Another covid Graph. This is the covid deaths per 1 million people v. The number of vaccine doses per 100 people. So a score of 400 basically means that if everyone got equal doses, each person would get 4 apiece. The countries are size coded to represent the Log2 of the population, because china and India are 100 times the size of the median population. They're color coded to their continent. Africa in red is hard to read.

drive.google.com/file/d/1ZL_T5

Source data at: covid.ourworldindata.org/data/

I made a phone app that pulls covid hospitalization, death, and positivity data straight from the CDC. It's nice to pull it up on the fly to show people. It looks like this and this.

Heavy is the weight of knowledge. Thank you @Ian and @RobotTaco for making my dream reality.

We should probably take our health advice from the dumpy little dude with the sandwich.

An unvaccinated likelihood of death can then be calculated for case mortality. By this method the vaccinated were only ever about half as likely to die, and that effect waned very quickly. This would indicated a vaccine efficacy of approximately 33% at best.

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But if instead the deaths in each population are divided by the infections in each respective population (from two weeks previous as discussed above) the case mortality of each population can be compared. This is a very different picture of the efficacy of the vaccine.

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The same method for unvaccinated likelihood of infection is applied to unvaccinated deaths with the following result. This is often considered the smoking gun for compulsory vaccination. "Look how many deaths could have been averted!" the proponents decry.

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The same populations separation is applied to weekly deaths. Again this is a much more optimistic view of vaccination efficacy, until just under a year into the campaign.

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The unvaccinated population infection rate can then be divided by that of the vaccinated to get a likelihood of infection measure. Such as, the unvaccinated are x times as likely to become infected as the vaccinated. Note the downward trend.

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The weekly cases can then be divided by the corresponding population number to determine the population infection rate. This shows a more favorable view of the vaccine.

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The next three plots concern a different metric of rating efficacy, so first it is important to show that as the vaccinated population increases the unvaccinated population decreases. There is also a time delay within the populations classifying deaths and cases because the CDC counts vaccinated deaths and vaccinated cases differently. On one hand this makes sense because deaths trail cases, but on the other hand could be a statistical trick to obscure vaccinated deaths. Note the anomaly.

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This is a similar plot showing the deaths in the aforementioned two populations. The data speaks for itself.

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This plot shows two populations: vaccinated and unvaccinated, or as should be noted normal people. It is very strange to classify people by a trait they do not possess. Note that there are in fact many infections in vaccinated people, what were originally called "breakthrough infections". Also note the limited date range of the data, this is as provided by the CDC.

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