’s algorithm has a wide uncertainty interval of between 1 million and 7.5 million deaths for India.For China, the model estimates almost 750,000 deaths , but with a wide uncertainty interval ranging from as low as 200,000 fewer deaths than expected, to as high as 1.9 million excess deaths.’s model highlights how countries’ official death counts often underestimate the true number — but that the extent of the underestimate varies.
. “You don’t learn anything by training a model on mostly rich countries with high life expectancy and applying it to poor countries with low life expectancy.” Some demographers see Shotwell’s point of view, saying that applying modelling to countries without their own deaths data is inherently difficult. “The process is intrinsically flawed. The data are a real mess and so any modelling effort is going to be very speculative,” says Jon Wakefield, a statistician at the University of Washington in Seattle, who leads a modelling project run by the WHO to estimate the pandemic’s excess death toll. “It’s very frustrating as the data are so limited.
We already US data is skewed up and Chinese data is skewed down. So is this even accurate?
In India alone during the Delta wave they were burning corpses that never made it into the hospitals to get properly recorded, so we know the global count is not accurate.
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