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So you're saying that places like NYC have reached natural herd immunity and can safely be opened back up? They reached an estimated R0 of 5.6 early on due to t

So you're saying that places like NYC have reached natural herd immunity and can safely be opened back up? They reached an estimated R0 of 5.6 early on due to their population density. From that number, they'd have to reach > 80% infected to hit herd immunity. They got hit hard, but they're nowhere near that level. If they went back to normal all of a sudden, there'd be a lot more death.

gloriosoc 6y ago
Oh I never said anywhere could be safely opened back up- that's a leap. I say quite the opposite- people should be careful and not open schools until cases drop to near zero.
- jachee 6y ago
  If the herd can't resume regular herd behavior without risking widespread infection, does it have immunity at all?
  - gloriosoc 6y ago
    Eventually we will be able to but cases should drop further. Herd immunity doesn't mean zero spread, it means R<1 and dropping cases that will eventually become close to zero.
gloriosoc 6y ago
There are parts of NYC with 68% positive antibodies a month ago- so I wouldn't be surprised if a lot more people in NY have been infected than is reported. But not sure I'm following your math from R0 5.6->80%?
- tunesmith 6y ago
  There's a math equation to get from R0 to herd immunity target: (R0-1)/R0 . If NYC is that high - I missed that - then maybe parts of NYC are actually close to the target.
  - lbeltrame 6y ago
    That assumes that the population is homogeneous in susceptibility. Some people argue that it is not the case, and as such the threshold is lower.
    - tunesmith 6y ago
      Well, R0 is by definition different for different populations - density, demographics, etc. R0 and the herd immunity threshold have a clear mathematical relationship; it's just a restating of the definition. So if you argue the threshold is lower for a subpopulation, you're also arguing that R0 is lower for that same subpopulation.
      - abhorrence 6y ago
        It also depends on the interconnectedness of the population. Person A may not be very connected, only being particularly likely to infect their own household, and maybe one or two other people outside. Person B might be highly connected and in a position to infect dozens of people. If you had a population of 80% "Person A" and 20% "Person B", this might average out to an R0 of around 6 -- but the actual reproductive rate would drop rapidly as "Person B"s gain immunity.
        tunesmith 6y ago
        That makes intuitive sense to me, that over the lifetime of an unrestrained virus propagation, the R0 value would start out higher and slowly decrease until the herd immunity threshold is reached. Not sure if that is commonly observed in the epi community but it wouldn't surprise me.
- deleted 6y ago
  [deleted]
- tghw 6y ago
  CDC Data from mid June had NYC at only 20% prevalence. Considering their low new daily cases since then, it's hard to believe they are anywhere near 68%.
  - ericb 6y ago
    So, I think the proposed math is something like: 1/3rd of infected don't show antibodies so that 20% means 30% were infected. Another study showed pre-existing T cell reactivity (maybe immunity?) in 40% of people, so if you combine those you're at 70%.
    - tghw 6y ago
      I find it hard to believe, with the R0 numbers we were seeing initially, that 40% had a significant level of immunity. If the numbers were that high, then R0 among those without memory T-cell immunity would have been significantly higher, which suggests that the level of exposure/immunity necessary for herd immunity to set in would be significantly higher as well.
      - ericb 6y ago
        What you're saying makes sense. Looking at some tables of it, it seems like if we imagine it in a naive population having a 50% higher reproduction number, it seems like it only bumps the herd immunity number by about 10% because it is such a high proportion already, if I'm understanding the charts right. It isn't clear if the numbers come together, but it does make me wonder if it could be close.
  - gloriosoc 6y ago
    NYC data from August has some boroughs as high as 51% https://www1.nyc.gov/site/doh/covid/covid-19-data-testing.page https://www1.nyc.gov/site/doh/covid/covid-19-data-testing.pa.... The 68% comes from this article https://www.nytimes.com/2020/07/09/nyregion/nyc-coronavirus-antibodies.html https://www.nytimes.com/2020/07/09/nyregion/nyc-coronavirus-.... The data appears to be from doctor's offices- not sure we can access that actual data (but I didn't try that hard to get it).
- rallison 6y ago
  That dataset is super useful and interesting, but it has a huge caveat, assuming we're talking about the same dataset (https://www.nytimes.com/2020/08/19/nyregion/new-york-city-antibody-test.html https://www.nytimes.com/2020/08/19/nyregion/new-york-city-an...). Oh, I see where the 68% comes from - https://www.nytimes.com/2020/07/09/nyregion/nyc-coronavirus-antibodies.html https://www.nytimes.com/2020/07/09/nyregion/nyc-coronavirus-... That's from one clinic, for people seeking an antibody test. Anyway, the first link I mention is the broader dataset from all the clinics like this, so it helps smooth out anomalies. But, the big caveat in all of this is that these suffer from selection bias - it's not a randomized sampling of people in each borough. It's still useful data, but it's far from being able to tell one the incidence rate to a reasonable degree of precision. Still, many reasonable inferences one can make.
gloriosoc 6y ago
And sadly, everyone has already died for the most part in NY, >30,000 people.
amluto 6y ago
This isn’t necessarily the case. It’s true in a model in which everyone is equally susceptible, equally likely to interact with any other given person, etc. Modeling an epidemic this way is like modeling a cow as a sphere. There’s an idea that is almost as oversimplified, makes a big assumption, and comes up with a much lower percentage: suppose that each person is just as likely to interact with any other given person, but that people come in two types: susceptible and naturally immune. Then the initial R0 in fact represents a more contagious disease than the first model (as the probability of infecting a susceptible contact needs to be higher for a given R0), but the disease stops increasing exponentially at a lower infection rate. In particular, once the entire susceptible population has gotten the disease, it’s over, and that’s less than 100% of people. Both of these models are hugely oversimplified, and neither one is likely to be fully correct.

