Covid-19: The T Cell Story(berthub.eu)
berthub.eu
Covid-19: The T Cell Story
https://berthub.eu/articles/posts/covid-19-t-cells/
138 comments
In Massachusetts (where we have been hit pretty hard by C19) we're trending downwards in a significant way and have been for a few weeks now. But I see more human activity out and about than ever (at least compared to March/April and mask wearing / social distancing is has been pretty obviously in decline for a number weeks now). The state is now well into it's re-opening phase 2 and even with the recent protests, there were only a handful of new infections.
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?
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?
You're either exponentially growing or exponentially shrinking. (some) People are being more careful or wearing masks, which probably has a bigger effect on superspreader events than just "herd immunity". It slows the rise, but that doesn't mean that without intervention you have escaped the pandemic even though it was tamped down quickly.
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...
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...
> You're either exponentially growing or exponentially shrinking.
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.
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.
Any exponential close to 1 looks linear for relatively long periods of time. That doesn't mean that if you "open up" the economy it will stay that way. Unfortunately, the heavily delayed hospitalizations and deaths are the only direct measures, while sampling rates/biases make even statistical analysis of positive tests for infection rates difficult.
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.
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.
> Maybe the R0 curve just flattens more quickly than we thought?
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:
https://rt.live/
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.
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:
https://rt.live/
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.
Thank you, I do mean Rt (being the R at time t) at least in the context of the current state of SARS-Cov2 for this discussion.
I think what you’re seeing is exponential growth. Meaning, after crushing the curve, things start all over again with slow growth and then pick up again in 2 months. We are beginning to see the exponential growth explode in Arizona, Texas and Florida now I think.
I'm not sure how it's exponential, if you look at the graph for the current # of cases [0], then you can see it's more or less flat (and the new case %-age has been around 2-3%), between deaths and (most) people recovering, the number of active cases must at least flat and most likely getting smaller?
[0] https://en.wikipedia.org/wiki/COVID-19_pandemic_in_Massachus...
[0] https://en.wikipedia.org/wiki/COVID-19_pandemic_in_Massachus...
> We are beginning to see the exponential growth explode in Arizona, Texas and Florida now I think.
We will see, I would not bet on it but that is certainly possible.
We will see, I would not bet on it but that is certainly possible.
It’s my understanding that Massachusetts testing has been way down recently which could account for the lower number of positive tests.
Number of daily tests has been broadly flat for the last three weeks and is ~20% lower than the peak testing in mid-May.
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:
https://covidtracking.com/data/state/massachusetts
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:
https://covidtracking.com/data/state/massachusetts
Exactly. Things with R0 < 1 will fizzle. Herd immunity is 0.
Little mathematical exercise. For Massachusetts:
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.
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 people are infectious for some finite number of days, f'/f will always tend to zero, even if f' stays constant.
It falls down exponentially! Just look at it. If you do a bit of math you will see that f will just converge.
I think it really depends on your underlying dynamics, but I just want to point out that f'/f going to zero does really not imply that f will converge.
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.
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.
And I still politely ask you to just look at the data. There is nothing in the data that points to "somehow" and "maybe". It is just a straight linear line in the logscale.
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.
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 doubting that it looks like what you say for the area you looked at. I just wanted to make a more general point that it is not sufficient to look at f'/f and conclude that things will be fine if that is tending towards zero.
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.
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.
Yes, it's important to go down exponentially - which exactly is the thing it is doing - not just in one place...
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...
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...
I think the downvotes were not for asking people to look at it, but because the comment can come off as overly dismissive of the severity of the situation.
Maybe, I'm not really a polite person. I think using a working formula for f would greatly help to evaluate the consequences of actions and helps to detect outbreaks early.
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.
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.
That's what bertheb says, R0 depends on the other variables, like density, climate etc etc.
> In Massachusetts (where we have been hit pretty hard by C19) we're trending downwards in a significant way and have been for a few weeks now.
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.)
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.)
I'm not sure if you're talking about daily deaths, or in total, but California has had 5,286 deaths as of today, not 58. We also had 81 deaths today specifically.
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.
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.
They probably meant per day. I think "flat" is a fair characterization of the metric:
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.
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.
> https://i.imgur.com/zZ7UGVW.png
I'm assuming the peaks and valleys correspond to weeks, but why are deaths correlating so strongly with the day of the week?
I'm assuming the peaks and valleys correspond to weeks, but why are deaths correlating so strongly with the day of the week?
That's probably an artifact of the reporting process. Most hospitals don't report during the weekends.
