As an avid reader of the New York Times, it pains me greatly to read about a familiar subject that has so many errors and misconceptions. Especially when COVID-19's impact on society is being discussed.
To understand how severe and lethal COVID-19 really is, we need to know how many have been infected, which, in this equation, is the "denominator." An early study from Stanford of Santa Clara County says we may be underestimating how many cases there already are, which inaccurately gauges COVID-19’s infectivity and eventual mortality.
Why are basic questions about the biology of SARS-CoV-2 so hard to answer?
It's quite likely that the human toll from COVID-19 will not be as bad as the prediction models forecasted. That's because models contain simplifying assumptions that rarely hold true in the real world; our human response is probably the least predictable of all. And yet, while all models are useful, all models are also wrong.
"Test here. Test now. Test, baby, test!" has become the conventional wisdom for handling the COVID-19 pandemic. But false positives and false negatives create substantial problems for mass testing.
There is a persistent belief that COVID-19 is "like seasonal flu." While there are similarities, the clinical course is very different.
The recently-passed Coronavirus Aid, Relief, and Economic Security Act includes a surprise: a loophole for surprise billing in testing for COVID-19.
No, the novel coronavirus that causes COVID-19 is not a biological weapon. But that doesn't mean the virus didn't escape from a laboratory. A growing body of circumstantial evidence indicates that very well may be what happened.
The numbers associated with COVID-19, its infectivity, hospital admissions, deaths, are all being studied like tea leaves for any pattern or trend. And the numbers vary quite a bit. Those variations are often ascribed to the veracity of the source or some underlying agenda of hope or fearmongering, and occasionally to a mathematical error.
Different countries may appear to have different death rates, but only because they have applied different sampling and reporting policies to their accounting efforts. It's not necessarily because they are managing the virus any better, or that the virus has infected fewer, or more, people.
The biology of the virus will help us learn how to fight it.