COVID-19 Mortality Rate Better Than Thought, Spread Is Wider

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The first substantial SARS-CoV-2 antibody test results in the US have been released from a Santa Clara County, CA study by Stanford University researchers. The study shows a likelihood that there are “50-85-fold more” cases in the county than the CONFIRMED number of cases, and an “infection fatality rate of 0.12-0.2%.” This is potentially excellent news.

For the whole country, this could mean the spread of COVID-19 is farther along than anyone thought, and that would mean mortality rates are lower than projections. I wrote about that likelihood on March 31, and now the data is starting to come out. It’s a great sign. Let’s hope this is the beginning of a trend. Still need a lot more data and a lot more research, but good news is good news.

Let’s look at the stats to see why that is such potentially good news (fingers crossed). Using my old and new home states, stats are from the same time as the Stanford study, April 4, 2020.

CONFIRMED Cases and Deaths Only (Not estimated. Remember that important distinction.)
US: 267,436 cases, 5,457 deaths. CONFIRMED case mortality rate: 2%
TN: 3,321 cases, 43 deaths. CONFIRMED case mortality rate: 1.3%
OH: 3,739 cases, 102 deaths. CONFIRMED case mortality rate: 2.7%

If we ESTIMATE the mortality rate using the 50-85 range increase in cases from the Stanford study, here is what we get:

If factor of 50 more:
US: 13,371,800 cases, 5,457 deaths. Estimated case mortality rate: .0415%
TN: 166,050 cases, 43 deaths. Estimated case mortality rate: .026%
OH: 189,950 cases, 102 deaths. Estimated case mortality rate: .054%

If factor of 85 more:
US: 22,732,060 cases, 5,457 deaths. Estimated case mortality rate: .024%
TN: 282,285 cases, 43 deaths. Estimated case mortality rate: .015%
OH: 317,815 cases, 102 deaths. Estimated case mortality rate: .032%

But since California is on the West Coast, and has more travel and commerce with China than most other parts of the US which may have increased their number of initial virus carriers, let’s be conservative and go with:

If factor of 20 more:
US: 5,348,720 cases, 5,457 deaths. Estimated case mortality rate: .1%
TN: 66,420 cases, 43 deaths. Estimated case mortality rate: .065%
OH: 74,780 cases, 102 deaths. Estimated case mortality rate: .1%

On this being a study from just a single California county, some thoughts.

California has significantly more commerce and travel with China than most states. For this reason, it seems possible the virus could have been in the state before the January 20th first reported case in the US. Why wasn’t it known? I don’t know.

California has generally warmer weather during those months than other parts of the US, and it has a younger and healthier population in general as well, which may have helped the virus stay hidden in a larger number of asymptomatic carriers, maybe even building out some herd immunity. On this, I am just guessing, but before the Stanford study many people were wondering why numbers were so much lower in CA than most states. Cities like San Francisco have lots of travelers from China and population density is similar to NYC, but haven’t seen the same results. Is it possible the first virus carriers were in California? I have no idea. But it is possible that unique demographics allowed the virus to move through the population mostly feeling like flu season, remaining undiagnosed.

Whether the virus entered the US in California or somewhere else, it seems improbable there could be this many positive antibody tests without a longer virus cycle in the US. If the virus wasn’t in the US population earlier than January 20th, then the speed of spread is MUCH higher than previously thought. Either way, it’s safe to assume a lot more people have had the virus than anyone thought, and mortality rates are lower than most models show. There is not enough data yet, but it’s a good start.

With a little “I told you so” theme, this gives special weight and credibility to the March 17th article by Dr. Ioannidis, Stanford epidemiologist, where he wrote,

“If we had not known about a new virus out there, and had not checked individuals with PCR tests, the number of total deaths due to “influenza-like illness” would not seem unusual this year. At most, we might have casually noted that flu this season seems to be a bit worse than average. The media coverage would have been less than for an NBA game between the two most indifferent teams.”

Standing disclaimer from me, I am not an epidemiologist and I am not trying to model anything with these numbers. Epidemiologists have to incorporate data of those tested into age and health demographics of larger populations to truly estimate numbers. What I have provided is simple math, not any informed, demographically representative model.

It is worth noting that the epidemiologists and models have been SO wrong to date because they have so little actionable data. Example: that big IHME virus model, the one most cited to shape public policy…the one politicians have been using to justify their decisions…Yeahhhh…epidemiologist peers now say the projections are based “on a statistical model with no epidemiologic basis.”

Seriously. You can’t make this stuff up.

The public health messaging from moment one should have been: “We don’t know what we are dealing with yet. More data and research is needed. Since we don’t know, we advise those in the highest risk populations to take extreme caution, etc, etc…”

And, then they could have given the same self-quarantine, mask wearing, social distancing advice, but not accompanied with sky is falling level warnings, shutdowns, and the one-size fits all insanity that has followed. There was another way. I hope widespread virus and antibody testing start becoming the basis for public policy, not the previously embraced data-free decision making.

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