Indeed - there have been many cases of where when things have been reported is what gets graphed instead of the actual dates of particular statistics.
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).
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).
I also wouldn't be surprised if there seasonality in medical care, like people receive worse care certain days, for some reason.
58 total deaths for the SF Bay Area in 2020, out of about 4 million people.
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.
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.
The number of deaths for the SF Bay Area is not 58, either. It is:
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.
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 not sure if you're talking about daily deaths, or in total, but California has had 5,286 deaths as of today, not 58. We also had 81 deaths today specifically.
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...
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...
> In a perfect world, the point of a lockdown is to arrest the spread of a disease entirely.
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.
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.
Are you ignoring LA county? It's rising rapidly there and starting to spread to the surrounding areas. The wealthy So. Cal. residents who haven't fled yet will move North and then the North will get hit, kind of like what happened to Connecticut via MA and NY.
Curiously enough, after running some simulations in a supposed realistically connected population model (but much smaller n), I found linear growth occurring soon after the exponential phase.
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.
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.
There are only so many people, so the growth rate can't stay exponential forever. Eventually it must look like an S-curve.
Right. An S-curve starts out looking exponential. Then begins looking rather linear in the vicinity of the inflection point. And later bends over. If you are in the midst of it then it might be hard to predict the shape of the curve.
Are you able to share the code for this model?
Imperial College report showing mobility trends explain over 80% of observed variations in transmission:
https://www.imperial.ac.uk/media/imperial-college/medicine/m...
https://www.imperial.ac.uk/media/imperial-college/medicine/m...
I have seen that "infection level off after 10-20%" multiple times.
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%.
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%.
raducu(2)
Also this Washington choir where over 80% of the attendants were infected
https://www.livescience.com/covid-19-superspreader-singing.h...
This article is just wishful thinking.
https://www.livescience.com/covid-19-superspreader-singing.h...
This article is just wishful thinking.
"This phenomenon is called overdispersion, and it means that while on average a patient infects 2 or 3 new people, this average consists of many people infecting nobody, and then some mass spreading events infecting many more."
> then some mass spreading events infecting many more
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.
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.
Just because the choir couldn't select its members on the basis of lower immunity, doesn't it mean it didn't select them (or they self-selected on) the basis of some characteristic(s) that happened to correlate with lower immunity, e.g. age, biological gender, ethnicity, socioeconomic status, etc.
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.
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.
> 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.
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.
No.
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.
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.
> I.e. given 100 choirs there will be a distribution of "not easily infected" individuals with choirs with few off them.
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.
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.
That is a fundamental misunderstanding of how probabilities work. There is always bunching, streaking, things that look like patterns but are not, and the like.
We obviously have a huge variation in spread. Indeed, the choir is evidence of superspreaders who produce many cases, and we have an average R0 of 2-3. Singing loudly in enclosed spaces with many people is an example of a contact network structure that causes greatly increased susceptibility and contagion. Indeed, it proves the whole point!
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.
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.
> There's been some quality analysis of time-series data that implies the threshold may be 20-30%.
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.
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.
This is a good introductory treatment of the topic:
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.
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.
I tried but I don’t see that the paper proved anything about the current pandemics?
A paper doesn't "prove" anything, but it does utilize and cite upon real world mobility data and real world susceptibility and transmissibility data for many diseases, including early estimates of these for SARS-CoV-2, e.g. https://wellcomeopenresearch.org/articles/5-67 is cited and used in the estimate of the coefficient of variation.
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...
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...
Good, so we agree that nobody has proved for SARS-CoV-2 that less than 70% of can be infected to achieve the so-called "herd immunity" (even when knowing that different people allow some very lax definitions of "herd immunity").
Yes, and nobody has proven really -anything- about most things, by this metric. All we have is evidence of varying quality.
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.
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.
> if 25% infected yields expected Rt of under 1 (the threshold for herd immunity, and I think this is likely)
That's what I'm missing, what are your sources to think that? I somehow haven't seen that in the links you gave.
That's what I'm missing, what are your sources to think that? I somehow haven't seen that in the links you gave.
Every single worker has COVID at one farm
https://www.bloomberg.com./news/articles/2020-05-29/every-si...
I think this tells you more about their working and sleeping conditions than it does about how the pandemic will play out on a national scale.
But the article claims it will discuss "How around 50% of us appear to have (some?) Cell Mediated Immunity against COVID-19". If we're seeing farms where every single person falls ill, that suggests that 50% of us aren't immune, though I suppose "(some?)" is a cop out.
