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The reason why they are banned is because what he is saying sounds plausible but is very, very likely wrong. First of all, the number of tests isn't growing at

by spion 6y ago

The reason why they are banned is because what he is saying sounds plausible but is very, very likely wrong. First of all, the number of tests isn't growing at the same rate as the number of cases, hospitalizations and deaths: https://imgur.com/2cVrGz7 https://imgur.com/2cVrGz7 You can notice the very low positive rate in mid August (150 000 tests and only 500 cases - even if all false positives, thats 0.3% FP rate) But okay, lets assume that they did something to ramp up the false positive rate, If we look at the daily rate of growth (% increase) for cases, hospitalizations and deaths: https://imgur.com/bEix1Fo https://imgur.com/bEix1Fo We can see that there is a perfect exponential 7% rate of growth starting in September and slowly decaying after. But more than that, the rate of growth in hospitalizations and deaths also follows the same pattern almost perfectly but with a 5 day and 16 day delay, respectively! If you were accidentally getting false positives, you would either not expect hospitalizations and deaths to grow at the same rate as false positives do. Even if they did, you would expect them to grow at exactly the same time without any delays. If there is no causal relationship they should all grow at the same rate, at the same time as any other random subgroup of people To get the delay to happen, you would have to somehow perfectly fine tune the false positives for hospitalizations and deaths as well. In which case you're better off just making numbers up. But more importantly, there is a much simpler explanation, which we get if we super-impose google mobility trends % increase in amount spent staying at home, and we shift cases by 6 days (avg time to symptoms), hospitalizations by 11 and deaths by 22 days: https://imgur.com/UKklUIP https://imgur.com/UKklUIP We can see that the rate of growth is almost perfectly inversely correlated with the % increase in staying at home (with perhaps some light seasonal / immunity / other measures effects included). Additionally we have non-PCR data like the Zoe symptom tracker app largely corraborating more or less the same rate of growth as detected by PCR tests: https://covid.joinzoe.com/data#levels-over-time https://covid.joinzoe.com/data#levels-over-time

