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We are told we live in an era of science. Neil deGrasse Tyson wrote, “the good thing about science is that it’s true, whether or not you believe in it.” But is that really so? Does not science change as new evidence is gathered? Statisticians use models to justify their findings. Yet models are only as good as their inputs. The epidemiologists’ models we have seen regarding COVID-19 have changed markedly over the past few weeks. In mid-March, Imperial College in London predicted 2.2 million deaths in the U.S., with no mitigation. By the first of April, modelers at Oxford saw that number drop to a range of 100,000 to 240,000, with some mitigation. Now the estimate is 60,000. A University of Virginia model shows COVID-19 will peak this summer, while Health Metrics Evaluation at the University of Washington suggests the virus will “peter out” in May. Models make assumptions about, among other factors, human behavior, the measurement of which is an art? What is a person to believe?
If we are to base our beliefs about COVID-19 on the basis of “evidence,” it is unsurprising that confusion abounds. We presume, with strong reason, that it came from the city of Wuhan in Hubei Province, China, but whether from a live bat sold for human consumption at a wet market or the Wuhan Center for Disease Control has never been made clear. We are told coronavirus is highly contagious. Ten days ago, the Los Alamos National Laboratory published an article in which they claimed that the transmission rate for COVID-19 is between 4.7 and 6.6. For comparison purposes, the seasonal flu, the transmission rate is 1.3. (The transmission rate is also referred to as the regeneration rate, or the R0.) At the midpoint, 5.7, over ten rounds, one person could infect 36 million people. The chief epidemiologist at the Chinese Centers for Disease Control in Beijing puts the transmission rate at between 1.0 and 5.0. At the midpoint of 2.5, and after ten rounds, one individual could infect 9,538 people. Which are we to believe?