So those people in one town did not have enough cell-mediated immunity to clear the viral dose from spending all day in an enclosed space with dozens of coworkers shedding the virus.
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.
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.
Surely it could be true that all population is immune to exposure to exactly half a virion well cooked with sauce, but such an information is pretty useless in any practical way, isn't it?
Half immunity in only certain circumstances is useless for all practical purposes.
Half immunity in only certain circumstances is useless for all practical purposes.
No, it is incredibly useful. That means a subset of the population, or a population certain environments, can be exposed and not get infected. AFAIU, every exposure has the chance of the immune system creating antibodies as it does for foreign cells.
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.
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.
I've seen news about bullshit studies from famous institutes in the UK that 60% of the UK's population already was infected by "now" (a month or two ago).
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?
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?
Why would that be an XOR situation?
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.
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.
Boom. That's totally correct. Plus, these articles showing "every single worker" infected or 60% of carrier passengers/prisoners etc. don't do a good job of reporting how may actually sick people there are. Immunity is not all or nothing. It's a relative amount of resistant. Many (likely all) of these "naturally immune" folks (if they exist, and I think the evidence is pretty compelling personally) would get COVID if injected with a sufficient dose of purified virus. Many (likely all) would get COVID if an infected person sneezed into their face. Many (maybe? probably?) would be fine sharing a grocery store with a pre-symptomatic carrier. That's huge if true. Time will tell.
I'm not sure if your sources really say what you imply that they do. Bergamo had a 58% COVID exposure rate as of last month, as determined by serology studies.
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).
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).
It was not my intent to imply anything about the severity of COVID infections, only that a 10-20% infection threshold is contradicted by several real world examples. While it is still possible that T-cells may have some immunological benefit against COVID, I don't think the 10-20% number qualifies as evidence of that.
I think that was sloppy writing at the article. I think what they meant was that the curve leveled off, not total infections. Fits with the next section narrative on assumptions on an easily infected population.
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.
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.
It would be more accurate to state "levels off before 10-20% develop antibodies". The author wrote an article that covers other mechanisms by which the human immune system can fight viral infections. The definition of what 'infected' means is a total gray area. The human body is constantly fighting low-level viral infections.
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.
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.
> A better explanation for the leveling off is simply that social distancing and lockdowns have reduced the spread of the virus.
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.
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.
Why do you think "northern hemisphere summer" would have any (positive) impact? I mean, just look at Brazil, an "eternal summer" country, they're not doing that well... Except if it's the combination of heat+low humidity, there might be counterexamples to that as well though (Iran?).
It's a guess, but a plausible one. We know the virus is destroyed quickly by UV light, which is stronger in summer. People spend more time outside in summer, and we know transmission is reduced outside. Other respiratory viruses spread poorly in summer.
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.
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.
It's not the heat but the UV exposure and people's ability to create vitamin D from it. People in countries that have "eternal summer" tend not to go out in the sun as it's hot. Also skin pigmentation is a big factor.
"tend not to go out in the sun as it's hot"
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.
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.
People spend much more time outside and the school is off. Also the European summer is mostly not as hot as the Brazilian.
I think you’re being too negative here. I’m not an expert.
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.
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.
I'm not sure what exactly you think is negative? The claim that infections top out at 10-20% is clearly not true. Even if it were true, we don't really know enough yet to conclude that T-cells would be the reason why.
> 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.
> 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.
It isn't true _everywhere_. It might be true in some places. For example, towns with young residents and lots of children. Or suburbia where there isn't much incidental contact with others for long periods of time.
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.
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.
> 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 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.
No one claimed infections "top out" at 10-20 %. I agree, that is CLEARLY untrue. Rather, they seem to "level off" at 10-20%. That is, the rate of spread/new infections stops increasing so fast. Exponential infections always follow an S curve, but usually the leveling off of new infections starts at much higher penetration levels (e.g., 50%).
I mean, perhaps I misinterpreted the original article, but it sure sounded like the author was hypothesizing that T-cells confer immunity to a large percentage of the population which is why we were only seeing infection rates of up to 10-20% in the wild. I'm not sure his theory would make much sense if he actually meant "the rate of spread stops increasing so fast after 10-20% but continues up to 60% anyway".
Your confusion seems to lie in the implicit assumption that the "immune" can never get infected. That's not how immunity works, in general. Immunity is a level of protection not an infallible shield.
