There is quite a lot of evidence that vaccination, on average, reduces:
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.
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?
By Luc Montagnier and Jed Rubenfeld
Jan. 9, 2022 5:20 pm ET
----WSJ
I did not believe the user of this website was really about reason, as this post was devoted greatly.
Clearly the Exponential function dominate the linear function (benefit of vaccines/re-infect immune) in UK.
https://twitter.com/DrEricDing/status/1473752247376961543
For knowing this result, u need not to have an "accurate" model with many dependence assumptions.
Omicron doubles in 1.5 to 3 days in areas. https://www.reuters.com/business/healthcare-pharmaceuticals/omicron-cases-doubling-15-3-days-areas-with-local-spread-who-2021-12-18/
Mathematically, the consequence caused by the transmission >>>>> death rate.
Transmission rate doesn't really seem like the important variable unless you care about the effect on mortality. If your goal is to reduce transmission, then the important statistic is overall transmission. If (as seems likely with Omicron), ~100% of the population is going to get it eventually, then trying to reduce the speed at which people get it (the transmission rate) only matters if that effects mortality.
Or to put it another way, if you have a disease where hospitalization doesn't effect mortality (or a sane world where hospitals can scale up with "only" a year and a half of lead time), and a disease so transmissable that everyone is going to get it, then the transmission rate hardly matters since x% of people dying now vs x% of people dying over the next few months isn't a big difference.
It is not possible that 100% will get it.
https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology
https://twitter.com/DrEricDing/status/1469723185084084225?s=20
Please check the calculation part. I wish the health system would not stress out by the omicron.
I am surprised that most people did not read the virus spreading dynamics even after two years of COVID. For any large scale plague, the transmission will cause the most life loss. Assuming the serenity of COVID is reduced to the same level as a flu and ignore the long COVID.
Now, Think about a ten times transmissible swan flu. Individual tends to think it is acceptable. However, it could cause millions of life loss in US alone.
To my best knowledge, there is no data evidence that the vaccines do prevent the transmission of the delta and Omicron (Please fix if I was wrong). The effect of reduce the mortality rate is LINEAR. A simple calculation suggests that the LINEAR contribution could be dominate by the transmission rate increase due to the exponential effects. In other words, the life saving due to the vaccine is not comparable to the more life loss due to the transmission rate increase.
Thus, the priority of the policy should be reduce the transmission, instead of the linear factors. Am I wrong?
By the way, I am not anti-vaccine.