This naive model is not a straw man! Such obvious nonsense models are the most common models quoted by the press, the most common models quoted by so-called ‘scientific experts’ and the most common models used to determine policy.
I think you underestimate the sophistication of the top epidemic modelers: Neil Ferguson, Adam Kucharski, Marc Lipsitch, and others. I tend to agree we need urgent empirical work on herd immunity thresholds (see my other comment) but the top epi people are aware of the considerations you raise. Communicating with the public is very challenging under the current circumstances and so it’s reasonable these people would choose words carefully.
Your statement is also empirically false. One of the most influential models is the “Imperial Model”, which certainly impacted UK policy and probably US and European policy too. Other countries did versions of the model. The lead researcher on the model literally became a household name in the UK. The Imperial Model is an agent-based model (not an SIR model). It has a very detailed representation of how exposure/contact differ among different age groups (work vs. school) and in regions with different population densities. It doesn’t assume the only intervention is immunity, and follow up work has tested many different interventions. (AFAIK, it does assume equal susceptibility. But as it’s an agent-based model you could experiment with heterogeneity in susceptibility. And I think evidence for variable susceptibility for reasons other than age remains fairly weak: https://twitter.com/OwainEvans_UK/status/1268873649202909185)
The lesson here may be that the public line about ‘there’s a fixed 70% herd immunity threshold’ is just that—a public line, and isn’t (and never was—if I remember rightly, the Imperial model from March estimated a herd immunity threshold of 40% without a lockdown) biasing the output of modelling. It could also be the case that doctors or generic public health people in the US are repeating the 70% line while epidemiologists and modellers with specific expertise (in the US and elsewhere) are being more methodical.
For what it’s worth, I haven’t heard much mention of a 70% immunity threshold in the UK recently, but I suspect the public conversation is worse in the US. That being said, there is still explicit derision of the concept of herd immunity, based on declining antibody counts that don’t give strong evidence for anything, so Zvi’s point that a lot of people don’t want to hear about herd immunity still clearly applies—see e.g. this:
Prof Jonathan Heeney, a virologist at the University of Cambridge, said the findings had put “another nail in the coffin of the dangerous concept of herd immunity”.
With that as the background, I’d be interested to know your opinion on this UK government report. They go over a bunch of factors that might increase transmission and say that a ‘reasonable worst case’ scenario is R_t increasing to 1.7 in September and remaining constant, assuming effectively zero government action—total second wave deaths are about double the first, with a similar peak of currently infected individuals and the peak in January (meaning a lot of time to course-correct and reimpose measures). As far as I can tell that’s just a guesstimate modelling assumption, not motivated by any kind of complicated transmission model.
(Honestly, this is a fair bit better than I would have guessed for the worst case scenario—a far cry from the sorts of things we discussed here in March.)
They don’t say how plausible they think this scenario is or give explicit motivation for R_t=1.7, just model the consequences of that change.
Does this look like a paper that doesn’t account for a potentially lower immunity threshold, so is probably overestimating the damage of a winter wave? And what about seasonality—they claim that the degree of seasonality of Covid-19 is highly uncertain. Is this true? I’ve heard some sources say it’s probably not that seasonal and others say it definitely is. What’s your read of that question? A winter wave seems to be the most likely route to a damaging second wave in Europe and it would be good to know how plausible that is.
It accreted detail for a decade just to prove that they were doing something. It is a good demonstration of the typical failure modes of an agent-based model. A useful model has very few parameters abstract parameters, so that they can be measured from reality. Agent-based models are useful to explore the space of relevant parameters, not to simulate a country. If simulating a country is “sophisticated,” then I don’t want to be a sophist.
I wasn’t saying I’m a fan of the Imperial Model and I agree with most of these points. I think there are epi modelers who aware of the limitations of models.
I think you underestimate the sophistication of the top epidemic modelers: Neil Ferguson, Adam Kucharski, Marc Lipsitch, and others. I tend to agree we need urgent empirical work on herd immunity thresholds (see my other comment) but the top epi people are aware of the considerations you raise. Communicating with the public is very challenging under the current circumstances and so it’s reasonable these people would choose words carefully.
Your statement is also empirically false. One of the most influential models is the “Imperial Model”, which certainly impacted UK policy and probably US and European policy too. Other countries did versions of the model. The lead researcher on the model literally became a household name in the UK. The Imperial Model is an agent-based model (not an SIR model). It has a very detailed representation of how exposure/contact differ among different age groups (work vs. school) and in regions with different population densities. It doesn’t assume the only intervention is immunity, and follow up work has tested many different interventions. (AFAIK, it does assume equal susceptibility. But as it’s an agent-based model you could experiment with heterogeneity in susceptibility. And I think evidence for variable susceptibility for reasons other than age remains fairly weak: https://twitter.com/OwainEvans_UK/status/1268873649202909185)
The lesson here may be that the public line about ‘there’s a fixed 70% herd immunity threshold’ is just that—a public line, and isn’t (and never was—if I remember rightly, the Imperial model from March estimated a herd immunity threshold of 40% without a lockdown) biasing the output of modelling. It could also be the case that doctors or generic public health people in the US are repeating the 70% line while epidemiologists and modellers with specific expertise (in the US and elsewhere) are being more methodical.
For what it’s worth, I haven’t heard much mention of a 70% immunity threshold in the UK recently, but I suspect the public conversation is worse in the US. That being said, there is still explicit derision of the concept of herd immunity, based on declining antibody counts that don’t give strong evidence for anything, so Zvi’s point that a lot of people don’t want to hear about herd immunity still clearly applies—see e.g. this:
With that as the background, I’d be interested to know your opinion on this UK government report. They go over a bunch of factors that might increase transmission and say that a ‘reasonable worst case’ scenario is R_t increasing to 1.7 in September and remaining constant, assuming effectively zero government action—total second wave deaths are about double the first, with a similar peak of currently infected individuals and the peak in January (meaning a lot of time to course-correct and reimpose measures). As far as I can tell that’s just a guesstimate modelling assumption, not motivated by any kind of complicated transmission model.
(Honestly, this is a fair bit better than I would have guessed for the worst case scenario—a far cry from the sorts of things we discussed here in March.)
They don’t say how plausible they think this scenario is or give explicit motivation for R_t=1.7, just model the consequences of that change.
Does this look like a paper that doesn’t account for a potentially lower immunity threshold, so is probably overestimating the damage of a winter wave? And what about seasonality—they claim that the degree of seasonality of Covid-19 is highly uncertain. Is this true? I’ve heard some sources say it’s probably not that seasonal and others say it definitely is. What’s your read of that question? A winter wave seems to be the most likely route to a damaging second wave in Europe and it would be good to know how plausible that is.
The Imperial model is worse than the SIR model.
It accreted detail for a decade just to prove that they were doing something. It is a good demonstration of the typical failure modes of an agent-based model. A useful model has very few parameters abstract parameters, so that they can be measured from reality. Agent-based models are useful to explore the space of relevant parameters, not to simulate a country. If simulating a country is “sophisticated,” then I don’t want to be a sophist.
I wasn’t saying I’m a fan of the Imperial Model and I agree with most of these points. I think there are epi modelers who aware of the limitations of models.