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After the Covid fiasco I'd fully expect that over cautios bioethics becomes itself controversial. We're still counting extra hundreds of thousands dead because

After the Covid fiasco I'd fully expect that over cautios bioethics becomes itself controversial. We're still counting extra hundreds of thousands dead because regulatory agencies refused to even consider challenge trials, not to mention dropping the ball in almost any possible way: actively discouraging masks, forbidding rapid tests, and lately hurting trust in vaccines with a fully reflex banning of AZ and JJ that had a clear outcome from the beginning. Since nazi germany and Mao I don't think there was a category of people that caused as many death and suffering as bioethicists. Moderna was clearly ready to bring a product to market, but it took a pandemic for us to get rna vaccines. God know how many people died needlessly because people created a whole career path by chanting "thalidomide".

f6v 5y ago
I think slow vaccine rollout was caused more by logistics issues. It’s just not easy to produce billion doses on a whim.
- saalweachter 5y ago
  I think at the current rates of production were still looking at another, what, 36 months to fully vaccinate the world?
- radu_floricica 5y ago
  The pandemic has an exponential growth. Starting vaccinations even a few weeks earlier would have saved many lives by lowering the curve earlier. Starting them three months earlier might have... actually, probably would have skipped the winter wave completely, at least in the developed countries. Which would have freed extra doses for the developing countries as well - it's an interlinked chain that works better earlier you start, because that's how vaccines work. They're a preventive measure. And the cost? Infecting 100 or 1000 volunteers in low risk categories, with an expected death count of under 1. There were websites where people registered in thousands for this.
  - saalweachter 5y ago
    You can't test only low risk individuals; that is an absurdly skewed sample set. You'd completely miss the safety and efficacy data on exactly the people you want to vaccinate first.
    - radu_floricica 5y ago
      I feel there's a paradigm difference here. Classical scientific method says "make hypotheses, test, confirm or infirm". That was the golden standard for 400 years. It works very well, but it has weaknesses (what doesn't). First is that it doesn't specify how you should select the hypotheses on which you want to perform (costly) experiments, and the second is that it's very bad at processing partial data. You can confirm on infirm - with some complicated math on how good the experiment was. Nowadays we have much better protocols, mostly based on the Bayesian method which is much more robust on getting small pieces of information. One of its principles is actually this: no matter how small and useless an experimental result is, it should still budge your understanding of the phenomenon even if it's a tiny bit. There are some caveats here as well, but overall it's a whole different ballgame from the big, old, clunky scientific method. So how does this apply to this situation? You have a cycle of 2 weeks for covid infections. A completely-pulled-out-of-my-ass protocol would involve 100 very-low-risk individuals split into vaccinated and control and infected deliberately with various doses. You get some data from this: how well is the vaccine working? how is the disease progressing depending on initial viral dose? For the next 2 weeks you switch to slightly higher vulnerable populations, again split into control/vaccine. For the next 2 weeks you ditch the control to lower the risk, and add another decade to the age of the population. Now you have good enough data to start vaccinating vulnerable population at large. You keep testing on volunteers, because you're about to vaccinate millions and you NEED extra data, like what's the fastest protocol to vaccinate everybody. Except now you have enough efficacy data to know that you won't kill your vaccinated volunteers, so you start using more mixed populations with higher ages - though the bulk will still be low-risk. And play with whatever variables you need adjusting, like vaccine dosage or booster timing, and adjust the population-at-large protocols based on that. Compare this with what we did, which is literally last century methodology. We pulled out of the ass a protocol: vaccine dose of X, booster timing of 3 weeks. Then we tested it in a large scale long term trial. We confirmed it worked. And used it as-is, and declared that any change will require another large-scale long-term trial. Look at the definition of the scientific method in the beginning of my comment. It's just not common sense in a pandemic, except for bureaucrats minimizing their personal risk. Which is why my rather justified anger with the system.

f6v 5y ago

I think slow vaccine rollout was caused more by logistics issues. It’s just not easy to produce billion doses on a whim.

saalweachter 5y ago
I think at the current rates of production were still looking at another, what, 36 months to fully vaccinate the world?
radu_floricica 5y ago
The pandemic has an exponential growth. Starting vaccinations even a few weeks earlier would have saved many lives by lowering the curve earlier. Starting them three months earlier might have... actually, probably would have skipped the winter wave completely, at least in the developed countries. Which would have freed extra doses for the developing countries as well - it's an interlinked chain that works better earlier you start, because that's how vaccines work. They're a preventive measure. And the cost? Infecting 100 or 1000 volunteers in low risk categories, with an expected death count of under 1. There were websites where people registered in thousands for this.
- saalweachter 5y ago
  You can't test only low risk individuals; that is an absurdly skewed sample set. You'd completely miss the safety and efficacy data on exactly the people you want to vaccinate first.
  - radu_floricica 5y ago
    I feel there's a paradigm difference here. Classical scientific method says "make hypotheses, test, confirm or infirm". That was the golden standard for 400 years. It works very well, but it has weaknesses (what doesn't). First is that it doesn't specify how you should select the hypotheses on which you want to perform (costly) experiments, and the second is that it's very bad at processing partial data. You can confirm on infirm - with some complicated math on how good the experiment was. Nowadays we have much better protocols, mostly based on the Bayesian method which is much more robust on getting small pieces of information. One of its principles is actually this: no matter how small and useless an experimental result is, it should still budge your understanding of the phenomenon even if it's a tiny bit. There are some caveats here as well, but overall it's a whole different ballgame from the big, old, clunky scientific method. So how does this apply to this situation? You have a cycle of 2 weeks for covid infections. A completely-pulled-out-of-my-ass protocol would involve 100 very-low-risk individuals split into vaccinated and control and infected deliberately with various doses. You get some data from this: how well is the vaccine working? how is the disease progressing depending on initial viral dose? For the next 2 weeks you switch to slightly higher vulnerable populations, again split into control/vaccine. For the next 2 weeks you ditch the control to lower the risk, and add another decade to the age of the population. Now you have good enough data to start vaccinating vulnerable population at large. You keep testing on volunteers, because you're about to vaccinate millions and you NEED extra data, like what's the fastest protocol to vaccinate everybody. Except now you have enough efficacy data to know that you won't kill your vaccinated volunteers, so you start using more mixed populations with higher ages - though the bulk will still be low-risk. And play with whatever variables you need adjusting, like vaccine dosage or booster timing, and adjust the population-at-large protocols based on that. Compare this with what we did, which is literally last century methodology. We pulled out of the ass a protocol: vaccine dose of X, booster timing of 3 weeks. Then we tested it in a large scale long term trial. We confirmed it worked. And used it as-is, and declared that any change will require another large-scale long-term trial. Look at the definition of the scientific method in the beginning of my comment. It's just not common sense in a pandemic, except for bureaucrats minimizing their personal risk. Which is why my rather justified anger with the system.