Saw this highlighted in the Economist but that is paywalled . . WebMD has a good write up. It basically lines up with the Santa Clara research but uses CDC stats on "non-influenza flue like illnesses"
Punch line is that Covid is more widespread and less severe than the current confirmed case count and implied CFR currenly suggests.
https://www.webmd.com/lung/news/20200418/new-model-shows-covid-more-widespread-less-severe
https://www.medrxiv.org/content/10.1101/2020.04.01.20050542v2
Punch line is that Covid is more widespread and less severe than the current confirmed case count and implied CFR currenly suggests.
https://www.webmd.com/lung/news/20200418/new-model-shows-covid-more-widespread-less-severe
https://www.medrxiv.org/content/10.1101/2020.04.01.20050542v2
Quote:
The report, which uses CDC data of cases of influenza-like-illness, or ILI, estimates that at least 8.7 million people were infected across the U.S. during the 3-week period they studied in March. (Earlier, the researchers had estimated it could be as many as 28 million, but revised it when they re-examined the data after publication.) The research has not yet been peer reviewed.
Quote:
By quantifying the number of excess ILI patients in March relative to previous years and comparing excess ILI to confirmed COVID case counts, we estimate the syndromic case detection rate of SARS-CoV-2 in the US to be approximately 1 our of 100. This corresponds to at least 28 million presumed symptomatic SARS-CoV-2 patients across the US during the three week period from March 8 to March 28. Combining excess ILI counts with the date of onset of community transmission in the US, we also show that the early epidemic in the US was unlikely to be doubling slower than every 3.5 days. Together these results suggest a conceptual model for the COVID epidemic in the US in which rapid spread across the US are combined with a large population of infected patients with presumably mild-to-moderate clinical symptoms. We emphasize the importance of testing these findings with seroprevalence data, and discuss the broader potential to use syndromic time series for early detection and understanding of emerging infectious diseases.