curve has reporting issues (national deathcount on Sunday was -1).
So what I see for Germany is the number of detected cases increased the last week, therefore I expect in 2-4 weeks the increase of deaths in Germany, that's how much the delay is between more cases and more deaths.
I don't see anything else that is a realistic "signal" about anything.
Last month around me it worked like this: Big spikes in case were caused by a test-campaign around somebody who died (or was ill). So a trend in case might in fact be an old trend of death - and the burst in case might in fact be asymptomatic or mild cases not leading to any death.
When you try to fit the spikes of death and case things are strange. In Germany maybe case and death have completely detached from each other.
Currently there are a lot of corrections going on. Bavaria had a deathcount of -3 on Saturday. Maybe they are piling up to report a big spike soon, or they stopped someone from reporting nonsense? We will see...
https://www.imperial.ac.uk/media/imperial-college/medicine/m...
Imperial College is always wrong.
Such that, if you maintain close contact, you would still see it spread through more. Explaining the closed populations. But the burst up the curve is driven by the easily infected population more than just mere exposure.
I suspect this might be a topological condition (i.e. the due to the distribution of connections) that limits the growth, in the same way a rope will burn linearly if you set fire to one end of it. Of course, a society is more connected than a rope (in the sense we usually have more contacts that just 'up the rope' and 'down the rope'), but continuing exponential growth requires 'fresh' connections at each generation (so no cycles in the graph) - maybe this limits the acceleration of the epidemic.
This does not in any way suggest that 50% of the general population cannot have enough cell-mediated immunity (or even immunity from a cross-reactive adaptive/antibody response to another coronavirus) to clear e.g. the viral dose from eating at a restaurant.
Half immunity in only certain circumstances is useless for all practical purposes.
Furthermore, if we know that shared living spaces, farm work communes, jails etc are the main source of serious infection, we can hyper-focus on those scenarios and allow the economy to somewhat recover.
We all need to get off our relative high horses, by the way. What is an opportunity to work from home for a lot of us, is a recipe for financial disaster and bankruptcy for many, many others.
All these bullshit researchers had to do to prove their point was walk on the street, take 20 random people and test them, surely if such a large percentage of the population was infected, it would be trivial to prove it with tests.
Same here -- if there are obvious exceptions to some bullshit claim that 50% of the people are immune, then just drop that claim already, right?
They're all tested positive, but 50% of them might have some form of immunity, and are fighting it off like that would a normal cold (or even with zero symptoms). The virus is still in their system, but their bodies are disposing of it without issues.
My understanding is given that there is a pre existing T cell response that doesn’t mean that you will not be infected or that you will not have antibodies if infected. However it could totally be possible that when infected you are much less likely to show symptoms,die or spread it to others.
It’s a tough hypothesis to completely verify but the dip in deaths and infections in many places in spite of lifting lockdowns lends some support to this theory.
This controversial thesis has support from some experts like Dr Sunetra Gupta who teaches epidemiology at Oxford.
> It’s a tough hypothesis to completely verify but the dip in deaths and infections in many places in spite of lifting lockdowns lends some support to this theory.
This sounds like a pretty weak correlation to be honest. Just because a lockdown is officially lifted doesn't mean everyone suddenly reverts to their old behaviors.
The behavior changes vs. policy changes is interesting. Estimates of Rt were falling before the lockdowns and continued falling at about the same rate without any apparent discontinuity through the intervention.
It makes one wonder how effective the policy measures really were-- if they were effective vs. what the population did voluntarily vs. any inherent effects from the structure of the contact network, you'd expect to be able to see some effect in a time series.
The goal here, in my opinion, is to _never_ return to the sort of broad stroke lockdown where society stops completely. That is an entirely untenable solution, and any new evidence we have to mitigate the virus without that sort of action is welcome news. We should be cheering these researches on, not vilifying them. They are doing science. It is an imperfect art, it is a series of terrible mistakes that eventually lead to a revelatory moment.
