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.
At the same time mainstream media has become pure propaganda. Interesting times.
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.
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.
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.
* WHO recommended that sick people wear (surgical) masks since February at home to reduce the chances of spreading the virus in the household - if they can't fully isolate. (You can check this using archive.org - I don't have the link readily at hand, unfortunately)
* Yes, PPE was a concern but also it was thought that there is no spread without symptoms initially. Which was the main reason why masks were not recommended for the wider population:
* non-respirators don't really *protect* the wearer
* respirators like N95 do but PPE is in short suply
* if you have symptoms, wearing a mask helps, sure, but best to fully isolate as much as possible.
I'm sure there were some people and groups that connected the PPE and "masks don't work and thats why they are not recommended" but it was certainly not a blatant lie - it was really thought they wouldn't help and isolation for the infected is much better. With presymptomatic (and therefore possibly asymptomatic) spread and the realization of millions infected everywhere, everyone wearing masks became a viable option.
Aerosols are a whole other topic I won't get into (and I don't really have the expertise for it either!) but suffice to say from my understanding its not really as simple as "works" / "doesn't work".
Yes there is a problem with science failing us here as well, as the bar for evidence is simply set too high and the processes are too slow to deal with a pandemic. This is true for medications too...
What does this mean?
https://bitesizebio.com/24581/what-is-a-ct-value/
If you have to cycle 40 times for example, that is like finding a needle in a haystack, i.e. not understood how relevant such high precision is in terms of weather someone is actively spreading, or had it a month ago, etc.
Flu is really serious, which is why we have nationally co-ordinated campaigns of surveillance and monitoring, and then rapid vaccination development, and then programmes to immunise as many vulnerable people as possible.
So far, we think covid-19 is about 10 times worse than flu.
On an average year in the UK flu kills about 10,000 people. So far Covid has killed many more - at least 50,000.
But this is using an inclusive definition for "killed by flu" that will include many people, and it's using a restrictive definition for "killed by covid" that will undercount deaths.
To give an idea of how flu in the UK is counted you can look at this report: https://assets.publishing.service.gov.uk/government/uploads/...
> 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.
Its similar to Rt but its much easier to calculate. To get Rt, you need to estimate how many people are infected on average by one person at any point in time. This is usually done by simulating the process in various ways.
To get the daily rate of growth on the other hand, all you need to do is look at the actual numbers. Assuming you had 110 cases today and 100 cases yesterday, the current rate of growth is 10%. Since the rate calculated this way will be a bit erratic, I use some averaging and exponential smoothing to get the general trend.
The code to generate these graphs is available here: https://colab.research.google.com/drive/1QDZgdGkOzs6dP74-iED... - it uses the UK coronavirus API and google mobility trends reports directly from their respective URLs, so it should be easy to make a copy of the notebook, review/modify it and re-run it.
Why is the ratio of positive cases to hospitalizations different? I'd say this is mostly due to testing capacities being very different between now and March. In March in the UK for example it was impossible to get a test unless things are so bad that you need to get hospitalized. This is very different from now where anyone with symptoms (or just saying they have symptoms) can get tested.
Based on the graph, another reason for the different ratio between cases and deaths seems to be the period July to mid August. This was a period where the growth rate of cases didn't match the growth rate of hospitalizations and deaths at all. This likely means that parts of the population that wasn't that succeptable to severe outcomes were getting infected (which makes sense given that bars were opened up again at the beginning of July - although that is speculation)
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 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.
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
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
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
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