Sweden has achieved about 15% infection rate and has showed no signs of slowing.
https://www.smh.com.au/world/asia/new-beijing-outbreak-raise... https://www.worldometers.info/coronavirus/country/sweden/
https://www.smh.com.au/world/asia/new-beijing-outbreak-raise... https://www.worldometers.info/coronavirus/country/sweden/
No, the 14% figure is for people that self-requested testing. You can see it as an upper boundary, but it won't be the actual figure, which is likely around 8% or so.
Exactly. In other words 86% of Swedes who had reason to suspect an infection and wherewithal to request testing and provide sample did not have COVID.
T cell responses are interesting. Infectolab (https://www.infectolab-americas.com/) and its originator Armin (Germany) are using T cell responses for tick borne diseases like: Borellia (Lyme), Bartonella and Babesia because antibody testing is difficult for these (broken via CDC specs as most labs won't test key proteins 31, 34 and never indicate which antibodies are present). Bartonella is very hard to test for with antibody and PCR. It really depends on lab specialty. The sars-cov-2 antibody tests are unpredictable in quality, so nearly useless (my dr is 0-50 via Quest and many people had + PCR).
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.
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.
Note that Armin Labs are considered quacks by many. For one example [1]. I will leave it to others to do your own research but just wanted to recommend caution about their use. Especially for diagnosis of Lyme, plenty of tragic stories of people getting an Armin positive test while negative on the others and then being encouraged through years of serious antibiotic treatments only to have always been negative all along.
[1] https://forums.phoenixrising.me/threads/documentary-undercov...
[1] https://forums.phoenixrising.me/threads/documentary-undercov...
I mentioned Armin/Invecto since they are looking at T-cell responses to pathogens. Much of the prolonged antibiotic treatment is no longer common practice. It damages the GI, suppresses portions of the immune system and is in many cases bypassed by 2 resistant forms of Lyme (round body starvation form & biofilm colonies). Most of my testing has been through Igenex, which has a lot of experience with antibody testing that places like Quest screw up. The typical Western Blot for Borellia (Lyme) is not a frequent test for many of the labs and requires 5 - 7 IgM or IgG that won't form in the patient because their immune system is too suppressed to manifest all of them. For example, the outer protein of Lyme does not have to shift and result in another antibody presentation because the host immune system has not forced it to. The CDC spec deliberately ignores two specific antibody proteins OSP 31, 34 as do the common tests and instructs most labs not to reveal which antibodies the patient has. Therefore, that patient might have some, but not all of the "required" antibodies.
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 ;-)
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 ;-)
Curious about your thoughts on a patient that tested both the Quest and IgeneX immunoblots at the same time. The Quest showing 5 positive bands. And the IgeneX no positive bands. Then repeated the same two tests four months later at the same time with the same results. So Quest repeatedly CDC positive, IgeneX repeatedly negative. How would you interpret that?
I would ask which Quest lab specifically ran the test as lab quality can vary. I have not seen mainstream labs return all of the antibody stains present in some time, they usually indicate (+/-) for IgM (less likely) or IgG (more likely). The count is also usually indicated (titer level). It also depends which antibody proteins are present. Many of them overlap with other pathogens (IE: 41 covers most of the spirochete class: Borellia, Leptospirosis, Syphilis, etc). It depends on current & historical symptoms and markers like C3a/TGF-b/MMP-9/CD57,8 and would probably try other tests for markers, possibly another Lyme specific test at a different, but specialized lab like Galaxy Diagnostics (well known for Bart), Fry labs or another in NY whose name escapes me.
Where do you live where you have a single doctor with 50 PCR+ COVID patients who have been tested for antibodies? Bergamo?
Do you have any thoughts on the usage of consumption of Borax to treat Lyme disease (asking because your username)?
Never used it and wouldn't use it. I do not condone long term antibiotics either.
The author asks some questions which can be answered by gathering some data.
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?
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?
It looks like that's already been done, and there are tightly packed groups where the infection rate was 70-100%, as mentioned above. So, no existing immunity at all, probably. That was the claim in the original article, but the data does not bear it out.
Finding densely connected subgroups in special populations (prisons, cruise ships) with high infection rates doesn't mean that that specifies the equilibrium herd immunity threshold in other populations.
Similarly, the plural of anecdote is not data. You are completely right, and nobody has a good rebuttal.
70% infection Suggests a LOT of pre-existing immunity.
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."
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."
The article specifically points out that there could be herd immunity in a certain population without any antibodies.
Yes, but that doesn't mean those people won't go on to develop antibodies if exposed to a significant dose, e.g., in a tightly packed community. It means they don't need to already have the antibodies to have a pre-existing level of resistance. It takes weeks to develop an antibody response from scratch.
