I made a little different, simplified take on the matter:
For Radvac to be net useful, it needs that following is true:
p(RV prevents Covid)*p(user gets Covid [is exposed to Covid such that it would lead to infection])*p(Covid causes long term harm) > p(RV causes long term harm)
p(RV harm) is currently from the RV paper likely less than 1/10000, cited example is Pandemrix that caused long term harm of narcolepsy with 1/16000 if you had Swedish or Finnish genome.
p(Covid harm) is high in old people, where you can die with up to 25% probability, but for most of young people around here long Covid would seem to dominate and that seems to be maybe 1%. Long Covid probability seems to be not well found, and this seems a likely direction for improving decision with better data.
with these presets we get:
p(RV prevents Covid)*p(user gets Covid) > p(RV harm)/p(Covid harm) ⇔
p(RV prevents Covid)*p(get Covid) > 0,0001⁄0,01 = 0,01
from this, we get 3 inequalities as boundary conditions:
(presume scenario where getting Covid is max, that is 100% ⇒ prevention needs to be > 0,01; vice versa)
p(RV prevents Covid) > 0,01
p(get Covid) > 0,01
p(RV prevents Covid)*p(get Covid) > 0,01
so with current boundary conditions the key thing to find out with Radvac is how likely it is to cure Covid. This needs to be shown likely to be over 1% or it should not be used unless other boundary conditions can be shown to differ.
An aside: this same calculation applies to all other vaccines, which is why the effort has been put into making sure p(harm from vaccine) is ascertained to be much less than 1/10000. This making sure the vaccine harms the least is about necessary condition for mass vaccinations to be net useful for the participants themselves. This is why we have used 1 year+ for safety testing, which gives us way better and lower prior for vaccine harm than 1/10000. If you get no long term harm from N trial persons, then per succession rule your naive prior is that p(harm) < 1/(N+2).
A friend offered that page 7 of white paper could maybe be used to deduce that Radvac would prevent Covid with ~40%.
This would mean the decision boundaries would get to
p(Covid)*40% > 0.01 ⇔
p(Covid) > 0.01/0.40 ⇔
p(Covid) > 0.025
so then you would need your chance to get Covid to be over 2.5% for the use to be net beneficial.
If we also presume a 80+ year old person who has 25% probability of death given Covid, then it becomes
so for them the chance to get Covid before official vaccination would need to be over 0.001 for it to be net beneficial with these boundary conditions.
I made a little different, simplified take on the matter:
For Radvac to be net useful, it needs that following is true: p(RV prevents Covid)*p(user gets Covid [is exposed to Covid such that it would lead to infection])*p(Covid causes long term harm) > p(RV causes long term harm)
p(RV harm) is currently from the RV paper likely less than 1/10000, cited example is Pandemrix that caused long term harm of narcolepsy with 1/16000 if you had Swedish or Finnish genome. p(Covid harm) is high in old people, where you can die with up to 25% probability, but for most of young people around here long Covid would seem to dominate and that seems to be maybe 1%. Long Covid probability seems to be not well found, and this seems a likely direction for improving decision with better data.
with these presets we get: p(RV prevents Covid)*p(user gets Covid) > p(RV harm)/p(Covid harm) ⇔ p(RV prevents Covid)*p(get Covid) > 0,0001⁄0,01 = 0,01
from this, we get 3 inequalities as boundary conditions: (presume scenario where getting Covid is max, that is 100% ⇒ prevention needs to be > 0,01; vice versa)
p(RV prevents Covid) > 0,01
p(get Covid) > 0,01
p(RV prevents Covid)*p(get Covid) > 0,01
so with current boundary conditions the key thing to find out with Radvac is how likely it is to cure Covid. This needs to be shown likely to be over 1% or it should not be used unless other boundary conditions can be shown to differ.
An aside: this same calculation applies to all other vaccines, which is why the effort has been put into making sure p(harm from vaccine) is ascertained to be much less than 1/10000. This making sure the vaccine harms the least is about necessary condition for mass vaccinations to be net useful for the participants themselves. This is why we have used 1 year+ for safety testing, which gives us way better and lower prior for vaccine harm than 1/10000. If you get no long term harm from N trial persons, then per succession rule your naive prior is that p(harm) < 1/(N+2).
A friend offered that page 7 of white paper could maybe be used to deduce that Radvac would prevent Covid with ~40%.
This would mean the decision boundaries would get to p(Covid)*40% > 0.01 ⇔ p(Covid) > 0.01/0.40 ⇔ p(Covid) > 0.025 so then you would need your chance to get Covid to be over 2.5% for the use to be net beneficial.
If we also presume a 80+ year old person who has 25% probability of death given Covid, then it becomes
p(RV works)*p(get Covid)*p(Covid harm) > p(RV harm) ⇔ p(Covid)*40%25% > 1/10000 ⇔ p(Covid) > 0.0001/(0.40.25) = 0.001
so for them the chance to get Covid before official vaccination would need to be over 0.001 for it to be net beneficial with these boundary conditions.