Thanks for putting yourself out there. I did find the NYT article you linked to about gaps in CDC data interesting. It really speaks to the issues of trust we have with public institutions and also the role raw data plays in public debate.
Zooming in on your paragraph that starts with: “ The, er, limitations of the coronavirus vaccines…” I think what’s missing is the likelihood of an infected vaxxed person being hospitalized or dying COMPARED to the likelihood of an unvaxxed person being hospitalized or dying. I pasted in the below model as it explains why this is valuable in a simple testable (try it out in Excel!) way.
To explain, let’s imagine that we have a hypothetical population of 10,000 people where 90 per cent are vaccinated. Now let’s assume that if you are vaccinated you are seven times less likely to end up in hospital than if you are unvaccinated. So for our mathematical experiment, let’s set the hospitalization rate of the unvaccinated population at seven per cent and one per cent for the vaccinated. In reality any numbers you pick will lead to the same conclusion as long as you maintain the 7:1 ratio, which is actually very close to the actual number.
So now, if we imagine that COVID-19 infects everyone in our sample population, then one per cent of our 9,000 vaccinated people will land in hospital. In other words, 90 vaccinated people end up in hospital after being infected with COVID-19 in our theoretical population. Now, among the 1,000 unvaccinated people, if seven per cent end up in hospital then that translates into 70 unvaccinated people being hospitalized.
I AGREE
Thanks for putting yourself out there. I did find the NYT article you linked to about gaps in CDC data interesting. It really speaks to the issues of trust we have with public institutions and also the role raw data plays in public debate.
Zooming in on your paragraph that starts with: “ The, er, limitations of the coronavirus vaccines…” I think what’s missing is the likelihood of an infected vaxxed person being hospitalized or dying COMPARED to the likelihood of an unvaxxed person being hospitalized or dying. I pasted in the below model as it explains why this is valuable in a simple testable (try it out in Excel!) way.
To explain, let’s imagine that we have a hypothetical population of 10,000 people where 90 per cent are vaccinated. Now let’s assume that if you are vaccinated you are seven times less likely to end up in hospital than if you are unvaccinated. So for our mathematical experiment, let’s set the hospitalization rate of the unvaccinated population at seven per cent and one per cent for the vaccinated. In reality any numbers you pick will lead to the same conclusion as long as you maintain the 7:1 ratio, which is actually very close to the actual number.
So now, if we imagine that COVID-19 infects everyone in our sample population, then one per cent of our 9,000 vaccinated people will land in hospital. In other words, 90 vaccinated people end up in hospital after being infected with COVID-19 in our theoretical population. Now, among the 1,000 unvaccinated people, if seven per cent end up in hospital then that translates into 70 unvaccinated people being hospitalized.
By just looking at hospitalizations, it would seem that the 90 vaccinated patients outnumber the 70 unvaccinated. They do. But the risk of being hospitalized is unchanged. You are still seven times more likely to be hospitalized if you are unvaccinated. https://www.mcgill.ca/oss/article/covid-19/hospitalization-rates-confirm-covid-vaccines-benefits
unapologetically brilliant...