Kind of shows how little we know about the human immune system. Hopefully this pans out and we won’t see massive new waves. I have an anecdotal example of that this might be something. A friend and his wife got tested before delivering their baby and she was positive for antibodies and he was negative. He might have just beaten the virus before the body even started creating antibodies.
For my master thesis, I wrote a small tool that would take and analyze raw genomic high throughput data of various T-cells (chip-seq + rna-seq of th17 to be precise) and create a regulatory network in Cytoscape. The basis was a paper relating to The Th17 Project [1].
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.
[1] http://th17.bio.nyu.edu/pages/index.html
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.
[1] http://th17.bio.nyu.edu/pages/index.html
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Highly recommend the companion series of podcasts to TWiV if this article perks your interest: https://www.microbe.tv/immune/
> Does this mean that if you recently had a Coronavirus that caused a head cold that you have a measure of protection?
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."
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."
These cold viruses circle the globe too, and just because some children had colds and transferred them to adults, does not mean that they had exactly the strain that could potentially give partial Covid19 immunity.
So while I agree with your general advice about sources, I don't find your "apparently" to be apparent at all.
So while I agree with your general advice about sources, I don't find your "apparently" to be apparent at all.
> 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.
"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.
You should look for the better source for your claims than some podcasts.
It is already estblished that those who had COVID develop immunity for it, just not entirely clear how long lasting it is. But given its similarity to the original SARS it should last quite a long time.
> It is already estblished that those who had COVID develop immunity for it
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?
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?
This article talks about cell mediated immunity whereby compromised cells are detected via peptides produced, but there's actually another mechanism that's still in the early stages of being understood called merocytophagy where an immune cell can partially "eat" another cell to see if it "tastes" sick.
"It took nine days for the number of infections to grow from 6 million to 7 million, and less than eight days to get to the latest million, so the pandemic is not slowing down." https://www.abc.net.au/radio/programs/coronacast/the-global-...
What's the qualifications of the author before I go and invest time to read his/her theories?
> Geeky entrepreneur
https://www.linkedin.com/in/bert-hubert-b05452/
So... “none”?
Author here - the article has been read and retweeted by actual world class experts. Given that the article is now nearly two weeks old, I would have heard about any gross inaccuracies in there by now. There is some minor feedback and it needs a few tweaks, but overall it holds up really well.
It's Musk Syndrome -- be successful at something, to whatever degree, so think that a bit of Googling makes you an expert at everything, and that the world needs to hear your take.
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.
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.
Nearly as tedious as listening to experts in lab coats make wild projections that were wildly off and basing policy on it.
I’m very happy that we get lots of opinions from non-experts.
I’m very happy that we get lots of opinions from non-experts.
Yeah, experts won't get everything right so let's listen to the ignorants...
Pretty sure there is a phallacy that describes this line of thought. Multiple ones, probably.
Pretty sure there is a phallacy that describes this line of thought. Multiple ones, probably.
Why don’t we listen to the information too, and decide for ourselves by thinking critically? Maybe even change our own minds sometimes? None of that is beyond my reasoning abilities, and I have a hard time believing that it is beyond yours.
By the way the GP is criticizing the “appeal to authority” fallacy, and you are criticizing a form of the “argument to moderation” fallacy.
By the way the GP is criticizing the “appeal to authority” fallacy, and you are criticizing a form of the “argument to moderation” fallacy.
"Appeal to authority" -- which is a huge red flag when it's brought up in a discussion, because it's usually an attempt to elevate nonsense as if all voices are equal and things like expertise and training/dedication aren't pertinent -- has zero relevance, and seems to be misunderstood a hundred times for every time it is actually understood.
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.
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.
> an attempt to elevate nonsense as if all opinions are equal
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.
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.
It's not as much theories as science reporting.
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So... Does this mean that we can "self" vaccinate by going maskless and running around the city and in and out of grocery stores every few days?
I am mostly joking here, but I am also serious, the post implies that we can develop immunity by getting minor exposures over a period of time.
I read the article twice and probably need it twice more to fully understand it, but please enlighten me.
Innate immunity is more like a built-in mask. It’s pretty much the opposite of building up an immunity.
That's essentially how live-attenuated vaccines work. You'd have the same benefits and drawbacks, but a dosing issue.
It's essentially what we've done in SF:
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.
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.
The amount of frustrations surrounding all of this is mind numbing. I almost expect we will get vaccinations before we get understanding.
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.
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.
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.