Could this not be some version of "herd immunity"? So in cramped conditions such as a warship, cruise liner or prison the virus just spreads so damn quickly (given the 5-14 day incubation) that we see most people getting infected really barely before the incubation is over for the index patient).
However in "normal" societal conditions (i.e. where people go to their jobs and are back to their homes at night and are only exposed "occasionally", e.g. on the commuter rail or maybe in a store) that the virus spreads much more slowly and coupled with asymptomatic carriers who (controversially may not "shed" the virus as much, or at all) that reduces the overall input to R0 such that it spreads more slowly and "fizzles out" with R0 < 1.
Presumably "herd immunity" isn't a binary thing ... we don't just go from 59% with raging infections at R0 = ~5-6, then suddenly at 60% infected R0 drops to <1? (or whatever numbers you believe "herd immunity" happens at). Maybe the R0 curve just flattens more quickly than we thought?
We will see, I would not bet on it but that is certainly possible.
[0] https://en.wikipedia.org/wiki/COVID-19_pandemic_in_Massachus...
It's difficult to match new case numbers against specific tests given aggregated data sources, lag etc., but the rough positive test percentage looks like ~2-4% currently vs >10% mid-May and >20% in April:
Same in California - corona is a health care non-issue here, aside from our governor running for President soon, so it's a political issue. So we'll be the last to leave lockdown, and it's looking like Jan. 2022 now.
By non-issue, I mean hospitalizations and deaths according to the official data have been at the same low rate for months, basically flat. California never had a ventilator or ICU shortage at all. (Last time I checked, we had 58 total deaths in SF/Santa Clara County with millions of pre-lockdown arrivals from China.)
In a perfect world, the point of a lockdown is to arrest the spread of a disease entirely. Unfortunately, we're well past that point in the US. Having failed at that, the goal is not just to keep health care utilization below capacity, but also to buy time to mitigate the disease, by stockpiling PPE and health care supplies, implementing robust contact tracing, and establishing policies for individuals and businesses.
California has been prudently relaxing restrictions for a month now, and is now in Stage 3 of the reopening plan.
https://i.imgur.com/zZ7UGVW.png
Deaths has a few problems. One is that once SARS-CoV-2 finds a nursing home, it's a lot of at-risk people in once place, so it gets hit hard. If deaths is the metric you care about, it's best to put a lot of effort into testing workers at care facilities and putting those facilities strict lockdowns. Once we learned this, we started "gaming" the metric a bit. The other problem is it's a lagging indicator.
Positive tests are problematic because early on, there was a shortage, presumed positive people weren't being tested because it wouldn't change anything, people without symptoms aren't being tested, etc. It's the flakier metric, for sure.
For how many tests are coming back negative, I wish we were doing random testing and random antibody testing. If we're not doing those, we're still being reactive.
The positive rate for 2020 of all tests is 3%, but it really appears that whatever tests they're using are just wrong - California had daily flights from Wuhan.
The one logical conclusion from this thread is that if ships, hospitals and nursing homes have high rates of infection, and California homes don't, turn off your central AC and heating in NY, etc.
San Francisco: 47 Alameda: 116 Santa Clara: 152 San Mateo: 99 Contra Costa: 50 Marin: 18 Solano: 23
In other words, about ~500 deaths. If you had 60% of the population infected, you might expect 20x that figure.
Why would it matter that California had daily flights from Wuhan? It changes the math almost by nothing. Whenever an initial seeding occurs, domestic cases rapidly and overwhelmingly takeover in spread.
Wuhan has, as of early June testing, a SARS-CoV-2 seropositive rate of approximately ~3%. There were very few infected people coming on those daily flights in January, and by February domestic spread was many orders of magnitude larger than any seeding event.
California also shutdown earlier than other states, especially in the Bay Area, where essentially all major employers implemented work from policies 7-10 days before the official Shelter-in-Place notice.
California may have had an earlier seeding, but we know for sure it was spreading domestically by mid-January in several cities around the country.