spion 6y ago
But more generally, there is a fundamental problem with social media: it unfortunately acts a generator of large amounts of "perfect bullshit". First, it gets thousands of people to generate various conspiracy theories and plausable looking ideas. Many of these will be interesting and have some real merit, but most of them wont. (By itself, this doesn't do much - we're people, we generate ideas and theories, many of them wrong - nothing special about this process). Then, give people the retweet button, and have them act as a filter for the most convincing looking bullshit. This is where things go terribly wrong. Retweets are a powerful amplifier. What gets retweeted? Is it the most throughly researched content? Highly unlikely, most people aren't experts in the subject (heck, I don't even dare go into the mechanics of the theory, only the math and stats) but they will still retweet if they find it interesting, plausible and aligned with what they strongly believe or wish was true. Side note: having some authority (Nobel prize, etc) also helps a lot with this process. Still, remember that social networks won't differentiate whether 1 scientist is actually having this idea or many, and worse, it won't necessarily inform you if many scientists are actually demonstrating how the idea is wrong (unless they make it equally interesting for a retweet) Finally, trick people by giving them dopamine system rewards (notifications for retweets and likes). They inevitably end up surrounded by a group of supporters that praise and retweet what they say. This whole process makes it so much harder for people (even scientists) to go back on what they said if it turns out they were wrong. So what do you end up with? You end up with a system that delivers the most perfect bullshit to the largest number of people. The scientists that tweet the same old boring conservative stuff (words like "likely" often appear) are largely voiceless. I don't think any single person is necessarily at fault in this system. But I do think that the system is a total disaster, and we have to invent something better than this. These simplistic social network designs are failing humanity.
- ekianjo 6y ago
  > These simplistic social network designs are failing humanity. At the same time mainstream media has become pure propaganda. Interesting times.
- mrow84 6y ago
  This is a good point - it is essentially a distributed multiple comparisons problem, further compounded by lots of dubious assessments of significance.
  - marcosdumay 6y ago
    It is evolution in action.
lettergram 6y ago
I’m sorry, don’t want to ban other (especially qualified) viewpoints just because they are “likely wrong”. To his point and yours, how does this compare to normal flu? I don’t know, I don’t see that example in your data. The problem as this highly qualified individual states it, is that they’re looking for a diagnosis and are changing the number of times amplification of the dna occurs over time. Am I sure of his claims, no. But either way, I appreciate hearing both view points. Having minored in bioengineering in school I can understand both arguments and make the determination myself... provided I have the info.
- spion 6y ago
  I don't think bans are the answer, but see my other comment about how social networks end up generating the perfect bullshit and make it hard for scientists to go back on their theory and to realize whether they're wrong or not. Bans don't solve the issue. We have to invent better social networks, where scientists can be free to theorize and be connected with their peers, without getting false awards (retweets, likes). In general as humanity we also need to be more aware of the nature of social networks and how they don't always elevate the best content. As for credentials, you have to realize that the mechanics of social networks are very good at finding and amplifying the few individuals with credentials that have something wrong (but interesting) to say. What you don't get to hear about are the dozens of reviews of that theory, also by experts, that show how its wrong. Because thats just not interesting for a retweet, and its kind of a downer, too.
  - lettergram 6y ago
    I’m fully aware, I actually built something that quantifies what other experts think of your opinion. It’s called InsiderOpinion (insideropinion.com). That being said, the reality is that social media has over stepped as late. I’m literally being forced to “thedonald.win” or some other sh to find broadened perspectives. Perhaps some insight as to why people have trouble believing scientists at the moment... they told everyone “masks and gloves don’t work” (to keep supply for essential workers) then told everyone “make your own masks” (btw studies going back to the early 1900s says masks will work if it’s droplet based) we also now have evidence masks don’t work. We knew this would be the case because back in January and February they confirmed it was airborne. Multiple studies since confirmed this. Basically the virus can survive in the air and our cloth masks aren’t good enough. Part of Biden’s transition team said that on Joe rogan’s podcast in March. Then we had the whole “don’t go into crowds” followed by “oh protests are for a good cause, so it’s okay” and stuff like that. The reality is that the public shouldn’t trust what they are being told. Literally everything has been a lie thus far.
Izkata 6y ago
> But okay, lets assume that they did something to ramp up the false positive rate, If we look at the daily rate of growth (% increase) for cases, hospitalizations and deaths: > https://imgur.com/bEix1Fo https://imgur.com/bEix1Fo > We can see that there is a perfect exponential 7% rate of growth starting in September and slowly decaying after. But more than that, the rate of growth in hospitalizations and deaths also follows the same pattern almost perfectly but with a 5 day and 16 day delay, respectively! What locale is this graph from? Because it does not at all match.. let's say, the US, where there's now been 3 peaks and the number of daily cases doubled each peak, but the daily deaths was highest in the first peak. They are not growing at remotely the same rate over here, even with a 5 or 16 day delay.
- acqq 6y ago
  > let's say, the US, where there's now been 3 peaks and the number of daily cases doubled each peak, but the daily deaths was highest in the first peak. If one tries comparing the US data with the data from other developed countries, what can be seen is that only the third peak in the US has the "realistic" ratio of cases to deaths, that is, one which better corresponds to the ratio from the countries which were able to handle the outbreaks better, as in, being able to detect enough cases early enough, and for that appropriate test capability, adequate to the incidence, seems to be necessary. The approximate expected ratio, worldwide, seems currently (with obviously better testing infrastructure compared to Spring, and as long as the hospitals aren't too stressed) to be 1 case of death per 100 registered "cases", after a delay, and when using some averages across a moving window of data for both both cases and deaths, e.g. 7 day average. In the first peak in the US it was as high as almost 1 death per 14 cases(!!) -- around 32K detected daily cases with 2.2K deaths were the peaks of the averages! In the second peak in the US it was 1 death per 60 cases. Both show that there weren't enough cases detected, compared to some other countries, and that more appropriate level of testing was achieved in the US only during the third wave. But if there are some systematic delays in reporting deaths during the third wave, it can still turn out that what we see at the moment will look worse, once the wave is over. Even that effect is known to exist in the reporting of one European country: Sweden, where the systematic reporting delay of deaths during the wave gives very wrong impression of the ratio as long as the wave is raising, compared to other countries: https://news.ycombinator.com/item?id=25122196 https://news.ycombinator.com/item?id=25122196 There are some signs that such an effect can be in play in the US as well. Additionally, what one has to be aware of is that any statistics over a big population will at different points of time not always proportionally reflect the contribution of the events in different areas -- e.g. New York. That way, some big problems for the local population will appear "less bad" when averaged, which shouldn't be comforting.

by spion 6y ago

spion 6y ago

But more generally, there is a fundamental problem with social media: it unfortunately acts a generator of large amounts of "perfect bullshit". First, it gets thousands of people to generate various conspiracy theories and plausable looking ideas. Many of these will be interesting and have some real merit, but most of them wont. (By itself, this doesn't do much - we're people, we generate ideas and theories, many of them wrong - nothing special about this process). Then, give people the retweet button, and have them act as a filter for the most convincing looking bullshit. This is where things go terribly wrong. Retweets are a powerful amplifier. What gets retweeted? Is it the most throughly researched content? Highly unlikely, most people aren't experts in the subject (heck, I don't even dare go into the mechanics of the theory, only the math and stats) but they will still retweet if they find it interesting, plausible and aligned with what they strongly believe or wish was true. Side note: having some authority (Nobel prize, etc) also helps a lot with this process. Still, remember that social networks won't differentiate whether 1 scientist is actually having this idea or many, and worse, it won't necessarily inform you if many scientists are actually demonstrating how the idea is wrong (unless they make it equally interesting for a retweet) Finally, trick people by giving them dopamine system rewards (notifications for retweets and likes). They inevitably end up surrounded by a group of supporters that praise and retweet what they say. This whole process makes it so much harder for people (even scientists) to go back on what they said if it turns out they were wrong. So what do you end up with? You end up with a system that delivers the most perfect bullshit to the largest number of people. The scientists that tweet the same old boring conservative stuff (words like "likely" often appear) are largely voiceless. I don't think any single person is necessarily at fault in this system. But I do think that the system is a total disaster, and we have to invent something better than this. These simplistic social network designs are failing humanity.

