Was New York actually infected at 83% by May 2020?
Infection Fatality Myth: In May 2020, we had data and analyses that showed the infection fatality rate of COVID was around 0.1%, and that New York had passed herd immunity - Covid Myth Buster News
This article dates back to May 2020. It’s my first article. I wanted to share it with you. I took a lot of flack for stating early IFR was 0.1%. I stand by what I wrote then. Built on solid data, the article has aged well. I wanted to appease the fear I saw around me. Almost 2 years later, I am still working hard at it… I hope you enjoy it.
May 2020, Paris - I wanted to share with some of you what I believe happened in NYC with Covid. I collected a series of tangible verifiable data points and some excellent research and tried to make some sense out of it. The data point to a possible infection of more than 83% of New Yorkers since early January: 70% would have been asymptomatic and didn't feel a thing, and 13% would have had actual symptoms: fever, headaches...
Building on the incredible online thermometer data provided by Kinsa Insight, I started from - what I saw in the data - was the peak of the pandemic on March 18: an approximately 4% of outlier in "% of ill" in the whole of NYC.
Data from pregnant women delivering at the New York–Presbyterian Allen Hospital and Columbia University Irving Medical Center late March show 88% were asymptomatic (possibly their immune system is weakened by the foetus protecting HLA-G placental protein).
"In our area, which includes upper Manhattan and the Bronx, about 15 percent of patients who came to us for delivery tested positive for the coronavirus, but around 88 percent of these women had no symptoms of infection. That means 13.5 percent of all our patients during this time were infected with the coronavirus but weren’t exhibiting symptoms. " Washington Post by Dena Goffman and Desmond Sutton
Multiple research papers point to such high levels of asymptomaticity in urban environment (see research on undocumented infection in China). Closed environments like cruise ships or aircraft carriers, as well as retirement homes show lower levels of asymptomatic, most likely because of heavier load contamination and/or immune senescence.
Accounting for 85% of asymptomatic, that probably means that around 27% of New Yorkers were contaminated with Covid-19 that week! Even though it is based on temperature collected data, this is very much consistent with the contamination level found in pregnant women. This is also congruent with the claim of Professor Michael Levitt, Nobel Prize winner, Stanford School of Medicine, that the slowing pace of death at the time was indicative that the epidemic was reaching its peak.
I had now three solid factual bases to model the infected population in New York City; I then grossly simulated a skewed Gauss curve to compute weekly infection levels between early January and May 10.
Proxying for Covid-19 Mortality Rate
Last week, I spent some time trying to evaluate the actual mortality rate of Covid-19. The available data was pretty much useless as most countries haven't been testing much. And frankly - by the looks of it - the industrialisation of the testing technology and process - understandably - is still not very reliable.
I decided to try and figure out a way to proxy for data that - everything else being equal - would equate to be more reliable. One way I thought to do that was to normalise the number of tests by the number of death, in other words: the ∑ of tests / ∑ of Covid-19 deaths. Why? Well, because the higher that number, the more the testers would have tested outside of the hospital, and thus they are closer to the truth and have a much better picture of reality. For example, as of today Singapore has undertaken 224,262 tests for 21 deaths. With 10,679 tests undertaken against each victim of Covid-19, Singapore health authorities clearly have scanned largely beyond the victim's family, contacts and health workers. And thus, the 0.1% Covid mortality rate found in Singapore is probably much closer to the reality than countries that have undertaken possibly more tests, say Germany with 3,14 million tests, but who have only undertaken 393 tests against each victim.
I then plotted the mortality rates of a number of comparable nations against this proxy, and the visual convergence was pretty amazing to me. And I am sure it is to you too (see chart below). Though each country infection fatality rates (IFR) is/seems randomly different, the same biological/epidemiological gravity is pulling. To dissipate the randomness, I simulated mathematically a larger testing pool - using Google Sheets - I built a convergence curve (Power Series) of it all, and the mortality rate points to 0.1% as you can see for yourself (with a Rsqr 0.76). Fundamentally, this convergence curve eliminates the randomness and points to a sort of biological gravity.
Having viewed a video of Chemistry Nobel Prize winner, Pr. Michael Levitt of Stanford School of Medicine, where he was explaining the early plateauing of the Covid-19 epidemic. I decided to send it to him, and he was kind enough - despite a crazy agenda around Covid - to send me a kind email saying he thought my assumptions were "very reasonable". I had another validation that this number made sense over the weekend through a wonderful exchange with my INSEAD 97J class (love you guys!). So I decided to use 0.1% mortality rate, which matches with other coronaviruses lethality and computes to 15,000 cumulated death in NYC, a curve that matches very well with reality (as of May 10, 2020).
Based on this data and analysis, New Yorkers are probably safe now as they would have attained herd immunity, specially if basic measures are taken to reduce the R0 and lower the immunity threshold.
I am sure many will find flaws to the approach I have described above. It has the benefit of existing, of being data-driven and of having multiple verification points that anchor it quite solidly. I have kept the data mostly untouched, and have triangulated it repeatedly. I am not an epidemiologist, but I am an experienced consultant trained in the scientific approach with experience in biotechnology. My purpose all along was - and still is - to try to help bring a better picture of the situation in these chaotic times. I started thinking the virus was killing 5.5%, but data, analogies with cancer growth models I had been tinkering with, combined with the scientific approach I was taught, all helped me down to the other end of the spectrum: 0.1% mortality rate. I am confident this piece of work can be replicated to other countries and cities, very quickly, and I will start doing it for my home country France.
The data and the excellent work of many researchers seem to indicate clearly that Covid-19 has spread through the world mostly unseen, and caught everybody by surprise. I hope this will help bring some clarity and bring us all closer together towards a solution.
All the best,
As you can imagine, I never imagined we’d still be where we are today knowing what I knew then. It is still unbelievable that mainstream media, public health authorities, Big Tech and major part of the population still haven’t realised how disproportionate their reaction was all along, that the protection of cross-immunity was brushed under the carpet, and that our lives were uselessly disrupted for 2 years.
Have a great weekend.