Scientists have long been aware that the official coronavirus numbers don’t capture the full story. One study that looked at life insurance applicants suggested the number of cases might have been twice as high as the official tally; another model suggested that deaths worldwide were roughly double the reported count. That’s a common theme for pandemic modelers: Among three of the most well-known coronavirus data sources, each comes with a distinct disadvantage. Going by the number of confirmed cases underestimates the actual number of infections, because not everyone who was infected was tested or diagnosed. Irons and his colleagues looked at data from two random testing surveys conducted in Indiana and Ohio and used those numbers to calibrate a model they built using less-perfect data sources: deaths, number of confirmed cases, and number of tests performed.

Source: Los Angeles Times July 26, 2021 18:56 UTC