“The first principle is that you must not fool yourself - and you are the easiest to fool.” ~Richard Feynman I’ll say up front here and with complete sincerity that I do not know how much, if at all, the considerations at the link below explain present-day data. But it is
Related to the professor's model is the issue about the control groups for the vaccine trials last year being wiped out because they weren't kept from getting vaccinated after the trials ended. In the U.S., we don't have a good statistical basis for comparing these 3 groups: vaccinated, unvaccinated and uninfected, and unvaccinated and infected.
I know terrible data quality is one of the common complaints that people make about the CDC and the state health agencies.
Justin Hart finds that this kind of reporting lag error for deaths is probably on the scale of multiple weeks, if not months. There are still deaths being reported from 2020!
This simulated data set reminds me of the small denominator instability problems associated with integrating dynamical equations in non-Cartesian spaces in the vicinity of a geometric singularity. I remember trying to implement time-dependent dynamics simulations for the 3D rotation of asymmetric solid rotors and seeing the really ugly divergences around the poles. There are ways to smooth the divergences (e.g. using quaternions) but if you aren't aware of the problem, you'll just integrate right through the unstable region and permanently ruin your time series.
This feels like the same sort of thing. There are effective divergences due to unstable denominators (here the small population of vaccinated at the start of the series, and unvaccinated at the end of the series). This makes this portion of the data the worst for evaluating the efficiency of the vaccine. The best data comes if you stabilize somewhere in the middle (around 50% vaccination), where both denominators are sizable relative to their rate of change. *Even if* the lag were perfectly corrected, the intrinsic noisiness of the data will be magnified by the small denominator problem. Relevant quote: "There could, of course, be reasons other than just delays in death reporting or misclassification. For example, any systematic underestimation of the actual proportion who remain unvaccinated would lead to a higher mortality rate for unvaccinated higher than that for the vaccinated, even if the mortality rates were equal in each category." This problem gets *worse* as you approach full vaccination due to the unvaccinated denominator going imperfectly toward zero!
Interesting! I would go read the article, but I’m allergic to math. Your explanation is enough to give me (even more) pause when being given data especially by those with an agenda.
Related to the professor's model is the issue about the control groups for the vaccine trials last year being wiped out because they weren't kept from getting vaccinated after the trials ended. In the U.S., we don't have a good statistical basis for comparing these 3 groups: vaccinated, unvaccinated and uninfected, and unvaccinated and infected.
I know terrible data quality is one of the common complaints that people make about the CDC and the state health agencies.
Justin Hart finds that this kind of reporting lag error for deaths is probably on the scale of multiple weeks, if not months. There are still deaths being reported from 2020!
https://covidreason.substack.com/p/cdc-data-almost-half-of-newly-reported?r=7ikwa&utm_campaign=post&utm_medium=web&utm_source=
This simulated data set reminds me of the small denominator instability problems associated with integrating dynamical equations in non-Cartesian spaces in the vicinity of a geometric singularity. I remember trying to implement time-dependent dynamics simulations for the 3D rotation of asymmetric solid rotors and seeing the really ugly divergences around the poles. There are ways to smooth the divergences (e.g. using quaternions) but if you aren't aware of the problem, you'll just integrate right through the unstable region and permanently ruin your time series.
This feels like the same sort of thing. There are effective divergences due to unstable denominators (here the small population of vaccinated at the start of the series, and unvaccinated at the end of the series). This makes this portion of the data the worst for evaluating the efficiency of the vaccine. The best data comes if you stabilize somewhere in the middle (around 50% vaccination), where both denominators are sizable relative to their rate of change. *Even if* the lag were perfectly corrected, the intrinsic noisiness of the data will be magnified by the small denominator problem. Relevant quote: "There could, of course, be reasons other than just delays in death reporting or misclassification. For example, any systematic underestimation of the actual proportion who remain unvaccinated would lead to a higher mortality rate for unvaccinated higher than that for the vaccinated, even if the mortality rates were equal in each category." This problem gets *worse* as you approach full vaccination due to the unvaccinated denominator going imperfectly toward zero!
Interesting! I would go read the article, but I’m allergic to math. Your explanation is enough to give me (even more) pause when being given data especially by those with an agenda.