I would be curious to know if they were sufficiently able to remove confounding variables to make this claim. For comparison to another statistical quirk: someo

I would be curious to know if they were sufficiently able to remove confounding variables to make this claim. For comparison to another statistical quirk: someone I know made this chart which fails to show any correlation between vaccination rates and death rates in the last 14 days on a state-by-state basis in the US: https://docs.google.com/spreadsheets/d/1x9sKFsr-3VpdjvjZqHql6EOg8LqsyLQ0/edit?usp=sharing&ouid=111029439715098511131&rtpof=true&sd=true https://docs.google.com/spreadsheets/d/1x9sKFsr-3VpdjvjZqHql... Now, we have evidence from other sources that the vaccine is definitely highly effective in every particular state (this should not require a citation, but here you go anyways: https://covid.cdc.gov/covid-data-tracker/#rates-by-vaccine-status https://covid.cdc.gov/covid-data-tracker/#rates-by-vaccine-s...). But it seems there are too many confounding variables here (age, population density, etc.) such that attempting a simple region-by-region comparison over any particular time period might incorrectly conclude that the vaccine does not work. Was JHU able to address that data collection and comparison problem here?