There is a distinction though. Sid could actually debate, unlike the other three you mention (except when SId has borrowed Laz’s phone). You also neglected to mention the Tippo as @Malarkey calls them, I’ve been unsure of what of what that term meant in the past but now take it means an inbred moron.
There’s no “both sides” here, there’s rational people pointing out the utter insanity and hate filled rhetoric of TFK’s pretend culture warriors. I suppose @glasagusban deserves a pass as according to @Thomas_Brady he’s from Limerick’s deprived inner city (Caherdavin).
In a pandemic, the heterogeneity of the infectious process also makes forecasting difficult. When you flip a coin, the outcome is not affected by the flips prior. But in dynamic systems, the outcomes are more like those in chess: The next play is influenced by the previous one. Differences in outcome can grow exponentially, reinforcing one another until the situation becomes, through a series of individually predictable moves, radically different from other possible scenarios. You have some chance of being able to predict the first move in a game of chess, but good luck predicting the last.
That’s exactly what Gomes’s work attempts to do. She describes a model in which everyone is equally susceptible to coronavirus infection (a homogeneous model), and a model in which some people are more susceptible than others (a heterogeneous model). Even if the two populations start out with the same average susceptibility to infection, you don’t get the same epidemics. “The outbreaks look similar at the beginning. But in the heterogeneous population, individuals are not infected at random,” she told me. “The highly susceptible people are more likely to get infected first. As a result, the average susceptibility gets lower and lower over time.”
Effects like this—“selective depletion” of people who are more susceptible—can quickly decelerate a virus’s spread. When Gomes uses this sort of pattern to model the coronavirus’s spread, the compounding effects of heterogeneity seem to show that the onslaught of cases and deaths seen in initial spikes around the world are unlikely to happen a second time. Based on data from several countries in Europe, she said, her results show a herd-immunity threshold much lower than that of other models.
“We just keep running the models, and it keeps coming back at less than 20 percent,” Gomes said. “It’s very striking.”
Makes a lot of sense, and may explain why regions that had a massive first wave are not having a second wave, even after partially opening up. New York and New Jersey were by far the worst hit in the US, and their numbers have dropped significantly, even though probably no more than 10-20% were infected in those states.
Something like that. What she is suggesting is that some people are more likely to get infected than others, and when you cross a certain threshold of those people being infected it gets harder and harder for the virus to find new hosts.