gloriosoc 6y ago

Oh I never said anywhere could be safely opened back up- that's a leap. I say quite the opposite- people should be careful and not open schools until cases drop to near zero.

jachee 6y ago
If the herd can't resume regular herd behavior without risking widespread infection, does it have immunity at all?
- gloriosoc 6y ago
  Eventually we will be able to but cases should drop further. Herd immunity doesn't mean zero spread, it means R<1 and dropping cases that will eventually become close to zero.

gloriosoc 6y ago

There are parts of NYC with 68% positive antibodies a month ago- so I wouldn't be surprised if a lot more people in NY have been infected than is reported. But not sure I'm following your math from R0 5.6->80%?

tunesmith 6y ago
There's a math equation to get from R0 to herd immunity target: (R0-1)/R0 . If NYC is that high - I missed that - then maybe parts of NYC are actually close to the target.
- lbeltrame 6y ago
  That assumes that the population is homogeneous in susceptibility. Some people argue that it is not the case, and as such the threshold is lower.
  - tunesmith 6y ago
    Well, R0 is by definition different for different populations - density, demographics, etc. R0 and the herd immunity threshold have a clear mathematical relationship; it's just a restating of the definition. So if you argue the threshold is lower for a subpopulation, you're also arguing that R0 is lower for that same subpopulation.
    - abhorrence 6y ago
      It also depends on the interconnectedness of the population. Person A may not be very connected, only being particularly likely to infect their own household, and maybe one or two other people outside. Person B might be highly connected and in a position to infect dozens of people. If you had a population of 80% "Person A" and 20% "Person B", this might average out to an R0 of around 6 -- but the actual reproductive rate would drop rapidly as "Person B"s gain immunity.
      - tunesmith 6y ago
        That makes intuitive sense to me, that over the lifetime of an unrestrained virus propagation, the R0 value would start out higher and slowly decrease until the herd immunity threshold is reached. Not sure if that is commonly observed in the epi community but it wouldn't surprise me.
deleted 6y ago
[deleted]
tghw 6y ago
CDC Data from mid June had NYC at only 20% prevalence. Considering their low new daily cases since then, it's hard to believe they are anywhere near 68%.
- ericb 6y ago
  So, I think the proposed math is something like: 1/3rd of infected don't show antibodies so that 20% means 30% were infected. Another study showed pre-existing T cell reactivity (maybe immunity?) in 40% of people, so if you combine those you're at 70%.
  - tghw 6y ago
    I find it hard to believe, with the R0 numbers we were seeing initially, that 40% had a significant level of immunity. If the numbers were that high, then R0 among those without memory T-cell immunity would have been significantly higher, which suggests that the level of exposure/immunity necessary for herd immunity to set in would be significantly higher as well.
    - ericb 6y ago
      What you're saying makes sense. Looking at some tables of it, it seems like if we imagine it in a naive population having a 50% higher reproduction number, it seems like it only bumps the herd immunity number by about 10% because it is such a high proportion already, if I'm understanding the charts right. It isn't clear if the numbers come together, but it does make me wonder if it could be close.
- gloriosoc 6y ago
  NYC data from August has some boroughs as high as 51% https://www1.nyc.gov/site/doh/covid/covid-19-data-testing.page https://www1.nyc.gov/site/doh/covid/covid-19-data-testing.pa.... The 68% comes from this article https://www.nytimes.com/2020/07/09/nyregion/nyc-coronavirus-antibodies.html https://www.nytimes.com/2020/07/09/nyregion/nyc-coronavirus-.... The data appears to be from doctor's offices- not sure we can access that actual data (but I didn't try that hard to get it).
rallison 6y ago
That dataset is super useful and interesting, but it has a huge caveat, assuming we're talking about the same dataset (https://www.nytimes.com/2020/08/19/nyregion/new-york-city-antibody-test.html https://www.nytimes.com/2020/08/19/nyregion/new-york-city-an...). Oh, I see where the 68% comes from - https://www.nytimes.com/2020/07/09/nyregion/nyc-coronavirus-antibodies.html https://www.nytimes.com/2020/07/09/nyregion/nyc-coronavirus-... That's from one clinic, for people seeking an antibody test. Anyway, the first link I mention is the broader dataset from all the clinics like this, so it helps smooth out anomalies. But, the big caveat in all of this is that these suffer from selection bias - it's not a randomized sampling of people in each borough. It's still useful data, but it's far from being able to tell one the incidence rate to a reasonable degree of precision. Still, many reasonable inferences one can make.

gloriosoc 6y ago

And sadly, everyone has already died for the most part in NY, >30,000 people.

amluto 6y ago

This isn’t necessarily the case. It’s true in a model in which everyone is equally susceptible, equally likely to interact with any other given person, etc. Modeling an epidemic this way is like modeling a cow as a sphere. There’s an idea that is almost as oversimplified, makes a big assumption, and comes up with a much lower percentage: suppose that each person is just as likely to interact with any other given person, but that people come in two types: susceptible and naturally immune. Then the initial R0 in fact represents a more contagious disease than the first model (as the probability of infecting a susceptible contact needs to be higher for a given R0), but the disease stops increasing exponentially at a lower infection rate. In particular, once the entire susceptible population has gotten the disease, it’s over, and that’s less than 100% of people. Both of these models are hugely oversimplified, and neither one is likely to be fully correct.