Misleading Pandemic Numbers Lack Context

Back in September, I posted an article about how bias can creep into your analysis. It was the basis for an SQL Saturday presentation I did in Orlando, FL in October. In that presentation, I used example from politics, climate change, rainfall, test scores, and of course, COVID-19.

Apparently, there are others around the country and the world that recognize that some of the hysteria people have every time a new variant of COVID hits the world (I said some, not all) is due to the way the data is presented. In a recent article written by Jim Downs for the Los Angeles Times and republished in the Sunday December 26, 2021 Orlando Sentinel, he explores the possibility that reporting just the number of new cases, or the number of deaths does not provide enough of a context as to what is really going on.

As an example from the past, he refers to a cholera outbreak in the early 1800s in India in which hospital workers treated patients with cholera without contracting the disease. If only the number of cases had been reported, the ‘average’ person might have been worried about contracting cholera from other people they meet in public. However, we now know that cholera was not transmitted by direct contact. That is why the hospital workers were not affected.

In fact, a little later in that century, another cholera outbreak led a London physician to the ‘discovery’ that most of the cholera cases could be traced back to water coming from a specific water pump. This basically means that understanding how a disease spreads and who gets infected and who does not could be important information to understanding the spread of any disease.

Mr. Downs states that understanding the uninfected as well as the ones who were infected but recover is equally as important as the other numbers that we typically see in the media. He mentions some of the same things I talked about in my presentation such as the rate of infections, not just the total number of infections over time. You see, the rate of infections determines whether the disease is growing or dying out. But it is more than that. Location is also important. We have seen in the recent Omicron variant where the rate of infection is growing rapidly in the United States, specifically the southern states while it appears to be abating in South Africa were it first appeared. Does this mean that it has run its course in South Africa? Does this give us information about how long a peak may last? As Mr. Downs says, “Reporting the number of infected is a numerator. We are missing the denominator.”

In my presentation, I clearly showed that the media attempts to portray California, Texas, and Florida as some of the worst states with the highest number of cases. However, these are also the three largest states in the United States by population. So, the real question should be, what is the number of case per 1,000 or even 100,000 people in each of these states compared to the US average. Even that however can be deceptive because the cases are probably going to peak at different times with the early peaks being in the Northeast and the latest peaks in the Southeast. This means that comparing rates per 100,000 people in any given week has a location bias. To really understand the situation, one needs to look at the rate during each state’s peak or after the peaks have died down, which they will, what the rates per 100,000 people were in each of the states from the beginning to the end of the Omicron or any other variant.

The same can be said of deaths, although here I prefer to look at deaths per 1,000 people who were infected and if possible, divide these into vaccinated and unvaccinated groups. Even that however, is not good enough. If a person is vaccinated on Monday, it can take several days, perhaps a week or more, for the vaccination to be effective. So, is a person who tests positive for Omicron on Thursday even though they were vaccinated on Monday really a vaccinated case or an unvaccinated case? Pointing to people who have been vaccinated just recently but then when out in a crowd they contract COVID may give people the wrong idea that vaccinations are totally worthless. Thus, the reporting as currently performed provides a disservice to public health promoting vaccination.

Keep in mind that I’m not saying that COVID or even the Omicron variant should be ignored or that it is a scam. Like Cholera, it is a real disease. But we need to avoid the bias of quick numbers that do not consider things like time, location, or even whether the victim recently was in large crowds (more than about 6-10 closely packed people). Should you use this to avoid going to work? In most cases, not at all. Even if your work puts you in contact with a large number of people in any given day, taking precautions like masks will probably keep you safe (Note: I have not seen any statistics on whether COVID victims are wearing masks around large crowds or not.) On the other hand, wearing a mask while driving alone in your car may be just as ridiculous as not wearing a mask in a large crowd.

Perhaps with just a small degree of common sense and concern about the health and welfare of not just ourselves, but those around us, we can make it through this. Remember this, “All Lives Matter”.


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