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[1/17] Let's talk about herd immunity, EVIDENCE only. That 70% usually quoted assumes 100% of people are susceptible to infection based on a very restricted model (SIR) that has served us well over time.
[2/17] That 70% can be composed of several immunity traits. From barrier immunity, humoral immunity (antibodies), or cellular immunity. They can be acquired via infection, vaccination, or cross-infection.
[3/17] We know that estimating when we reached that 70% is hard, because... cross-reactivity between pathogens could diminish the susceptible types, barrier immunity is difficult to quantify, ...
[4/17] ... cellular immunity is difficult to observe outside of laboratory environments, the humoral response may decay over time (bad for vaccines), etc.
[5/17] We try to estimate that based on finding the source of infection (antigen tests like PCR), and seroprevalence studies (looking for antibodies) and T-Cell specific reactivity to antigens.
[6/17] Known sources of errors for PCR are: PCR response decay over time (if you didn't catch the infection when it happens, you won't see it), you do not test everyone, as some may not show symptoms ...
[7/17] That doesn't mean they are not infected, but we can argue that those carriers have not developed the sickness. Using confirmed cases, therefore, it is a lower bound.
[8/17] In the case of COVID19, we have observed that PCR positives without developing the sickness could be higher than 80% depending on actual demographics.
[9/17] Known sources of error for Antibody testing are: Tests are not 100% specific and/or sensible in real-life scenarios (outside of the lab), we perform them through population sampling, ...
[10/17] Assuming both are great, antibodies for COVID19 are shown to fade over time (and quite fast), therefore 2 successive measurements for the same person may be positive and then negative.
[11/17] This means that if today I have to say 11% seroprevalence, and then I take the same measurement with the same people, some may have already gone negative while others are positive. Sampling the same population is key.
[12/17] Is the seroprevalence then 11%? Probably no, it may be higher, but at the time of measurements, there is no way to know. The dynamic of the immune response may be painting the wrong picture.
[13/17] Therefore, using the seroprevalence status sounded like a good idea a few months ago, but it has proven to also underestimate the actual population that has been infected. Key for understanding herd immunity.
[14/17] A more interesting metric is T-Cell reactivity, Sweden's study shows that there are at least double the amount of immunity than using seroprevalence. BUT,
[15/17] because it is hard to do, we still only have a single big scale estimation of specific T-Cell immunity. That means that variance and mean error is still unknown.
[16/17] Tidbits, when someone says: "We haven't achieved herd immunity because seroprevalence studies say only 11% has been infected" tell them: "There is absolutely NO EVIDENCE your statement is true".
[17/17] If you ask me, IMHO having seen other (not fully proved yet) correlations. I believe there is a high chance that the herd immunity threshold for seroprevalence will be in the 15%-25% range at any given time and the statement to be eventually proven WRONG.
Given all the unknowns though, I may change my opinion as new data is generated. That is the base of the scientific method.
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