There are several issues with the post, which we can categorize into two parts: (1) the equivalency drawn between the influenza and SARS-CoV-2 viruses and (2) the presentation of the data by CNN.

Since the beginning of the COVID-19 pandemic, comparisons have been drawn between SARS-CoV-2 and the influenza viruses. This false equivalency, while understandable, is the first big issue with this post. Both the SARS-CoV-2 and influenza viruses are spread via respiratory droplets and induce respiratory disease. Also, I think we, as humans, have a tendency to equate the unknown with that which we know. It makes us feel more comfortable. We feel more secure, but this way of thinking is problematic. When we equate the unknown with the known, we close our minds off to the new information that we gather, often to our detriment.

SARS-CoV-2 and the influenza viruses are different for a number of reasons. For one, SARS-CoV-2 is far more infectious, which leads to a greater number of cases. SARS-Cov-2 also leads to more instances of severe illness and death. This is in large part because we have various pharmaceutical and non-pharmaceutical interventions that have been shown to be effective against the influenza virus year after year. We have an extremely effective and safe vaccine that has been shown to reduce the number of influenza-related infections, hospitalizations, and deaths. Health departments are well equipped to handle isolated outbreaks of influenza. We simply don’t have the same tools to battle the SARS-CoV-2 virus.

The second big issue is the way in which CNN has presented this information. I haven’t seen the actual clip of the commentators presenting this graphic, so I can only go on what I’m seeing. CNN has pulled these figures from a CDC report on suggested modeling parameters. Mathematical models are essential tools for the evaluation of public health interventions and to aid in public health preparedness. Many of you have had some exposure to these mathematical models, either through social media or the popular press. The results of these models are often presented in the form of an epidemic curve that forecasts the course of an epidemic through a population.

The models are constructed using a series of assumptions based on real world data. One of these parameters is a case fatality ratio (CFR), or “death rate” according to CNN. It is worth noting that this number is meant for planning purposes, not necessarily as an actual estimate of the case fatality rate. So, what does that mean? Answer: when we are building these mathematical models we anticipate that there will be some degree of error in the assumptions we are making. It is acceptable and understood that we will often be using our “best-guess” estimations. These best-guesses do not stand up to the same rigorous review as figures published in peer-reviewed journals. As a result, there is a good degree of uncertainty in the estimation. For example, this estimation of a CFR has a confidence interval of 0.2% and 1%, meaning that the true modeled CFR can land anywhere between 0.2% and 1%.

Let’s do a bit of a deeper dive on this 0.4% CFR estimation. What methodology did the CDC use to arrive at this figure? Was it data/evidence based? If so, when was the data collected? What does this 0.4% actually measure? Are there excluded populations? All of these questions are absolutely essential in the interpretation of this figure.

I don’t see an extensive methodology on how the CDC arrived at this figure. That should be a red-flag for us. This is not to say that the CDC doesn’t have a solid methodology, only that we don’t have the ability to evaluate it. It does seem, however, that the CDC used data from March 1st-March 31st in the estimation of the modeling parameters. This is hugely problematic. For one, this was before we reached peak severity of the first wave of the COVID-19 epidemic. Hospital capacity was less strained. This is no longer the situation. As hospital capacity decreases, meaning fewer personnel and resources per patient, CFR increases. Moreover, there is a substantial amount of evidence that deaths are under-reported and testing is over-reported. As a result, this CFR is likely to under-represent the current CFR.

So, what does this 0.4% CFR actually measure? According to the CDC, it is the symptomatic case fatality ratio, meaning it is the CFR among those who have shown symptoms. Those who are asymptomatic do not effect this rate. Using this figure to represent a “death rate” is incorrect because it does not account for asymptomatic COVID-19 infections. As a result, this figure actually overestimates the overall CFR for COVID-19 infections.

Finally, this figure is based on the overall populations and does not capture the true intricacies of the COVID-19 epidemic in the US. Often times, we epidemiologists will look at sub-populations to get more detailed picture of the data. In this case, this 0.4% “death rate” does not represent the CFRs of racial and ethnic minorities, the elderly, or those who live in states that are disproportionately affected by the epidemic. These are the populations most at risk and their respective CFRs are not only significantly higher than the mean, but are more helpful in public health planning and preparedness.

CNN presents a second parameter estimating the number of asymptomatic infections. This is perhaps even more misleading than the CFR. There is no methodology presented that explains this figure. Furthermore, until we have widespread antibody testing, it will be near impossible to get an accurate estimation of asymptomatic cases. Many of the estimates of asymptomatic infections are based on smaller scale studies in isolated or limited populations. All of this is to say, the CDC is likely basing their parameters on some figures within the literature, which is appropriate. However, taking this figure and broadcasting it without the associated limitations is not.

So what is the actual case fatality ratio and number of asymptomatic infections? The CFR is probably somewhere in the middle of the initial estimate and modeling parameter offered by the CDC. The number of asymptomatic infections can’t be accurately defined until antibody testing is conducted on a larger, representative scale. As more information becomes available, we will refine these predictions again. Science is an iterative process. We are constantly learning and constantly changing our hypotheses. This is the way it should work.

I’ve thrown a lot of information at you. I know, it’s pretty crazy that we have to put so much thought into the interpretation of two two-digit numbers. It’s even more disheartening that a trusted news source has produced a graphic that is so misleading. I can only hope that the CNN commentators explained these estimates in more details. Here’s the main take away—results are subject to interpretation. A quick Twitter blurb or CNN graphic is going to be misleading, especially if those who are posting have an agenda and are trying to bend the results to their own purposes. You have to take in all possible information and arrive at your own conclusions.

Think Critically,

Patrick Maloney, Ph.D.