So the test correction based on FINDDx increases the Gangelt IFR by ~4, and/or renders Gangelt less relevant since corrected seroprevalence overlaps with 0%.
There are other cases in which using FINDDx decreases IFR. For example:
Based on the above paper's FINDDx-based analysis, some of the worse offenders in terms of sensitivity (i.e. false negatives) at particular times post-infection are:
Sweden did not lockdown in response to COVID-19. Many politically-motivated COVID-19 contrarians try to support Sweden's policy by making misleading comparisons between Sweden + other countries. This thread will address that.
A lot of COVID-19 contrarians abuse the idea of "cross-reactivity" to make SARS-CoV-2 (the virus that causes COVID-19) look less dangerous than it really is. Many of them do this to avoid policies they dislike, like lockdowns.
Immune cells known as T cells and B cells have receptors that recognize viruses.
Think of the receptors as a lock, + portions of the virus as a key; i.e. the lock (receptor) binds to a specific key (virus region), + not to other keys
Even if you've never been infected with a virus, bacteria, etc., you almost certainly have T + B cells that recognize it.
When you're first infected, those cells (especially B cells) take a few days to increase in number (and activity) + generate their full immune response.
Interesting method from @GidMK and co-authors for calculating IFR from PCR-based cases.
(IFR is the proportion of SARS-CoV-2-infected people who die COVID-19;
PCR measures viral genetic material in people;
Cases are people who were infected)
The basic idea is that some countries had relatively few infected, and tested with PCR so thoroughly, that their PCR testing got a relatively large proportion of infected people.
Their method yields a range of IFR values consistent with those from serology [i.e. antibody testing], which provides independent validation for their method.
green: their PCR-based methods
blue: Serology-based estimates
For months, COVID-19 contrarians spread misinformation about the immune system and COVID-19.
So I'll combine some rebuttal points I've made elsewhere, with a focus on T cells.
The 'herd immunity threshold' is the number of people who need to be immune to infection, in order for 'infections per unit time' to stop increasing, at baseline.
So for the graph below:
y-axis = # of new infections per day
x-axis = time
This is under 'baseline' conditions; i.e. people don't change their behavior in response to infection, + no further public health interventions (ex: lockdown).