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.
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?
I agree that CA (especially NorCal) is probably over conservative... but only because there is so much travel that it doesn't matter if they get to zero, when a few travelers form Arizona, Nevada, or LA (ignoring air travel) will bring it all back in a jiffy. What is likely to be the real boom is when kids go back to school... most of them won't get too sick, but most of them have parents or teachers who will. That's your real second wave when everyone is already tired of caring.
Remember, a guy can have unprotected sex with a lot of women before anyone notices they're pregnant. It's probably 3 weeks from infection to ICU and 5 or 6 weeks to death so looking at deaths or even hospitalizations only gives you informations so delayed it's useless when the doubling time can be 3day (and even when it's 10 days). You're up 10x before you know it...
That's what the math says should happen. It's not what the measurements say. If you look on http://91-divoc.com/pages/covid-visualization/ you will see country after country goes linear. The USA for example had 30k new infections most day for a remarkable 2 months.
I have no idea what causes it. It could be measurement error (that's what I first put it down to in the USA). But if it is it damned hard to explain why it has happened over and over again. Russia has been sitting on 9k new cases per day for 2 months now. The UK went through a period of 5k new cases per day for a month.
It's not a universal, or even the most common pattern. But it happens enough to make the statement "the measures of new covoid 19 cases always change exponentially" wrong enough to be effectively useless as a prediction of where things will head.
I think that's a confusion of R0 with Rt, Rt is what is influencing what we observe and simply not a constant, and additionally not the same in different settings or states:
More importantly, for the Rt == 1 (and being constant) the number of cases would be constantly growing linearly. Only for Rt > 1 the growth is exponential.
As long as the Rt is around 1 it is not surprising that we don't see long lasting exponential growth.
But also note that the values calculated there are more an "illustration" of how the past partial data can be fit to some model than a certainty of the present. The model is helpful to allow us to have a possibility to talk about some concept (Rt and the differences in growth), but is not the current truth, especially for the situation where the data isn't complete and there is a delay in obtaining the updates versus what is actually happening at the moment at every place during the ongoing pandemics. Those who aren't in statistics now can be already infected and die in some weeks, or infect others, and we are never sure how much of those there are at the moment, given the constant changes of the behavior and the movements of the people.
[0] https://en.wikipedia.org/wiki/COVID-19_pandemic_in_Massachus...
We will see, I would not bet on it but that is certainly possible.
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:
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.
Same in California - corona is a health care non-issue here, aside from our governor running for President soon, so it's a political issue. So we'll be the last to leave lockdown, and it's looking like Jan. 2022 now.
By non-issue, I mean hospitalizations and deaths according to the official data have been at the same low rate for months, basically flat. California never had a ventilator or ICU shortage at all. (Last time I checked, we had 58 total deaths in SF/Santa Clara County with millions of pre-lockdown arrivals from China.)
In a perfect world, the point of a lockdown is to arrest the spread of a disease entirely. Unfortunately, we're well past that point in the US. Having failed at that, the goal is not just to keep health care utilization below capacity, but also to buy time to mitigate the disease, by stockpiling PPE and health care supplies, implementing robust contact tracing, and establishing policies for individuals and businesses.
California has been prudently relaxing restrictions for a month now, and is now in Stage 3 of the reopening plan.
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.
https://www.imperial.ac.uk/media/imperial-college/medicine/m...
It is simply not true. Some areas level off at 1-2%, some at 5%, 10, 20% or whatever else. It all depends on when strong social distancing measures were taken.
There were only a few places such as NYC or Bergamo, where measures were taken too late and infections went over 20%.
https://www.livescience.com/covid-19-superspreader-singing.h...
This article is just wishful thinking.
The point is however that it is impossible that the choir selected their members based on the criteria of "members can be only these who will be easily infected with the at the moment still unknown disease." There the existence of the superspreading event to "80% of some random selection" disproves the hypothesis that "on average much less than 80% of the population can be infected at once". That's obviously not true.
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).
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.
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.
You are probably correct, but it still seems to be the case that the virus spread is slowing faster than expected even with relaxations of lockdown measures.
I suspect the explanation is complex. It is probably a combination of factors: individual awareness in reducing social contact, northern hemisphere summer, and individual variability.
It's probably not the case that a large proportion of people are immune, but there might be big differences in susceptibility. The examples you mention are either from early in the pandemic where social distancing rules were less habituated, or enclosed environments where transmission is harder to avoid.
I'm just guessing, but this would support the European strategy of initial lockdown followed by moderate social controls if outbreaks become more controllable after the initial surge.
It's hard to compare with Brazil, because the stats coming out of there are poor and they're making less attempt at controlling the virus compared to almost any other country.
My understanding is given that there is a pre existing T cell response that doesn’t mean that you will not be infected or that you will not have antibodies if infected. However it could totally be possible that when infected you are much less likely to show symptoms,die or spread it to others.
It’s a tough hypothesis to completely verify but the dip in deaths and infections in many places in spite of lifting lockdowns lends some support to this theory.
This controversial thesis has support from some experts like Dr Sunetra Gupta who teaches epidemiology at Oxford.
> It’s a tough hypothesis to completely verify but the dip in deaths and infections in many places in spite of lifting lockdowns lends some support to this theory.
This sounds like a pretty weak correlation to be honest. Just because a lockdown is officially lifted doesn't mean everyone suddenly reverts to their old behaviors.
The 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 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.
https://www.smh.com.au/world/asia/new-beijing-outbreak-raise... https://www.worldometers.info/coronavirus/country/sweden/
It's absolutely exhausting, and is easily the weakest element of sites like Hacker News. There is a certain sort of hubris in this industry that just foments this exaggerated sense of relevance to every topic.
I’m very happy that we get lots of opinions from non-experts.
Pretty sure there is a phallacy that describes this line of thought. Multiple ones, probably.
In short, our immune context (genetic phenotype) is unique! We need a lot more data from everyone to start making accurate correlations. We do not measure T-cells, cytokines, mast cells, b-cells, HLA (partly how we potentially make antibodies) at any meaningful level to provide much confidence. Many natural/industrial substances suppress our T-cell responses and generally innate immune system (metals, mold toxins, etc), so we also need to start accounting for those.
It's a long road we have in front of us. Hopefully the medical system supports patient data ownership and research to improve on our obvious ignorance.
[1] https://forums.phoenixrising.me/threads/documentary-undercov...
As I mentioned above, metals & mold toxins can generate a lot of inflammation in some people that manifest as "foggy brain", joint aches, etc. It takes time and diagnostic testing paired with treatment protocols. Some people cannot process Aluminum and Mercury forms out of their body without relying on Glutathione (limited detox pathways, start with HLA genetic SNPs).
It's a good thing most western medicine doctors never prescribe expensive pharmaceuticals long term ... oh wait ;-)
This suggests going back and re-testing for antibodies some populations from tightly packed groups - the cruise ship passengers, warship crew, and nursing home residents. The antibody tests are more accurate than they were two months ago. The key here is to find out how many people, definitely exposed to the virus, not only did not show any symptoms, but did not develop antibodies. That group presumably had some form of pre-existing immunity.
Is anybody doing something like that?
Moreover, even if 100% antibody + rates does not rule out a lot of pre-existing immunity. If I am naturally resistant to coronavirus (e.g., COVID patient sneezes in my face and I get a mild fever 10 days later), I'll probably develop antibodies from an exposure and otherwise be fine, I'd call that pretty damn good pre-existing immunity. According to your logic, that would be "no [pre-]existing immunity at all."
Apparently, no, as there are no observed differences in the percentages of infected when the people who have children or work with children are compared with those who don't. If the stated assumption were true, those that were more exposed to other coronaviruses (which would be expected among those having or being close to kids) would be as a group less prone to be infected. That was apparently not observed.
So the "slowdowns" are just the humans adapting their behavior to reduce their chances of getting infected. When Rt is 1 the growth is linear, as simple as that. Less of such changes in the population behavior, more people are getting infected, faster.
Source: translated transcript of the podcast of Christian Drosten. Hint: also, don't believe what most of the media says that "he said" -- I've seen a lot of "editorialized" reporting of what he says, to the point of the end product being completely opposite of what he actually said. It's that bad. One really has to go to the source and read. Unfortunately, it's a lot of work, as the podcasts are long. So the people who don't read the source tend to have completely wrong idea what he actually said -- that's also why I'm not giving the specific link: if one doesn't invest really a lot of energy and find and carefully the sources, one has more chance to acquire completely wrong conclusions as "highlights."
So while I agree with your general advice about sources, I don't find your "apparently" to be apparent at all.
"Apparently" was used in the sense "I haven't seen and verified the sources myself" (i.e. the specific scientific material that supports the conclusion of an expert) but at least I took the effort to get the claim about that conclusion from a recognized expert directly." That is, I can't give you the ultimate source that the said expert used for that conclusion, but according to my understanding from reading a transcript of his podcast he concluded that and I have chosen to believe him. Experts spend their whole life to achieve expertise. There are a lot of "preprints" and even fast-approved published scientific papers floating around, which eventually (even extremely fast) are recognized to be flawed by the experts, but only after the false claims are repeated across the media. In such a situation, unless we are the experts ourselves, and unless we have infinite time available, our best bet is to trust the experts who are less prone to present or accept false claims. I consider Christian Drosten one of these.
Being curious, I do check some source scientific works myself, I'm just admitting that for that particular claim I haven't checked the sources myself but decided to trust Drosten. I also claim that I at least spent enough energy to read the exact transcript of his talk and avoided to read "journalistic interpretations" -- the expert's statements are very often totally deformed by retelling. If that's not enough for you and you believe that you can catch him in an error, I'd really like to know why you even believe to be able to achieve that. And if you believe that I'm claiming something he hasn't said, you can check the transcripts of his last two podcasts yourself and write here if I made an error, I'll be happy to learn more.
At some level every one of us has to trust some experts, the question is just if we can also recognize these who just claim some expertise but are actually promoting false claims. I believe that Ioannidis, for example, is an example of such, and I also see that other experts agree.
For "as close to the experts as possible" sources I also recommend everything from https://www.microbe.tv They also admit that they do make errors sometimes, because they didn't recognize early enough how serious this virus is going to affect everybody. But getting the coverage from the experts directly allows one to remain much saner than when reading media who regularly completely distort what experts actually say. And even when media accurately quote some single paper, media often falsely reflect what the whole body of knowledge actually is, as in xkcd "Significant" comics.
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.
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.
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.
