Whenever something lands almost exactly on the only inflection point, in this case R0 of one where the rate of cases neither increases nor decreases, the right reaction is suspicion.
In this case, the explanation is that a control system is in play. People are paying tons of attention to when things are ‘getting better’ or ‘getting worse’ and adjusting behavior, both legally required actions and voluntary actions.
This connects to my older comment about the ‘Morituri Nolmus Mori’ (people not wanting to die’) effect—it is apparently so predictable that, with short-ish term feedback, it can form a control system with the other end being all the usual cognitive and institutional biases that prevent us from taking these events seriously and actually planning. This is weird and surprising and I don’t think anyone expected it back in early March/feb. It may tell us quite a lot about how society reacts to high-risk events with low probabilities in general.
I don’t think many of us predicted such a strong and consistent effect back from MNM in early March—as evidenced by updates like this:
5. Fewer than 3 million US coronavirus deaths: 90%
I held. Again, we saw very good news early, so to get to 3 million now we’d need full system collapse to happen quickly. It’s definitely still possible, but I’m guessing we’re now more like 95% to avoid this than 90%.
For Coronavirus, this also means the remaining uncertainties are ‘can we do better than the equilibrium position’ (sociological and political) and ‘how bad is the equilibrium position’ (mainly a matter of the disease dynamics). It seems to me, the equilibrium obviously ends in partial herd immunity (nowhere near 75% ‘full herd immunity’, because of MNM) with some unknown level of healthcare overload along the way, and as you say the US is practically bound for equilibrium, while in Europe there is still a push somewhat away from it (with e.g. national-level tracing and testing programs) but that push is not that strong and also dependent on individuals sticking with social distancing rules. European lockdowns had R between 0.6 and 0.8, noticeably below 1, indicating that they beat the equilibrium to some degree for a while, while Wuhan had r =0.4 as they were even freer of the push towards the R=1 equilibrium.
I am still skeptical of the strength of “MNM” effects. Control systems with huge lag times are infamously unstable. Are most people really able to judge whether they should be scared or not based on the R value from a week or two ago, which they don’t even know but have to eyeball from the trend in cases?
This connects to my older comment about the ‘Morituri Nolmus Mori’ (people not wanting to die’) effect—it is apparently so predictable that, with short-ish term feedback, it can form a control system with the other end being all the usual cognitive and institutional biases that prevent us from taking these events seriously and actually planning. This is weird and surprising and I don’t think anyone expected it back in early March/feb. It may tell us quite a lot about how society reacts to high-risk events with low probabilities in general.
I don’t think many of us predicted such a strong and consistent effect back from MNM in early March—as evidenced by updates like this:
For Coronavirus, this also means the remaining uncertainties are ‘can we do better than the equilibrium position’ (sociological and political) and ‘how bad is the equilibrium position’ (mainly a matter of the disease dynamics). It seems to me, the equilibrium obviously ends in partial herd immunity (nowhere near 75% ‘full herd immunity’, because of MNM) with some unknown level of healthcare overload along the way, and as you say the US is practically bound for equilibrium, while in Europe there is still a push somewhat away from it (with e.g. national-level tracing and testing programs) but that push is not that strong and also dependent on individuals sticking with social distancing rules. European lockdowns had R between 0.6 and 0.8, noticeably below 1, indicating that they beat the equilibrium to some degree for a while, while Wuhan had r =0.4 as they were even freer of the push towards the R=1 equilibrium.
I am still skeptical of the strength of “MNM” effects. Control systems with huge lag times are infamously unstable. Are most people really able to judge whether they should be scared or not based on the R value from a week or two ago, which they don’t even know but have to eyeball from the trend in cases?
People are looking at numbers of infected and dead, the bigger the numbers the scarier it is. When the numbers are down a lot, they believe it’s over.
I don’t think having a particularly accurate understanding is necessary for this back and forth.