There is quite a lot of evidence that vaccination, on average, reduces:
the chance of contracting disease at all compared with those who are not vaccinated (~40-70% for Delta, reduced to maybe ~10-30% for Omicron);
the duration of detectable infection and presumably infectiousness (~20-30%, unknown for Omicron);
the quantity of virus present in respiratory tract, which may affect infectiousness (numbers vary wildly between studies); and
severity of illness in those who contract the disease (as you note).
The problem is not that (1) (2) and (3) don’t exist, the problem is that they weren’t sufficient to prevent widespread transmission, even with large fractions of the population vaccinated and fairly substantial non-medical interventions such as masks and distancing.
One other thing to consider is that in the broader picture virus transmission isn’t exponential or even logistic. Reproduction number R isn’t quite a lie, but it’s a drastic simplification that’s only useful in the early stages of an outbreak.
Associations that lead to transmission are non-uniform and non-random at every scale. Consider R_0 = 10. If one person can spread the virus to 10 other people, who can each spread it to 10 other people, it is very likely that those latter groups substantially overlap so that the second-generation number of infections isn’t 10^2 = 100, but may be only 40. You can see such slowing in every graph of every outbreak in every region, varying in size from towns to continents with the magnitude of the slowdown increasing with scale.
The behaviour of any one outbreak is not the end game, though. COVID will not be contained within the next decade. Everyone should assume that they will sooner or later be exposed to multiple variants in the coming years. Lockdowns, masks, distancing, and current vaccines buy most of us time: time that can be used to improve treatments and make newer vaccines that protect better.
I was presuming that we (and many other readers) are already familiar with such simplistic models.
I don’t know why you are asking me to do calculations using them when my post explicitly notes some of the errors in the assumptions of such models, and how the actual spread of infectious diseases does not follow such models as scale increases.
Let’s assume there were many COVID mutated variants. What is the best model for the average of the spreading path of all those mutations? It is the SIR model, as it has less dependency. More “accurate” models have more assumptions, hypothesis and depended conditions, which are not reliable. In brief, any other models looks more or less like the result of the SIR model. The difference cancels out.
Another reason is, all extra dependent hypothesis will be explored equally at an earlier stage of a research topic. In brief, most trash papers compete each other at the earlier stage and only after some time, a dominate theory/model will be established. The competition process actually is very similar as the process of virus evolution. At the earlier stage, there is no reason to assume a dominate new model yet. Thus, no heterogenous should be assumed.
There is no reason to assume heterogeneous, as the COVID is so new and the information/knowledge about its mutation direction is very shallow till now.
There is quite a lot of evidence that vaccination, on average, reduces:
the chance of contracting disease at all compared with those who are not vaccinated (~40-70% for Delta, reduced to maybe ~10-30% for Omicron);
the duration of detectable infection and presumably infectiousness (~20-30%, unknown for Omicron);
the quantity of virus present in respiratory tract, which may affect infectiousness (numbers vary wildly between studies); and
severity of illness in those who contract the disease (as you note).
The problem is not that (1) (2) and (3) don’t exist, the problem is that they weren’t sufficient to prevent widespread transmission, even with large fractions of the population vaccinated and fairly substantial non-medical interventions such as masks and distancing.
One other thing to consider is that in the broader picture virus transmission isn’t exponential or even logistic. Reproduction number R isn’t quite a lie, but it’s a drastic simplification that’s only useful in the early stages of an outbreak.
Associations that lead to transmission are non-uniform and non-random at every scale. Consider R_0 = 10. If one person can spread the virus to 10 other people, who can each spread it to 10 other people, it is very likely that those latter groups substantially overlap so that the second-generation number of infections isn’t 10^2 = 100, but may be only 40. You can see such slowing in every graph of every outbreak in every region, varying in size from towns to continents with the magnitude of the slowdown increasing with scale.
The behaviour of any one outbreak is not the end game, though. COVID will not be contained within the next decade. Everyone should assume that they will sooner or later be exposed to multiple variants in the coming years. Lockdowns, masks, distancing, and current vaccines buy most of us time: time that can be used to improve treatments and make newer vaccines that protect better.
Please do some simple calculation by using the SIR model. https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology
I was presuming that we (and many other readers) are already familiar with such simplistic models.
I don’t know why you are asking me to do calculations using them when my post explicitly notes some of the errors in the assumptions of such models, and how the actual spread of infectious diseases does not follow such models as scale increases.
Let’s assume there were many COVID mutated variants. What is the best model for the average of the spreading path of all those mutations? It is the SIR model, as it has less dependency. More “accurate” models have more assumptions, hypothesis and depended conditions, which are not reliable. In brief, any other models looks more or less like the result of the SIR model. The difference cancels out.
That’s a strong claim. Do you have any evidence for it?
Another reason is, all extra dependent hypothesis will be explored equally at an earlier stage of a research topic. In brief, most trash papers compete each other at the earlier stage and only after some time, a dominate theory/model will be established. The competition process actually is very similar as the process of virus evolution. At the earlier stage, there is no reason to assume a dominate new model yet. Thus, no heterogenous should be assumed.
There is no reason to assume heterogeneous, as the COVID is so new and the information/knowledge about its mutation direction is very shallow till now.
“the chance of contracting disease at all compared with those who are not vaccinated (~40-70% for Delta, reduced to maybe ~10-30% for Omicron);”
Do you have a link to the peer review papers about the above item?