The crappy hospitalization data is why I prefer to review status by state based on excess deaths. Hospitalization Data Reported by the HHS vs. the States: Jumps, Drops, and Other Unexplained Phenomena. Analysis about how crappy the hospitalization data is and suggestions about why. On the other hand, vital statistics reporting is long established, very complete, and very accurate. If you look at all-cause mortality, you take out of the equation questions of coincidence of morbidity and viral positivity.
A couple months ago, I did a back-of-the-envelope calculation of the human cost of herd immunity to SARS-CoV-2. I estimated the U.S. would not see herd immunity until it reached another 195 million cases and, grimly, the deaths that would accompany them. I believed herd immunity was impossible. Shortly thereafter, I saw an article from Johns Hopkins University that did the calculation the same way I did and came up with a similar estimate of 200 million cases.
In the early days of the pandemic, and even now in mid-April 2020, we heard many estimates of the risk of the coronavirus SARS-CoV-2. Some think maybe it’s like the flu. Others assert, this coronavirus is no flu. When we say coronavirus is or is not like influenza, what comparison are we making? One comparison could involve the incidence of death from each disease. The question could be, how do the numbers of people who perish this week from each disease, COVID-19 vs.
Dr. John Ioannidis Dr. John Ioannidis is well known as the author of the manifesto of reproducibility in research, Why Most Published Research Findings Are False. He published that in 2005. I learned of it much later when I attended an inspiring seminar by Edward Tufte. Fast forward to now, and I’m on fire to apply data science to public health. When I turn my sights on Ioannidis again, I realize he is a professor of epidemiology and statistics.