My model now says it’s a hybrid. People have different levels of antibody and other responses to the vaccines, which means some people are effectively fully immune (at least for a while), others get more limited protections
This is definitely an important question but it doesn’t seem to me as so wide open a question. I think the prior (i.e., established POV of “Science”) is just your model, and I think the evidence is consistent with that. The natures of the humoral and cell-mediated immune systems would seem to suggest that if you have few or low-capability antibodies/cell instructions, you’d be more likely to get sick, and that at a critical mass, you’re functionally immune because anything that enters will get dealt with (e.g., think border patrol). This would look like a sigmoid curve relating neutralization titers to vaccine efficacy.
If you take a snapshot of neutralizing titers on average induced by a vaccine vs. efficacy, then you could naturally wonder if this is just because of a heterogeneous immune response in the population (and/or sampling over time, e.g., although the average time since dose may be X, some samples may have been taken at X+30 days and therefore have lower levels from natural clearance over time) or if it’s simply probabilistic. Once you see titers over time and/or for multiple vaccines, or at the individual level for multiple people/samples, you can test whether that variability matters, and you can find that indeed immune responses are heterogeneous (not just binary though), resulting in functional immunity around a threshold titer level and probabilistic immunity below that (and as commonly assessed, those categorized with serological non-responsiveness below a lower threshold). Not surprisingly, the titer vs. efficacy relationship is sigmoid.
In fact, neutralization titers represent the amount you can dilute a sample while still preventing infection in 50% of inoculations (the more you can water it down while to get it down to 50% “efficacy,” the more antibodies there must be), so there is a probabilistic component even at that point (or at least error terms to our model), but certainly “the more [antibodies], the merrier [the human],” and at some point you have so many antibodies floating around (or your body can whip them up on a dime, for other diseases) that you’re functionally immune.
The case isn’t closed, but your model is the prior, the prior is your model. Woo!
This is definitely an important question but it doesn’t seem to me as so wide open a question. I think the prior (i.e., established POV of “Science”) is just your model, and I think the evidence is consistent with that. The natures of the humoral and cell-mediated immune systems would seem to suggest that if you have few or low-capability antibodies/cell instructions, you’d be more likely to get sick, and that at a critical mass, you’re functionally immune because anything that enters will get dealt with (e.g., think border patrol). This would look like a sigmoid curve relating neutralization titers to vaccine efficacy.
If you take a snapshot of neutralizing titers on average induced by a vaccine vs. efficacy, then you could naturally wonder if this is just because of a heterogeneous immune response in the population (and/or sampling over time, e.g., although the average time since dose may be X, some samples may have been taken at X+30 days and therefore have lower levels from natural clearance over time) or if it’s simply probabilistic. Once you see titers over time and/or for multiple vaccines, or at the individual level for multiple people/samples, you can test whether that variability matters, and you can find that indeed immune responses are heterogeneous (not just binary though), resulting in functional immunity around a threshold titer level and probabilistic immunity below that (and as commonly assessed, those categorized with serological non-responsiveness below a lower threshold). Not surprisingly, the titer vs. efficacy relationship is sigmoid.
In fact, neutralization titers represent the amount you can dilute a sample while still preventing infection in 50% of inoculations (the more you can water it down while to get it down to 50% “efficacy,” the more antibodies there must be), so there is a probabilistic component even at that point (or at least error terms to our model), but certainly “the more [antibodies], the merrier [the human],” and at some point you have so many antibodies floating around (or your body can whip them up on a dime, for other diseases) that you’re functionally immune.
The case isn’t closed, but your model is the prior, the prior is your model. Woo!