Monday, April 20, 2020

More on CA infection rates

This is Joseph

A new study is out which also suggests that the infection rate is higher than expected:
Based on results of the first round of testing, the research team estimates that approximately 4.1% of the county's adult population has antibody to the virus. Adjusting this estimate for statistical margin of error implies about 2.8% to 5.6% of the county's adult population has antibody to the virus- which translates to approximately 221,000 to 442,000 adults in the county who have had the infection. That estimate is 28 to 55 times higher than the 7,994 confirmed cases of COVID-19 reported to the county by the time of the study in early April. The number of COVID-related deaths in the county has now surpassed 600.
Methods are not yet out, but there are several points of concern. One, such small percentages are vulnerable to a lot of errors in specificity (see Andrew Gelman and Thomas Lumley).  They don't say in the press release (I cannot even find the study yet) but another press release says:
In L.A. County, researchers on April 10-14 sampled the blood of a random population of 863 area residents at six testing sites.
So we are talking maybe 30 or positives.

Two, the New York numbers make no sense if these estimates are correct, unless the disease is either wildly misclassified (in NY or LA there must be important mis-attribution of case of death) or the infection rates in NY must be enormous. Here is Megan McArdle discussing it

The obvious way to solve this is to do a 1000 person sample in NYC. If it is even close to the huge infection rates needed for these numbers then it should be quickly obvious in any study of seroprevalence.

The final point, is if the IFR is massively higher in NYC that, alone, is worth studying. They have 9,101 confirmed deaths and 4,582 probable deaths as of April 20th. There are 8.3 million people in NYC. That suggests an IFR of 0.16% assuming a 100% infection rate. The Stanford study suggested an IFR of 0.12% to 0.2% based on their models.

Figuring this out is important and not helped by being led by press releases before full scientific evaluation.

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