Regarding Covid-19, Do You Want More Detailed Information Or Media Pablum?

[JonDouglas]

On the subject of covid knowledge, you might think of it like a continual serving of a meal of information. The question may be, "Do you want a well-balaned meal or the fast-food pablum the media feeds you?" I was reminded of this question upon encountering yet another interesting (to me, if not you) article on covid-19, the virus which is affecting so much of our lives at this time. Like virtually all data on this "pandemic" this information (i.e. a more scientific paper) has elements of uncertainty and probabilistic inference, which is not "fast food" type of information, and the paper doesn't try to encapsulate the information like the media would. So, let's just try putting this bit of info out on the table and see who's interested.

Substantial underestimation of SARS-CoV-2 infection in the United States​

Abstract​

Accurate estimates of the burden of SARS-CoV-2 infection are critical to informing pandemic response. Confirmed COVID-19 case counts in the U.S. do not capture the total burden of the pandemic because testing has been primarily restricted to individuals with moderate to severe symptoms due to limited test availability. Here, we use a semi-Bayesian probabilistic bias analysis to account for incomplete testing and imperfect diagnostic accuracy. We estimate 6,454,951 cumulative infections compared to 721,245 confirmed cases (1.9% vs. 0.2% of the population) in the United States as of April 18, 2020. Accounting for uncertainty, the number of infections during this period was 3 to 20 times higher than the number of confirmed cases. 86% (simulation interval: 64–99%) of this difference is due to incomplete testing, while 14% (0.3–36%) is due to imperfect test accuracy. The approach can readily be applied in future studies in other locations or at finer spatial scale to correct for biased testing and imperfect diagnostic accuracy to provide a more realistic assessment of COVID-19 burden.​

Something to think about. Or not. The full paper, including all the particulars (authors, reference, peer review, etc.) are at the SOURCE.

 

[JonDouglas]

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COVID-19 Vaccines: What does 95% efficacy actually mean?

You have likely heard that Pfizer's COVID-19 vaccine efficacy is 95%, Moderna's is 94% and Johnson & Johnson's is 66%. But what do these numbers actually mean? ​

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It's not just an academic question. How people understand these numbers affects how they think about the vaccine, whether they get it and how they behave after getting it, all of which have implications for the pandemic on a larger scale.​

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So how should people interpret these numbers?​

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Barker cautions that it’s tricky to directly compare efficacy between the Johnson & Johnson, Pfizer, and Moderna vaccines, because the clinical trials happened in different geographic areas with different populations, and at slightly different time points in the pandemic when different variants of COVID-19 were circulating. “There were more people who had the B117 [U.K. variant] or other types of variants during the time of the Johnson & Johnson trial than during the Moderna trial,” she said. ​

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All three vaccines were 100% effective at preventing severe disease six weeks after the first dose (for Moderna) or seven weeks after the first dose (for Pfizer and Johnson & Johnson, the latter of which requires only one dose). Zero vaccinated people in any of the trials were hospitalized or died of COVID-19 after the vaccines had fully taken effect.​

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Full article at LiveScience.