9 ms·

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 hum

by lsllc 6y ago

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?

redis_mlc 6y ago
> 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.)
- jlmorton 6y ago
  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.
  - dehrmann 6y ago
    They probably meant per day. I think "flat" is a fair characterization of the metric: https://i.imgur.com/zZ7UGVW.png 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.
    - redis_mlc 6y ago
      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.
      - 6y ago
- hcknwscommenter 6y ago
  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.
remote_phone 6y ago
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.
- guscost 6y ago
  > 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.
- lsllc 6y ago
  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_Massachusetts#Statistics https://en.wikipedia.org/wiki/COVID-19_pandemic_in_Massachus...
elliekelly 6y ago
It’s my understanding that Massachusetts testing has been way down recently which could account for the lower number of positive tests.
- ddeck 6y ago
  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 https://covidtracking.com/data/state/massachusetts
kurthr 6y ago
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...
- rstuart4133 6y ago
  > 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/ 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.
  - kurthr 6y ago
    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.
acqq 6y ago
> 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/ 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.
- lsllc 6y ago
  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.
WildParser 6y ago
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.
- phreeza 6y ago
  If people are infectious for some finite number of days, f'/f will always tend to zero, even if f' stays constant.
  - WildParser 6y ago
    It falls down exponentially! Just look at it. If you do a bit of math you will see that f will just converge.
    - phreeza 6y ago
      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.
      - WildParser 6y ago
        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.
jacobush 6y ago
That's what bertheb says, R0 depends on the other variables, like density, climate etc etc.

by lsllc 6y ago

redis_mlc 6y ago

> 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.)

jlmorton 6y ago
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.
- dehrmann 6y ago
  They probably meant per day. I think "flat" is a fair characterization of the metric: https://i.imgur.com/zZ7UGVW.png 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.
  - redis_mlc 6y ago
    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.
    - 6y ago
hcknwscommenter 6y ago
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.

remote_phone 6y ago

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.

guscost 6y ago
> 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.
lsllc 6y ago
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_Massachusetts#Statistics https://en.wikipedia.org/wiki/COVID-19_pandemic_in_Massachus...

elliekelly 6y ago

It’s my understanding that Massachusetts testing has been way down recently which could account for the lower number of positive tests.

ddeck 6y ago
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 https://covidtracking.com/data/state/massachusetts

kurthr 6y ago

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...

rstuart4133 6y ago
> 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/ 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.
- kurthr 6y ago
  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.

acqq 6y ago

> 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/ 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.

lsllc 6y ago
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.

WildParser 6y ago

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.

phreeza 6y ago
If people are infectious for some finite number of days, f'/f will always tend to zero, even if f' stays constant.
- WildParser 6y ago
  It falls down exponentially! Just look at it. If you do a bit of math you will see that f will just converge.
  - phreeza 6y ago
    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.
    - WildParser 6y ago
      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.

jacobush 6y ago

That's what bertheb says, R0 depends on the other variables, like density, climate etc etc.