Community, Demography

Covid’s Mask and Pascal’s Wager

According to the Internet Encyclopedia of Philosophy, “Blaise Pascal (1623-1662) offers a pragmatic reason for believing in God: even under the assumption that God’s existence is unlikely, the potential benefits of believing are so vast as to make betting on theism rational.” As a stats guy, I could write this from memory, as a scientist, I need to cite a source.

Pascal’s statistical argument is a gambler’s view of the universe – the cost of believing, of the ante, is so small compared to the infinite reward (the size of the pot).  I worked with an accountant who had a system for buying lottery tickets – his break from understanding Pascal was that both cost and reward in the statistics of lottery cards are finite – the odds really can be calculated.  Lotteries are a tax on people who don’t want to do the math.

Covid is also a game for statisticians.  It’s still at a point where we have a bunch of unknowns, but there are fewer unknowns than there were 6 months ago.  Then the Diamond Princess was a horrifying news story – now it is data, as taken from “A total of 712 people were infected with COVID-19 on the Diamond Princess cruise ship – 567 passengers and 145 crew members. The cruise ship, which had more than 3,500 people on board, was quarantined for around two weeks. All passengers and crew members had finally disembarked the ship by March 1, 2020.”

Wikipedia shows 14 deaths among the 712 infected people on the Diamond Princess.  Somewhere right around 2%.  About the same as Texas and California, and lower than New York, New Jersey, and Massachusetts.

We’re still looking at less than perfect representative numbers – but Diamond Princess has provided some data:  roughly 20% of those exposed between January 20 and February 19 wound up infected.  In March, we had estimated R0 values from 1.5 to 3.5.  Now, we have Rt values (Average number of people who become infected by an infectious person with COVID-19 in the U.S. as of October 17, 2020).  Those numbers vary from 0.91 in Mississippi to 1.31 in New Mexico.  Montana scored 1.2. 

Generally speaking, in the absence of data, we have a tendency to assume the worst.  We have data now.  The actual infectivity is lower than the initial data – perhaps because the precautions have been effective, perhaps it is related to the fact that 80% of the people on Diamond Princess did not catch covid.  Correlation is not causation.  Causation is inferred from statistics, not proven.

This week, an article from the American Society of Hematology stated: “Blood type O may offer some protection against COVID-19 infection, according to a retrospective study. Researchers compared Danish health registry data from more than 473,000 individuals tested for COVID-19 to data from a control group of more than 2.2 million people from the general population. Among the COVID-19 positive, they found fewer people with blood type O and more people with A, B, and AB types.

Making statistics personal is a challenge – data suggests that my risk factors are increased by age (70), height (6’3”), asthma, and diabetes.  How much we don’t know – for neither my asthma nor the diabetes scores particularly high.  My risk factors are reduced by my blood type.  So let’s look at masking.

My mask is like Pascal’s wager – it seems logical that any level of masking will reduce transmission.  The question is: “How much?”  I don’t have that answer.  Does my mask protect me significantly?  When I have been in surgery, the surgeons and medical staff were masked to protect me.  Similarly, is my mask to protect others?   Business Insider offers an article comparing mask effectiveness, but cautions that “Mask studies should be taken with a grain of salt.”  My mask is like Pascal’s wager – and I hope wearing it adds a sense of security. It costs me little to wear it.