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Herd immunity isn't a binary threshold after which zero cases occur. Even in a first-order homogeneous and well-mixed SIR model, you may asymptotically approach 1 - 1/R0 of the population infected without ever getting there. If you do cross that threshold ("overshoot"), then the case count starts to drop, but new people still get infected and die on the downslope. The only case where a disease will naturally burn itself out abruptly is if there was massive overshoot, which would be bad, because it means yet more people died than necessary for natural herd immunity. And for real some people have many more contacts than others (nurses, police, etc.). They get infected first, with disproportionate harm, but then become immune first with disproportionate benefit. That heterogeneity means 1 - 1/R0 is potentially a significant overestimate of the share of the population that needs to get infected for herd immunity, but there have been very limited efforts to quantify that so far. It seems like some people believe natural herd immunity (from recovered patients) could work like vaccination does, to effectively eradicate the disease? That's probably false--the most likely natural endgame would be that the coronavirus becomes endemic, always present with some low incidence, with continuing mortality that's very low (because the incidence is low, and because older people probably benefit from immunity from when they were younger and the IFR for young people is <1/100 of older people's) but nonzero. Finally, herd immunity and interventions (social distancing, masks, etc.) work together. It's possible (and likely I believe) that in hard-hit areas that now show R ~ 1, this is due to the product of both factors, and that either relaxing to their previous lifestyle or applying the same interventions in a naive (100% susceptible) population would show R > 1. |