By Muslim Mirror Desk
Michael Levitt, a Nobel laureate and Stanford biophysicist says the real situation is not nearly as terrible as they make it out to be.
The recipient of the Nobel Prize for Chemistry in 2013 defies the doomsday predictions by various epidemiologists saying the data simply don’t support such a dire scenario-especially in areas where reasonable social distancing measures are in place.
Levitt’s statement assumes significance as he correctly predicted the trajectory that the coronavirus would take in China. After analysing the numbers in China from January, the biophysicist calculated that China would get through the worst of its coronavirus outbreak long before many health experts had predicted.
Taking note that the number of new cases being reported each day have fallen, Levitt wrote in early February: “This suggests that the rate of increase in the number of deaths will slow down even more over the next week.” As predicted, the number of deaths began decreasing every day. Subsequently, long before the world expected it, China is back up on its legs, with the most affected Hubei province all set to open up after more than two months of lockdown.
In fact, he predicted a ballpark figure of around 80,000, with about 3,250 deaths, when others were calculating deaths in the range of millions. As of Tuesday, China has reported 3,277 deaths with 81,171 positive cases.
Now he foresees a similar, tapering trend in the rest of the world.
After analysing data from 78 countries that report more than 50 new cases everyday, he says there are “signs of recovery” in many of them. His calculation does not focus on the total number of cases being reported in a country, but the total number of new cases identified every day. Levitt notes that the number of new cases in countries like China and South Korea were consistently on a decline.
“Numbers are still noisy, but there are clear signs of slowed growth,” he says. At the same time, the scientist acknowledges that his figures are messy and that the official case counts in many areas are too low because testing is spotty. But even with incomplete data, “a consistent decline means there’s some factor at work that is not just noise in the numbers,” he notes.