This writer primarily profiles YouYang Gu, whose models and research have been pretty accurate regarding COVID trends. There are other researchers listed as well.
https://www.bloomberg.com/opinion/articles/2020-08-13/covid-spread-is-forcing-scientists-to-rethink-herd-immunity
Some of the points I like. Public dialogue vs actual data
On the definition of herd immunity
And how they see it trending going forward
https://www.bloomberg.com/opinion/articles/2020-08-13/covid-spread-is-forcing-scientists-to-rethink-herd-immunity
Some of the points I like. Public dialogue vs actual data
Quote:
He pointed to data on Louisiana, where cases were rising earlier in the summer and seemed to level off after various counties issued mask mandates.
But breaking the data down by county, he says, revealed a different story. Mask mandates varied in their timing, but places that implemented them late saw no more cases or deaths than those that did so early. "I don't think there's currently enough evidence to support the fact that recent policy interventions (mask mandates, bar closures) were the main drivers behind the recent decrease in cases," he wrote.
On the definition of herd immunity
Quote:
But scientists have little experience applying herd immunity to a natural infection, and what understanding they have is changing. Scientists have started to investigate the possibility that there's another critical factor here heterogeneity in the way humans interact, and in our inherent, biological susceptibility to this disease.
In a Science paper published in June, University of Stockholm mathematician Tom Britton and colleagues calculated that herd immunity might be reached after as few as 43% of a very heterogenous population becomes infected. People mix unevenly in a way that could lead to little pockets of immunity, slowing the spread of the virus long before the world achieves herd immunity.
We may also be heterogeneous in our biology. A recent paper in Science suggests that many people who've never been infected with SARS-CoV-2 carry a kind of immune cell, called a T-cell, which recognizes this novel virus and may partially mitigate an infection.
And how they see it trending going forward
Quote:
Those differences can inform disease models, says statistics professor Gabriela Gomes of the University of Strathclyde in Scotland. "What we see is that infections do not occur at random, but that people who are most susceptible to infection get exposed first," she says, leaving a pool of ever-less susceptible people behind.
So far, her predictions of the spread in the U.K., Belgium, Spain and Portugal have aligned well with reality. Her models showed small, shallow second peaks that would concentrate away from the places where the pandemic was most rampant last spring. For example, in Spain, the first outbreak was around Madrid, and now a smaller outbreak is happening around Catalonia.
She says her models keep predicting declines after the infection reached between 10% and 35% of the population. That doesn't mean the virus has gone away only that by her models, it won't explode in those same places again. Gu's models, too, predict no big second waves in New York City or Stockholm, but leave open the possibility of new outbreaks in relatively unaffected areas, just as Hawaii is now fighting outbreaks and New Zealand has imposed a new, short lockdown.
