COVID-19 is Far Deadlier than the Flu

Skeptics have undermined the public-health response with dubious research

Lockdowns speak louder than words

The Chinese witnessed the virus’ deadly effects firsthand after COVID-19 spread largely unimpeded for at least a month in Hubei province. China responded aggressively by imposing a strict, military-backed quarantine of the region in late January.

Skeptics: cases are missing, don’t believe what you see

Despite such drastic actions, skeptics remained unconvinced about COVID-19’s deadly nature. Some pointed out the severity bias, the fact that new stories and data are more likely to showcase many of the worst case scenarios. Other discussions revolved around how case fatality rates (confirmed deaths / confirmed cases) are biased because we can’t be sure about the number of cases in the denominator if testing is hard to come by and many cases are asymptomatic.

Skeptics collect their own biased data

In early April, Ioannidis and a team of researchers (including one of the founders of the health care analytics firm I used to work for) took it upon themselves to see just how prevalent COVID-19 is within Santa Clara County, CA, one of areas with a known early outbreak. Their research found that the prevalence of COVID-19 was low in the county and that there are likely many uncounted cases, leading them to wrongly conclude that the infection fatality rate (true deaths / true infections, not just those confirmed) is just higher than the flu.

Uncounted deaths

What does this all mean? The denominator (number of cases) in the infection fatality rate calculation from the Santa Clara study is unreliable. But what the authors also haven’t touched on is that the numerator (the number of deaths) is also missing data. They account for deaths that are likely to occur though haven’t happened yet (time lag), but they don’t account for the odds that deaths are counted at all in real time.

Infection fatality rates don’t capture the full threat

Infection fatality rates say how likely one is to perish given they were infected. But we know that COVID-19 is far more transmissible than the seasonal flu, which means many more people are at risk of being infected by the coronavirus. CDC researchers estimate that on average, 8.9% of the population gets the flu every year, and that number jumps to 10.4% without a vaccine (author’s calculations using CDC data).

Proclaiming dubious results to the world

In 2018, MIT researchers conducted a study that showed false news spreads many times faster than the truth, likely because of the novel (made-up) nature of such information. The COVID-19 epidemic has been fraught with the same dynamics. In the wake of the Santa Clara study, doctors with no epidemiological background chose to run with these results (nearly 1 million shares), claiming in spite of all we have seen that COVID-19 is just the flu (5 million views). They were wrong but their views were spread widely as more evidence that shutting down public life was a foolish decision.

Real-time science is difficult

I’ve spent most of my career as a researcher and data scientist. Even when moving at the glacial pace of academic research, it’s incredibly difficult to do everything just right to design a study and gather/analyze data appropriately. This task becomes exponentially harder in-real time when there are life-and-death policy decisions to be made and there are powerful political forces at play. This inherent difficultly means that every piece of news needs to be viewed with a healthy dose of skepticism, with a backdrop of what makes sense. A single study or report that flies in the face of common sense and established, scientific consensus should not be enough to overturn policies aimed at saving potentially hundreds of thousands of lives.

Consultant, developer, statistical analyst. Published reports with CMS and the FDA along with a manuscript in the journal Vaccine.

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