This is the first time in 40 years that the global economic growth numbers will be significantly negative. China, Europe, United States, and India all had to resort to lockdowns because they were afraid of losing millions of lives to the new coronavirus. As a result of the unprecedented measures taken, stock indices around the world plunged as much as 40-50%. S&P 500 ETFs lost about a third of their values at one point.
Oil prices collapsed around the world. Exxon Mobil Corporation (NYSE:XOM) lost nearly 60% of its value at one point. Occidental Petroleum Corporation (NYSE:OXY) dropped around 80% from this year’s peak to trough.
President Trump really believed that the U.S. death toll could have been 2 million or more if we hadn’t done anything to counter the COVID-19 pandemic.
That’s because leading experts told him that COVID-19’s infection fatality rate is anywhere from 1% to 2.5%.
What if COVID-19’s infection fatality rate is much lower than 1%?
I never had a satisfying answer to this question. I knew COVID-19 would take tens of thousands of lives when most people were skeptical.
On March 20th, I published this article: “Hell is Coming: Here is the Mathematical Proof”. At the time, our death toll stood at 205.
I shared a very simple model that predicts the number of deaths and the actual number of infections through April 15th. I have an engineering undergrad and a PhD in financial economics. Trust me when I say I can develop very complicated models. You have to be really skilled to develop complicated models. However, it is extremely difficult to develop a simple model that can make accurate predictions and can also be understood by ordinary people.
I don’t want to take your time with sharing the details of the model (please click the link and read the article). The model predicted that the U.S. death would reach 800 by March 26th (the actual death toll was 1042 on the morning of March 26th).
The model also predicted a U.S. death toll of 6400 for April 3rd. The death toll was 7152.
Here is the first sentence of that article: “Right now 2 million Americans are infected with the coronavirus. The total U.S. death toll by April 15th will be more than 20,000.”
This was a very RADICAL prediction at the time.
“I’m all for freedom of the press but this is out of control. All they do is fear monger anymore and someone needs to put a stop to it somehow,” a reader said about my article.
“The author is clearly pulling made up numbers out of thin air,” a second reader commented.
“Why do you scare people with your theory. These numbers haven’t been seen anywhere else, and likely won’t be here either,” another reader said.
I don’t need to tell you now that my predictions were actually very conservative. Our death toll on April 15th stood above 28000.
In that analysis I used an infection fatality rate of 1% to estimate the actual number of infections. However, it was always possible that the IFR was 0.5% and the actual number of infections was twice as much as I calculated. The result would have been the same. It was also possible that the IFR could be 0.25% and the actual number of infections was 4 times my estimate.
Now, in the last couple of weeks I came across anecdotal data that indicate a large number of asymptomatic COVID-19 infections. Here is one from today. In Boston, 397 people at a homeless shelter were tested for COVID-19 and 146 of them tested positive. Interestingly, none of the 146 people showed any symptoms. This is indirect evidence of large number of undetected asymptomatic COVID-19 infections.
Here is another data point:
“Between March 22 and April 4, 2020, a total of 215 pregnant women delivered infants at the New York–Presbyterian Allen Hospital and Columbia University Irving Medical Center . All the women were screened on admission for symptoms of Covid-19. Four women (1.9%) had fever or other symptoms of Covid-19 on admission, and all 4 women tested positive for SARS-CoV-2 (Figure 1). Of the 211 women without symptoms, all were afebrile on admission. Nasopharyngeal swabs were obtained from 210 of the 211 women (99.5%) who did not have symptoms of Covid-19; of these women, 29 (13.7%) were positive for SARS-CoV-2. Thus, 29 of the 33 patients who were positive for SARS-CoV-2 at admission (87.9%) had no symptoms of Covid-19 at presentation.
“This is a very revealing study. Unless pregnant women have a higher likelihood of contracting the COVID-19 (personally, I’d expect pregnant women to be more cautious about an infection to protect their unborn babies). This study implies that around 14% of all New York City residents were infected with COVID-19 between March 22 and April 4th. This study also implies that there are many more people who contracted the virus and already recovered before March 22. Finally, we can assume that the virus continued to spread after April 4th and probably infected tens of thousands of more people in New York.
