Years ago, a (statistical) colleague advised me that, if it is necessary to conduct a test to tell whether or not a relationship is significant, then it isn't significant. These scatter diagrams are a good example of that: when R-squared is only around 1-2%, it is clear that the vaccinations (including boosters) are ineffective.
It is al Danish check for graph in the middle "intesivlaegelser". Green line are boostered people.
At firs glance this seems to contradict your findings here.
But it appears that the boostered people do get an infection easier. Also visible in the Danish data. Common explation ist that thes test more often than unvaccinated or double vaxed, also in the Danish data to see.
Could it be that boostered do get an infection easier than double vaxed an unvaxed but do have better outcome? If the increase in infection rate is higher than the protetcetion for hospitalisation both findings would match.
I guess this is right, please check at ourworldindata the current surge of Covid Cases in Portugal. They are heavily vaxxed and boostered.
If correct that would be the thing that Geert VanBosche predicted earlier. The vaxed become a risk for the unvexed.
It’s is often interesting with scattered data like this to look at the upper bounds .. rather than the line of best fit.. for the boosted the upper bounds of hospitalisation rate ie potential risk.. is doubled when you go over 40%...
Hi Joel, isnt there a time from injection factor as well to how effective the vaxx is? From what I have read it seems the efficacy wears off over time, and seems to wear of quicker with each additional dose. No idea how you would take this into account in what you have done
I don't get why we are receiving opposing reports when analysing the data for vaccination status and hospitalisations/deaths. I get that this is covid hosp specifically, but I don't know why we get two opposing results from the officials and a lot of top data analysts. Confusing!
The UK ONS is saying it's mainly the opposite for ASMRs: "Monthly age-standardised mortality rates (ASMRs) for deaths involving coronavirus (COVID-19) have been consistently lower for all months since September 2021 for people who had received a third dose or booster at least 21 days ago, compared with unvaccinated people and those with just a first or second dose."
how do you calculate the t-stats? wouldn't it just be the coefficient/standard error of coefficient? r-squared looks very low. not coming here to troll you. just curious. I'm studying quantitative analysis right now for my cfa. I don't support the jabs.
Yes, visually comparing the maps confirms some correlation between the disease and the jabbing levels, but there are significant inconsistency regions, e.g. in Florida and Texas. It would be interesting to try and figure out why...
Would you consider adding obesity as an explanatory variable? Phil's sheets have all the data... it seems that has explanatory power... would the relationship get stronger or weaker once you add this variable which is clearly stat sig.
Am I reading these maps correctly? I see a lot of areas in NY, NJ and FL that have very high uptake of dose two, but zero of the booster?? How does that make sense? Colleges alone (in NY and NJ) would be impacting those areas wouldn’t they? Are most students getting exemptions? I’m blown away to see this.
Do the COVID-19 injectables (aka "vaccines") reduce COVID-19 hospitalisations? ***Corrected
Thanks for the updates.
Years ago, a (statistical) colleague advised me that, if it is necessary to conduct a test to tell whether or not a relationship is significant, then it isn't significant. These scatter diagrams are a good example of that: when R-squared is only around 1-2%, it is clear that the vaccinations (including boosters) are ineffective.
Please do an analysis of rural v. urban counties.
The big gray area is Jesus saying no to vaccines.
Not sure if we're using the exact same data sets, but you can see the same thing on the "Key Metrics Vax by County" tab on my dashboards: https://public.tableau.com/views/CovidDashboardsbyTCoddington/KeyMetricsVaxbyCounty?:language=en-US&publish=yes&:display_count=n&:origin=viz_share_link
I have a question. There is good data available from Denmark that still shows that boostered people do have a lower chance to get into hospital.
https://covid19danmark.dk/
It is al Danish check for graph in the middle "intesivlaegelser". Green line are boostered people.
At firs glance this seems to contradict your findings here.
But it appears that the boostered people do get an infection easier. Also visible in the Danish data. Common explation ist that thes test more often than unvaccinated or double vaxed, also in the Danish data to see.
Could it be that boostered do get an infection easier than double vaxed an unvaxed but do have better outcome? If the increase in infection rate is higher than the protetcetion for hospitalisation both findings would match.
I guess this is right, please check at ourworldindata the current surge of Covid Cases in Portugal. They are heavily vaxxed and boostered.
If correct that would be the thing that Geert VanBosche predicted earlier. The vaxed become a risk for the unvexed.
3 snots for kids. Wow will denial continue as we lose our kids to odd health issues?
It’s is often interesting with scattered data like this to look at the upper bounds .. rather than the line of best fit.. for the boosted the upper bounds of hospitalisation rate ie potential risk.. is doubled when you go over 40%...
that’s significant
Hi Joel, isnt there a time from injection factor as well to how effective the vaxx is? From what I have read it seems the efficacy wears off over time, and seems to wear of quicker with each additional dose. No idea how you would take this into account in what you have done
Joel, is it possible from the data to tell if hospitalizations lead or lag the vaccinations?
I don't get why we are receiving opposing reports when analysing the data for vaccination status and hospitalisations/deaths. I get that this is covid hosp specifically, but I don't know why we get two opposing results from the officials and a lot of top data analysts. Confusing!
The UK ONS is saying it's mainly the opposite for ASMRs: "Monthly age-standardised mortality rates (ASMRs) for deaths involving coronavirus (COVID-19) have been consistently lower for all months since September 2021 for people who had received a third dose or booster at least 21 days ago, compared with unvaccinated people and those with just a first or second dose."
https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/deathsinvolvingcovid19byvaccinationstatusengland/deathsoccurringbetween1january2021and31march2022
Where is this data from? I neither believe nor disbelieve it until I can confirm it.
how do you calculate the t-stats? wouldn't it just be the coefficient/standard error of coefficient? r-squared looks very low. not coming here to troll you. just curious. I'm studying quantitative analysis right now for my cfa. I don't support the jabs.
Yes, visually comparing the maps confirms some correlation between the disease and the jabbing levels, but there are significant inconsistency regions, e.g. in Florida and Texas. It would be interesting to try and figure out why...
Thanks!
The first question "they" will ask is: why 24 Feb 2022?
Would you consider adding obesity as an explanatory variable? Phil's sheets have all the data... it seems that has explanatory power... would the relationship get stronger or weaker once you add this variable which is clearly stat sig.
https://docs.google.com/spreadsheets/d/13M1Lzxg_oddsxPMm3AgjxNjtplfFJj6xLpJ3scqVbC8/edit#gid=1843000234
Thank you
Am I reading these maps correctly? I see a lot of areas in NY, NJ and FL that have very high uptake of dose two, but zero of the booster?? How does that make sense? Colleges alone (in NY and NJ) would be impacting those areas wouldn’t they? Are most students getting exemptions? I’m blown away to see this.