Bayes’ Rule and COVID Vaccines
The article begins by giving the statistic that 58% of Israel’s COVID-19 hospitalizations are vaccinated individuals, and says that this suggest that the vaccination may be ineffective. Following this, the author Gary Smith writes that making this assumption off of that statistic is incorrect. He then gives an example of how statistics can be misleading to prepare the reader to understand why Bayes’ rule is important. Following this, he gives the background on the rule, which was formulated by Thomas Bayes’ in in the 18th century. He then goes on to use the rule to find the probability getting hospitalized while vaccinated and unvaccinated and came to the conclusion that you are 3.07 times more likely to be hospitalized if you are unvaccinated.
Recently in class, we have learned about Bayes’ rule, the main mathematical component of Gary Smith’s analysis.
Currently, the COVID-19 vaccine is a topic of debate in United States politics, with some people claiming that the vaccine does work, and others claiming it doesn’t. The same process of using Bayes’ rule can be used to calculate the risk of being hospitalized in the United States, giving us a relevant real world application of the topic. Having learned this, we now also fully understand his reasoning, and the math behind his claims.
Source: https://mindmatters.ai/2021/09/covid-19-bayes-rule-and-simpsons-paradox/