Covid-19: The T Cell Story • Hacker News Board

Re: Covid-19: The T Cell Story

#131

Post by mlyle » Fri, Jun 19, 2020, 3:43 AM UTC

acqq wrote: post #130

mlyle wrote: post #115

Earlier quoted context omitted.

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 t…

> 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…

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.

Re: Covid-19: The T Cell Story

#132

Post by acqq » Fri, Jun 19, 2020, 9:48 AM UTC

mlyle wrote: post #131

acqq wrote: post #130

Earlier quoted context omitted.

> 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…

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. T…

I tried but I don’t see that the paper proved anything about the current pandemics?

Re: Covid-19: The T Cell Story

#133

Post by kurthr » Fri, Jun 19, 2020, 4:33 PM UTC

rstuart4133 wrote: post #129

kurthr wrote: post #50

Earlier quoted context omitted.

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…

> 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…

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.

Re: Covid-19: The T Cell Story

#134

Post by mlyle » Fri, Jun 19, 2020, 4:57 PM UTC

acqq wrote: post #132

mlyle wrote: post #131

Earlier quoted context omitted.

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. T…

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...

Re: Covid-19: The T Cell Story

#135

Post by acqq » Fri, Jun 19, 2020, 5:58 PM UTC

mlyle wrote: post #134

acqq wrote: post #132

Earlier quoted context omitted.

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 S…

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").

Re: Covid-19: The T Cell Story

#136

Post by mlyle » Fri, Jun 19, 2020, 6:27 PM UTC

acqq wrote: post #135

mlyle wrote: post #134

Earlier quoted context omitted.

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 S…

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.

Re: Covid-19: The T Cell Story

#137

Post by EricE » Sat, Jun 20, 2020, 9:41 PM UTC

idrae wrote: post #67

Judgmentality wrote: post #56

Earlier quoted context omitted.

> 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?

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).

Re: Covid-19: The T Cell Story

#138

Post by hcknwscommenter » Sun, Jun 21, 2020, 2:11 PM UTC

nodamage wrote: post #128

hcknwscommenter wrote: post #121

Earlier quoted context omitted.

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.

Re: Covid-19: The T Cell Story

#139

Post by acqq » Sun, Jun 21, 2020, 9:29 PM UTC

mlyle wrote: post #136

acqq wrote: post #135

Earlier quoted context omitted.

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 evid…

> 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.

Re: Covid-19: The T Cell Story

#140

Post by mlyle » Mon, Jun 22, 2020, 12:50 AM UTC

acqq wrote: post #139

mlyle wrote: post #136

Earlier quoted context omitted.

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 evid…

> 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.

From the very first link:

> The herd immunity threshold (HIT) defines the percentage of the population that needs to be immune to reverse epidemic 15 growth and prevent future waves. Figure 3 shows the expected downward trends in the HIT for SARS-CoV-2 as the coefficients of variation of the gamma distributed susceptibility or exposure are increased between 0 and 4 (to assess robustness to changing the type of distribution see Figure S22 for equivalent plots with lognormal distributions). While herd immunity is expected to require 60-70% of a homogeneous population to be immune given an R0 between 2.5 and 3, 20 these percentages drop to the range 10-20% for CVs between 2 and 4.

Curvefitting from COVID-19 incident waves, past SARS experiences, surveying of contact tracing data, pre-epidemic mobility data in the population, etc, all point to CVs around 3 which corresponds to a herd immunity threshold of 15% or so (hence the paper's 10-20% range). I think this is optimistic and a threshold of more like 30-35% is likely, which with durable aspects of behavior change might really end up being ~25%.