I'm assuming the peaks and valleys correspond to weeks, but why are deaths correlating so strongly with the day of the week?
The lack of consistent context and framing around the human malware has made most discussions of it pretty useless, but easy to skew in a particular direction for those wishing to make political hay out of a trend (on either side - very few have clean hands in this regard).
They are talking about total deaths, but only in SF/Santa Clara. The actual figure is 47 for SF and 151 for Santa Clara [1][2].
[1] https://data.sfgov.org/stories/s/fjki-2fab
[2] https://www.sccgov.org/sites/covid19/Pages/dashboard.aspx#ca...
The problem with this is, that the whole world has to stop for that to happen, and by stop, i mean stop entirely.
Even if you stop the spread in California, and have zero infected people there, the first infected outsider that comes into the state, will spread infections, and you're back at closing down everything.
Basically the second part (keeping healthcare system managable, and hoping for a vaccine soon) is the only think we can do.
I agree that CA (especially NorCal) is probably over conservative... but only because there is so much travel that it doesn't matter if they get to zero, when a few travelers form Arizona, Nevada, or LA (ignoring air travel) will bring it all back in a jiffy. What is likely to be the real boom is when kids go back to school... most of them won't get too sick, but most of them have parents or teachers who will. That's your real second wave when everyone is already tired of caring.
Remember, a guy can have unprotected sex with a lot of women before anyone notices they're pregnant. It's probably 3 weeks from infection to ICU and 5 or 6 weeks to death so looking at deaths or even hospitalizations only gives you informations so delayed it's useless when the doubling time can be 3day (and even when it's 10 days). You're up 10x before you know it...
That's what the math says should happen. It's not what the measurements say. If you look on http://91-divoc.com/pages/covid-visualization/ you will see country after country goes linear. The USA for example had 30k new infections most day for a remarkable 2 months.
I have no idea what causes it. It could be measurement error (that's what I first put it down to in the USA). But if it is it damned hard to explain why it has happened over and over again. Russia has been sitting on 9k new cases per day for 2 months now. The UK went through a period of 5k new cases per day for a month.
It's not a universal, or even the most common pattern. But it happens enough to make the statement "the measures of new covoid 19 cases always change exponentially" wrong enough to be effectively useless as a prediction of where things will head.
However, rising %positive and higher reported rates together or rapidly rising hospitalizations (eg. rising even 5-10% per day) are indications that by the time any new (present day) controls have effect on measured outcome (20-30 day delay) the situation will be 5-10x worse.
I think that's a confusion of R0 with Rt, Rt is what is influencing what we observe and simply not a constant, and additionally not the same in different settings or states:
More importantly, for the Rt == 1 (and being constant) the number of cases would be constantly growing linearly. Only for Rt > 1 the growth is exponential.
As long as the Rt is around 1 it is not surprising that we don't see long lasting exponential growth.
But also note that the values calculated there are more an "illustration" of how the past partial data can be fit to some model than a certainty of the present. The model is helpful to allow us to have a possibility to talk about some concept (Rt and the differences in growth), but is not the current truth, especially for the situation where the data isn't complete and there is a delay in obtaining the updates versus what is actually happening at the moment at every place during the ongoing pandemics. Those who aren't in statistics now can be already infected and die in some weeks, or infect others, and we are never sure how much of those there are at the moment, given the constant changes of the behavior and the movements of the people.
a) Take the number of deaths per day, divide it by the total number of deaths. f'/f.
b) Take a look at this in a log-chart. Use 7 day averages for f' and f to get a cleaner picture.
You will see that f'/f falls down exponentially since pretty much the beginning.
c) determine the exponent by estimating the derivative ln(f'/f)'.
d) Now use that to calculate the function f.
e) Calculate an enddate when it will fizzle.
If for example you are in a still strongly growing (but no longer exponential) regime where you can approximate the currently infected number of persons as proportional to t^2 , and there is some constant IFR, then f'(t) is also proportional to t^2. f(t) will then be growing as t^3, so f/f' will go as t^-1, which will look quite similar to an exponential decay at the timescale of the process.
