[…] While there could be any number of reasons for the recent decline, it could just be “a normal distribution,” according to Ran Namerode, chairman of Redworth Capital Group, which focuses on global real estate, medical devices, movies productions and more.
COVID-19 has a periodicity of a classic Gaussian bell-curve shape wherever it appears…. In probability theory, the Gaussian distribution is a continuous function that approximates the exact binomial distribution of events.
“This bell’s life cycle is around six to eight weeks, with its peak appearing after about two or four weeks from the time when incidents begin to occur at a substantial rate,” Namerode said.In Israel, the disease first appeared in substantial numbers around March 10. The peak arrived on April 2, when the country had 765 new cases in 24 hours. Then, as Namerode predicted in a Post article in March, it began to decline to only a few new cases per day toward the end of April.
The World Health Organization has reported accumulated numbers of coronavirus cases in the world since the onset of the pandemic, and the numbers therefore appear to be constantly growing, Namerode said. However, if one looks at the pandemic by state or territory, the pattern is consistent in every one, and the virus begins to decline after around four weeks, he added.
“The first cases of morbidity in Hubei, China, appeared in late November,” Namerode said. “But a daily rate of 100 cases began around January 20. In mid-February, the daily rate reached thousands. However, by March 6, it dropped back to about 100 cases per day and has been continuously declining since then.”
“Within seven weeks the disease appeared, peaked and was suppressed almost completely,” he said. “When we talk about the end of the pandemic, what I mean is around 100 or 200 cases per day. These numbers do not represent a challenge to any health system. When the level has declined to these numbers, it is the end of the disaster.”
In South Korea, which implemented a strategy of large-scale testing instead of massive quarantine, dozens of daily new cases started to appear around February 20. By March 3, the daily rate had reached a peak of approximately 850 new cases. By March 15, it stabilized at an average of less than 100 new cases per day.
Italy was the first and one of the hardest-hit European countries. On February 22, more than 50 new cases appeared there. On March 21, the country peaked with approximately 6,500 new cases daily. Today, the country has fewer than 2,000 cases per day and is in a pattern of daily decline.
Coronavirus cases in Germany increased from March 10 to April 1. Four weeks later, the country has about 1,000 new diagnoses daily. The country has 83 million people, so its 1,000 cases per day is equivalent to about 100 in Israel.
Switzerland, Spain, the Netherlands and nearly all other countries show similar patterns.
“Since the basis of this model is its locality, an analysis of the US should be conducted at a state level rather than the countrywide one,” Namerode said.
After his first piece appeared in the Post, he was “criticized by so many people,” Namerode said. “But apparently it works in every country. Each territory behaved exactly like this.”
The bell curve’s length and number of deaths relates to how efficient each country was at implementing social distancing and other measures, he said. But there is no big difference between those countries that took extreme or medium measures, he added.
“This is not about biology or politics,” Namerode said, adding that his expertise is not in immunology nor infectious diseases.
“Numbers, however, can provide an explanation to natural phenomena even in places where the relevant science cannot,” he told the Post. As a businessman, he said, he is used to looking at numbers, analyzing them and then drawing data-driven conclusions.
“I looked at the numbers and could see a pattern, exactly how I would analyze market behaviors or trends,” he said.
Namerode is not the only businessman or mathematician to look for similar patterns. Nobel Prize laureate Michael Levitt successfully forecast the slowdown in the rate of infection in mainland China.
By looking at statistics emerging on the number of people infected and the number of deaths, Levitt identified a bounded growth pattern, showing that instead of the rate of infection increasing exponentially, it started to tail off.
Levitt also predicted that Israel would have few deaths. […]
Read full article at JPost
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