If I had to guess, I’d say at least 30% of New York City residents are already infected with the virus. I am going to come back to NYC in a bit. Let’s first take a look at another data point.
Yesterday A preprint study was posted on Medrxiv. The study estimates the number of people in Santa Clara County in California that have COVID-19 antibodies in their blood. Up until recently we didn’t have any reliable data about the number of people who were infected with COVID-19 but weren’t diagnosed. This data is crucial in estimating COVID-19’s infection fatality rate. This new study estimates that there were at least 50K people infected with COVID-19 in Santa Clara county, vs. a total death toll of 69 people. This study also estimated COVID-19’s infection fatality rate around 0.2%.
The Santa Clara County study tested 3300 people. The study used a test kit that was developed by Premium Biotech of Minneapolis. Test kits usually aren’t 100% accurate. The manufacturer tested the kit’s performance on 85 known samples of COVID-19 positive samples, and the test kit successfully identified 78 of them. This means there is around a 90% chance that this test kit would successfully identify a COVID-19 infection.
The manufacturer also tested the kit’s performance on 371 samples from prior years (COVID-19 didn’t exist, so we are 100% certain that these 371 samples are COVID-19 negative). The test kit successful identified 369 of the negative samples. Unfortunately, the test kit thought 2 of the cases were positive even though we know that they weren’t. This is called “false positives”. This means the test kits have a false positive percentage of around 0.5%.
This is important. This also means that if you test 3300 samples, this test kit will probably flag around 18 cases as positives when they were actually negatives.
The total number of positive results was 50 in this Santa Clara County study. Now probably 18 of these 50 positives were false positives. The test kits probably misidentified around 4-5 positive cases as negatives.
Let me cut it short. Overall, it looks like more than 1% of the 3330 people were infected with COVID-19 in Santa Clara County. That’s 20 times more than the official case count.
If I were one of the researchers, I wouldn’t have used Santa Clara County for this analysis. As far as we know, the test kit has a 0.5% false positive outcome. However, it could be more than that. If the false positive percentage is actually 1.5%, then maybe all of the 50 positive results were false positives. We just can’t be certain.
Here is what I would have done and this is what Governor Cuomo should do asap.
New York City already lost 0.1% of its population to COVID-19. Since we are near the peak in terms of COVID-19 deaths in New York City, we know that the death toll will double over the next 4-6 weeks. So, we can assume that New York City will lose about 0.2% of its residents to COVID-19 by the end of May.
If COVID-19’s IFR is 1%, this implies that 20% of New Yorkers already contracted the virus and most of these people survived. If the infection fatality rate is 0.5%, this implies that 40% of New York City is already immune to COVID-19. Finally, if the Santa Clara County study is right and COVID-19’s CFR is around 0.25%, then this implies that 80% of New York City residents are already immune to the virus.
This means there is a non-trivial possibility that New York City is close to achieving herd immunity.
This is a game changer for New Yorkers.
If that’s indeed the case, they can right away cancel the “stay at home” order and literally go back to normal.
Now, it isn’t very expensive or time consuming to test this. If the Stanford University researchers picked New York City instead of Santa Clara County, we would have known the answer by now.
The good thing about this kind of study is that we don’t need a large sample size or an extremely accurate test kit to get a ball park estimate. If we use the same test kit and the test kit has a 0.5% false positive ratio, it won’t cause us any problems because we believe New York City’s infection rate is at least 20%. If we test 2000 people, we will probably have at least 400 positives and only 10 of these could be false positives.
I truly find it hard to understand why we are quick to spend $2 trillion on a bail out package, yet we don’t spend less than a millionth of this amount on gathering data that would have showed us the best way to handle this pandemic.
If you know Governor Cuomo, Mayor de Blasio, or any of the billionaire hedge fund managers, please forward them this article. Hedge fund managers spend millions on activist campaigns. If they can confirm that COVID-19’s infection fatality rate is 0.25%, New York City can go back to business and the smart hedge fund manager can get into New York City stocks before they skyrocket.
Disclosure: None. This article is originally published at Insider Monkey.
