The author makes a critical mistake when analyzing the BNT/Pfizer clinical trial. Over and over when discussing the results they mention calculating efficacy against "infections" using this data. But this specific clinical trial provided no data about infections. The metric measured was symptomatic COVID-19 disease.
This kind of mistake is understandable, but doesn't really inspire much confidence in a screed about the mistakes others are making in analyzing COVID-19 data.
I recently completed my PhD in vertebrate comparative genomics so this is fun to see.
The single most important factor that needs to be accounted for in analyses like these is the correlation between phylogenetic similarity and the trait in question. In short, closely related species will tend to have similar lifespans, and closely related species will tend to have similar CpG density in any fixed genomic region. So the fact that you can predict lifespan from CpG density with enough parameters is unsurprising. You could almost certainly predict lifespan fairly well from any feature measuring phylogenetic similarity -- I would have liked to see some evidence showing that CpG density in these promoters is somehow uniquely suited for the task.