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Stanford’s Nobel Laureate Develops A Prediction Model For SARS-CoV-2 by Dr. Tomislav Meštrovic, MD, Ph.D.

Nobel prize-winning scientist Prof Michael Levitt and Dr. Andrea Scaiewicz from Stanford School of Medicine in the US, together with Dr. Francesco Zonta from ShanghaiTech University in China, decided to tackle this issue with a comprehensive mathematical approach and showed that the trajectory of cases or deaths in any outbreak could be actually converted into a straight line.
COVID-19 behaving according to the Gompertz function. This study demonstrated that the progression of the COVID-19 epidemic did not follow an exponential growth law even in the very beginning, but instead, its growth is slowing down exponentially with time. More specifically, the results irrevocably show that COVID-19 cases grew in accordance with the Gompertz function, and not the sigmoid function.
The main difference is that the sigmoid function starts off growing exponentially (it has a constant exponential growth factor) and then slows down. At the same time, the Gompertz function is never exponential, but instead exhibits a growth rate that decreases exponentially from the very first confirmed case.

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