ekianjo 6y ago
> These simplistic social network designs are failing humanity. At the same time mainstream media has become pure propaganda. Interesting times.
mrow84 6y ago
This is a good point - it is essentially a distributed multiple comparisons problem, further compounded by lots of dubious assessments of significance.
- marcosdumay 6y ago
  It is evolution in action.

lettergram 6y ago

I’m sorry, don’t want to ban other (especially qualified) viewpoints just because they are “likely wrong”. To his point and yours, how does this compare to normal flu? I don’t know, I don’t see that example in your data. The problem as this highly qualified individual states it, is that they’re looking for a diagnosis and are changing the number of times amplification of the dna occurs over time. Am I sure of his claims, no. But either way, I appreciate hearing both view points. Having minored in bioengineering in school I can understand both arguments and make the determination myself... provided I have the info.

spion 6y ago
I don't think bans are the answer, but see my other comment about how social networks end up generating the perfect bullshit and make it hard for scientists to go back on their theory and to realize whether they're wrong or not. Bans don't solve the issue. We have to invent better social networks, where scientists can be free to theorize and be connected with their peers, without getting false awards (retweets, likes). In general as humanity we also need to be more aware of the nature of social networks and how they don't always elevate the best content. As for credentials, you have to realize that the mechanics of social networks are very good at finding and amplifying the few individuals with credentials that have something wrong (but interesting) to say. What you don't get to hear about are the dozens of reviews of that theory, also by experts, that show how its wrong. Because thats just not interesting for a retweet, and its kind of a downer, too.
- lettergram 6y ago
  I’m fully aware, I actually built something that quantifies what other experts think of your opinion. It’s called InsiderOpinion (insideropinion.com). That being said, the reality is that social media has over stepped as late. I’m literally being forced to “thedonald.win” or some other sh to find broadened perspectives. Perhaps some insight as to why people have trouble believing scientists at the moment... they told everyone “masks and gloves don’t work” (to keep supply for essential workers) then told everyone “make your own masks” (btw studies going back to the early 1900s says masks will work if it’s droplet based) we also now have evidence masks don’t work. We knew this would be the case because back in January and February they confirmed it was airborne. Multiple studies since confirmed this. Basically the virus can survive in the air and our cloth masks aren’t good enough. Part of Biden’s transition team said that on Joe rogan’s podcast in March. Then we had the whole “don’t go into crowds” followed by “oh protests are for a good cause, so it’s okay” and stuff like that. The reality is that the public shouldn’t trust what they are being told. Literally everything has been a lie thus far.

Izkata 6y ago

> But okay, lets assume that they did something to ramp up the false positive rate, If we look at the daily rate of growth (% increase) for cases, hospitalizations and deaths: > https://imgur.com/bEix1Fo https://imgur.com/bEix1Fo > We can see that there is a perfect exponential 7% rate of growth starting in September and slowly decaying after. But more than that, the rate of growth in hospitalizations and deaths also follows the same pattern almost perfectly but with a 5 day and 16 day delay, respectively! What locale is this graph from? Because it does not at all match.. let's say, the US, where there's now been 3 peaks and the number of daily cases doubled each peak, but the daily deaths was highest in the first peak. They are not growing at remotely the same rate over here, even with a 5 or 16 day delay.

acqq 6y ago
> let's say, the US, where there's now been 3 peaks and the number of daily cases doubled each peak, but the daily deaths was highest in the first peak. If one tries comparing the US data with the data from other developed countries, what can be seen is that only the third peak in the US has the "realistic" ratio of cases to deaths, that is, one which better corresponds to the ratio from the countries which were able to handle the outbreaks better, as in, being able to detect enough cases early enough, and for that appropriate test capability, adequate to the incidence, seems to be necessary. The approximate expected ratio, worldwide, seems currently (with obviously better testing infrastructure compared to Spring, and as long as the hospitals aren't too stressed) to be 1 case of death per 100 registered "cases", after a delay, and when using some averages across a moving window of data for both both cases and deaths, e.g. 7 day average. In the first peak in the US it was as high as almost 1 death per 14 cases(!!) -- around 32K detected daily cases with 2.2K deaths were the peaks of the averages! In the second peak in the US it was 1 death per 60 cases. Both show that there weren't enough cases detected, compared to some other countries, and that more appropriate level of testing was achieved in the US only during the third wave. But if there are some systematic delays in reporting deaths during the third wave, it can still turn out that what we see at the moment will look worse, once the wave is over. Even that effect is known to exist in the reporting of one European country: Sweden, where the systematic reporting delay of deaths during the wave gives very wrong impression of the ratio as long as the wave is raising, compared to other countries: https://news.ycombinator.com/item?id=25122196 https://news.ycombinator.com/item?id=25122196 There are some signs that such an effect can be in play in the US as well. Additionally, what one has to be aware of is that any statistics over a big population will at different points of time not always proportionally reflect the contribution of the events in different areas -- e.g. New York. That way, some big problems for the local population will appear "less bad" when averaged, which shouldn't be comforting.