You may see some wobble caused by outbreaks here and there, but the mechanics of COVID-19 are always the same.
Going to zero exponentially does mean it will converge mathematically. And t^-1 won't look like the things that can be observed. There is no such thing as a constant upward trend away from the exponential. If you look carefully and do some regressions you can see some ups and downs. Changes in the behaviour of the people are able to modify the trend, but there is no constant movement away from an exponential.
You can always add an upper exponential bound that will converge below single digits per day before there is any chance that the lines cross.
I'm not claiming that is what you were saying, I just wanted to call out this point in case other people read this that may come to this conclusion.
FWIW, the solution to f'/f = exp(-t) is f(t)=c*exp(-exp(t)), which actually looks like an epidemiological curve, but like any sigmoid it will be hard to determine the actual saturation point until after it has already passed the inflection point.
Good luck that most countries on the planet have already passed the inflection point, so finding the saturation point isn't that difficult anymore.
Interesting to see that just asking people to look into this will generate downvotes. It's just basic mathematics. And it really isn't that hard to verify...
In my area they made masks mandatory and cut down the protection of hospitals - now we have an infected nurse. I think politicians currently are using seriously wrong models for their actions.
I had a discussion with one of the "official" epidemiologists. It went like the discussion with you - just at the point where we are now he decided to go into all-caps swearing, said that he will never look into this, called his models perfect and then blocked me.
PCR positive doesn't mean you are seriously infected or an active 'spreader'... just means they found some viral RNA in your mouth/throat/nasal-passage.
Just means you were definitely exposed and are carrying the virus. Many of those cases were completely/almost asymptomatic, right?
N. Italy is it's own unique situation... don't have specific comments there...
With an enclosed dense population situation like a prison or a naval vessel... it is interesting that the spread would stop short of ~100% infection once it crosses the 25-50% threshold, given what we understand of the overall high transmissibility of this virus.
It is simply not true. Some areas level off at 1-2%, some at 5%, 10, 20% or whatever else. It all depends on when strong social distancing measures were taken.
There were only a few places such as NYC or Bergamo, where measures were taken too late and infections went over 20%.
https://www.smh.com.au/world/asia/new-beijing-outbreak-raise... https://www.worldometers.info/coronavirus/country/sweden/
You are probably correct, but it still seems to be the case that the virus spread is slowing faster than expected even with relaxations of lockdown measures.
I suspect the explanation is complex. It is probably a combination of factors: individual awareness in reducing social contact, northern hemisphere summer, and individual variability.
It's probably not the case that a large proportion of people are immune, but there might be big differences in susceptibility. The examples you mention are either from early in the pandemic where social distancing rules were less habituated, or enclosed environments where transmission is harder to avoid.
I'm just guessing, but this would support the European strategy of initial lockdown followed by moderate social controls if outbreaks become more controllable after the initial surge.
It's hard to compare with Brazil, because the stats coming out of there are poor and they're making less attempt at controlling the virus compared to almost any other country.
Is that really true? Most of these "hot" LATAM and asian countries do not have wide-spread central air conditioning. In those countries, staying inside is horrible in the hot months.
How many of those had a full blown infection? I suppose detectable antibodies suggests there was at least some level of infection, but a very mild infection plus rapid clearance in a plurality of the population fits with the sentence you quote. The other cites you provide basically show the same thing. Of course social distancing/lockdowns also help. Why not both? The reality is that there are two mysteries here: i) a lot of people are relatively unaffected by the virus and serology shows they were exposed; ii) a lot of people, even under very crowded conditions (USS Teddy R; prisons) just don't get COVID at all. 40% of those on the carrier simply never got it. How is that possible? The prison data is high if we take your the 80% upper level of your range, but you will expect to see those sorts of outliers in small datasets. This is particularly true where the early antibody tests were not very specific (i.e., relatively high false positive rate).
