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)
Thread on a myth Jay Bhattacharya (@DrJBhattacharya) continues to peddle to undermine confidence in public health agencies and to suit his policy agenda.
The myth may undermine responses to future public health emergencies.
Reporting systems are not perfect, so they sometimes miss infected people. That makes reported cases less than total infections, and thus CFR is higher than IFR.
The WHO was open about this since the early stages of the pandemic: