The 'herd immunity threshold' is the proportion of people who need to be immune to infection in order for the pandemic to not accelerate, even if we lived life like usual.
Proteins known as antibodies (+ the immune cells that make them, such as B cells / plasma cells) are crucial for preventing re-infection, + thus to getting herd immunity.
Seroprevalence studies measure how many people have increased antibodies levels
So over-estimating the proportion of people with increased antibody levels (i.e. seroprevalence) is dangerous for at least 2 reasons:
1) under-estimating IFR by over-estimating the number of infected people 2) over-estimating how close society is to herd immunity
6/P
Early in the pandemic, Ioannidis under-estimated the risk of IFR by giving unrealistic under-estimates of both IFR and the number people SARS-CoV-2 would infect.
That brings us to his newer paper. He + his co-authors show previously infected people are at lower risk of infection than people who were not previously infected.
Makes sense, since antibodies increasing after infection + help prevent re-infection.
Similar pattern in Austria overall months later, with an IFR of ~0.5% and ~5% seroprevalence.
(with Ioannidis' method under-estimating IFR by not including a long enough lag)
One of the leading SARS-CoV-2 lab conspiracy theorists from DRASTIC, @TheSeeker268, has a new article. I want to focus on its discussion of miners since it shows how ridiculous conspiracists can be.
Like many other aspects of lab leak conspiracy theories, the miners point was debunked for a year or more. But conspiracists peddle it anyway, hoping people are uninformed, or paranoid, or... enough to fall for it.
"Multianalyte serology in home-sampled blood enables an unbiased assessment of the immune response against SARS-CoV-2" nature.com/articles/s4146…
Context:
Public Health Agency of Sweden:
"Globally, it is estimated that 0.5–1 percent of those who are infected with COVID-19 die" web.archive.org/web/2020103000…
On other threads I criticized estimates of COVID-19's fatality. Here I'll highlight the best estimate I've seen:
0.9% from Neil Ferguson's team at Imperial College.
Ideologues often criticize the 0.9% estimate in order to downplay the severity of COVID-19 + evade policies they dislike. John Ioannidis resorted to that
In the slide below, Ioannidis discusses age-specific IFR (infection fatality rate), i.e. what proportion of SARS-CoV-2-infected people die of the disease COVID-19 at various ages.