Then, to calculate mortality, most people take the number of deaths on day x and divide by the denominator implied by the results
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So, if we think 4% of 100,000 people have had COVID-19, and 10 of them have died on day x, we'd say that the infection-fatality rate is 10/4000 = 0.025%
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BUT there's an issue here People don't die from COVID-19 immediately. It usually takes somewhere between 15-20 days from when they get infected The data is right-censored!
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There are probably a bunch of people who HAVE the disease on day x who are counted in our sample and will die but haven't yet!
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So, what we SHOULD do in cases like these is either: a) use a statistical model to account for this issue b) wait a few weeks and use different death estimates to correct for potential right-censoring
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Instead, most people just take the proportion immune on day x and divide by deaths on the same day This will almost certainly underestimate the 'true' infection-fatality rate, and is a big worry
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Replying to @GidMK
This example of right-censoring is accurate if you are looking at PCR but not as accurate for antibody tests, since it takes 2-3 weeks to reliably produce antibodies the fatality/seropositive case ratio comes closer to the true IFR than fatality/PCR positive case ratio does - no?
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Replying to @BonellC
From my understanding, it depends on the antibody tested, but generally speaking people will develop antibodies progressively over time from a few days after infection, so you'll have some people relatively recently infected testing positive
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Replying to @GidMK
It seems to me all the talk about “much lower IFR than we thought” is coming from business interests trying to rationalize a reduced response to the pandemic.
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What do you think of the actuarial type data of “excess deaths” as a crude surrogate for calculating IFR? Keeping in mind, that we must consider potentially increased (delayed?) MI/stroke/CA deaths as well as decreased MVA/suicide/air pollution deaths from lockdown
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Difficult question. We've got a biased numerator (infections) and a biased denominator (deaths). There are many ways to correct for these biases, but they all come with pros and cons
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