In short, our immune context (genetic phenotype) is unique! We need a lot more data from everyone to start making accurate correlations. We do not measure T-cells, cytokines, mast cells, b-cells, HLA (partly how we potentially make antibodies) at any meaningful level to provide much confidence. Many natural/industrial substances suppress our T-cell responses and generally innate immune system (metals, mold toxins, etc), so we also need to start accounting for those.
It's a long road we have in front of us. Hopefully the medical system supports patient data ownership and research to improve on our obvious ignorance.
[1] https://forums.phoenixrising.me/threads/documentary-undercov...
As I mentioned above, metals & mold toxins can generate a lot of inflammation in some people that manifest as "foggy brain", joint aches, etc. It takes time and diagnostic testing paired with treatment protocols. Some people cannot process Aluminum and Mercury forms out of their body without relying on Glutathione (limited detox pathways, start with HLA genetic SNPs).
It's a good thing most western medicine doctors never prescribe expensive pharmaceuticals long term ... oh wait ;-)
This suggests going back and re-testing for antibodies some populations from tightly packed groups - the cruise ship passengers, warship crew, and nursing home residents. The antibody tests are more accurate than they were two months ago. The key here is to find out how many people, definitely exposed to the virus, not only did not show any symptoms, but did not develop antibodies. That group presumably had some form of pre-existing immunity.
Is anybody doing something like that?
Moreover, even if 100% antibody + rates does not rule out a lot of pre-existing immunity. If I am naturally resistant to coronavirus (e.g., COVID patient sneezes in my face and I get a mild fever 10 days later), I'll probably develop antibodies from an exposure and otherwise be fine, I'd call that pretty damn good pre-existing immunity. According to your logic, that would be "no [pre-]existing immunity at all."
What I learned is that we are sort of in the very beginnings of understanding how immune cells work (together), how their plasticity works in terms of gene expression at any given point in time. For example, environmental circumstances such as cytokine presence can lead to T-cell types "transforming" to other types which have other effects etc. but it's not well understood how this occurs. A very large amount of genes regulate each other, you get a complicated network of up- and downregulations and it's hard to reproduce and understand what needs to be done to generate a certain type of T-cell with certain characteristics and behavior.
And then you haven't even tried doing that in-vivo where you could try to push the immune system to express genes such that many T-cells of type X are generated to enhance or fight inflammation. Because then you just disturbed a complex interacting system with zero idea what the total effect is and whether "the network" is resilient enough to not crash and burn somewhere somehow.
It's very very complicated with a huge amount of combinatorics involved so Complex Systems Theory helps to build models helps a bit.
Apparently, no, as there are no observed differences in the percentages of infected when the people who have children or work with children are compared with those who don't. If the stated assumption were true, those that were more exposed to other coronaviruses (which would be expected among those having or being close to kids) would be as a group less prone to be infected. That was apparently not observed.
So the "slowdowns" are just the humans adapting their behavior to reduce their chances of getting infected. When Rt is 1 the growth is linear, as simple as that. Less of such changes in the population behavior, more people are getting infected, faster.
Source: translated transcript of the podcast of Christian Drosten. Hint: also, don't believe what most of the media says that "he said" -- I've seen a lot of "editorialized" reporting of what he says, to the point of the end product being completely opposite of what he actually said. It's that bad. One really has to go to the source and read. Unfortunately, it's a lot of work, as the podcasts are long. So the people who don't read the source tend to have completely wrong idea what he actually said -- that's also why I'm not giving the specific link: if one doesn't invest really a lot of energy and find and carefully the sources, one has more chance to acquire completely wrong conclusions as "highlights."
So while I agree with your general advice about sources, I don't find your "apparently" to be apparent at all.
"Apparently" was used in the sense "I haven't seen and verified the sources myself" (i.e. the specific scientific material that supports the conclusion of an expert) but at least I took the effort to get the claim about that conclusion from a recognized expert directly." That is, I can't give you the ultimate source that the said expert used for that conclusion, but according to my understanding from reading a transcript of his podcast he concluded that and I have chosen to believe him. Experts spend their whole life to achieve expertise. There are a lot of "preprints" and even fast-approved published scientific papers floating around, which eventually (even extremely fast) are recognized to be flawed by the experts, but only after the false claims are repeated across the media. In such a situation, unless we are the experts ourselves, and unless we have infinite time available, our best bet is to trust the experts who are less prone to present or accept false claims. I consider Christian Drosten one of these.
Being curious, I do check some source scientific works myself, I'm just admitting that for that particular claim I haven't checked the sources myself but decided to trust Drosten. I also claim that I at least spent enough energy to read the exact transcript of his talk and avoided to read "journalistic interpretations" -- the expert's statements are very often totally deformed by retelling. If that's not enough for you and you believe that you can catch him in an error, I'd really like to know why you even believe to be able to achieve that. And if you believe that I'm claiming something he hasn't said, you can check the transcripts of his last two podcasts yourself and write here if I made an error, I'll be happy to learn more.
At some level every one of us has to trust some experts, the question is just if we can also recognize these who just claim some expertise but are actually promoting false claims. I believe that Ioannidis, for example, is an example of such, and I also see that other experts agree.
For "as close to the experts as possible" sources I also recommend everything from https://www.microbe.tv They also admit that they do make errors sometimes, because they didn't recognize early enough how serious this virus is going to affect everybody. But getting the coverage from the experts directly allows one to remain much saner than when reading media who regularly completely distort what experts actually say. And even when media accurately quote some single paper, media often falsely reflect what the whole body of knowledge actually is, as in xkcd "Significant" comics.
Who claimed something opposite to that and where? I don't understand why you write that at all.
I'm arguing that getting the coverage directly from the experts who do reflect the knowledge of the whole fields (and who don't promote some narrow agenda like Ioannidis) is provably better than getting it from the politicians or the press. The goal is always to recoginize these who introduce a bias which distorts the truth.
What are your "better" sources (as in, more expert and less biased) that cover this pandemics?
It's absolutely exhausting, and is easily the weakest element of sites like Hacker News. There is a certain sort of hubris in this industry that just foments this exaggerated sense of relevance to every topic.
I’m very happy that we get lots of opinions from non-experts.
Pretty sure there is a phallacy that describes this line of thought. Multiple ones, probably.
By the way the GP is criticizing the “appeal to authority” fallacy, and you are criticizing a form of the “argument to moderation” fallacy.
An appeal to authority is disputing widely understood information by citing an authority as if it is an override. e.g. "my doctor told me that vaccines don't work": Against hundreds of thousands of experts and professionals, and a widely understood body of evidence, someone cites a dubious authority as a counter argument.
"My plumber says that lead makes you grow strong bones" "My accountant says that compounded interest is a myth". "My cousin has a chemistry degree and he says that global warming isn't real".
Does that somehow mean that every layperson needs to fill the world with their hot take of noise about COVID-19? No, not at all. This does not follow.
The argument to moderation claim makes even less sense, and I'm not sure you understand what it even is because it's woefully out of place.
This is precisely the “argument to moderation” fallacy.
I disagree with the rest, and will not comply with any part of your recommendation. If you think my opinion is “noise” then it is probably best for you to ignore it.
1) Allow a million passengers from China to disembark in Calif.
2) Everybody goes to the grocery store twice a week and mingles in a leisurely fashion.
For us, it's worked out great. Probably the lowest measured infection and documented mortality rates in the world for an area that doesn't do testing and tracing.
Seen any apocalyptic news stories about SF hospitals like NY? Nope, me neither.
I'm much more worried about the negative cardiac effects of no exercise than corona deaths.
And I share that call-out in this article that not knowing is not a claim that things aren't working. I just expand it as not a claim that some things have worked. We need more studies that will frustratingly take time.