I could use a bit more of an explanation of how certain countries could have consistently higher excess deaths than others. Is the chart measuring against a European average, or do more and more people keep dying in Spain each year, or what?
Spain (and southern Europe in general) seems to have a particularly spikey flu season. Excess death expectations follow a smooth curve that peaks in the winter and comes to a nadir in the summer. Whenever the flu season hits the region, the excess deaths spike up above that curve.
Wouldn't that undermine the point you were making with those charts though? Meaning: that explanation makes it sound like the excess deaths in week 2 are just an artifact of the model--they're using a smooth curve to model something that is more chunky, and so when deaths spike up abruptly it looks like there are excess deaths. But this should be compensated for on the other end when deaths abruptly spike down. In any case, it actually would be fascinating to see the data on excess deaths relative to the European average. Or maybe even better: flu deaths per capita in the European countries. If flu deaths per capita patterned like covid deaths per capita, that would strongly suggest a cultural or biological explanation as opposed to a political one.
You’re forgetting one possible reason that Seatlle or SF did substantially better than New York. Perhaps the virus was circulating freely among the population In winter 2019.
"Although we fully support the ambition of Flaxman et al.1—to estimate the effectiveness of different NPIs from the available data—we find the underlying modelling approach problematic. Flexible parameterization leads to issues with identifiability, which are masked by model assumptions. In particular, we find it questionable to designate a country-specific effectiveness parameter to the last NPI that was introduced in each country. Besides the problems illustrated in Fig. 2, with large variations in the estimated effectiveness of NPIs, this prohibits prospective use of the model, as it is unknown at any given time whether the latest NPI will also be the last to be implemented in a particular country.
We conclude that the model1,3 is in effect too flexible, and therefore allows the data to be explained in various ways. This has led the authors to go beyond the data in reporting that particular interventions are especially effective. This kind of error—mistaking assumptions for conclusions—is easy to make, and not especially easy to catch, in Bayesian analysis. As NPIs are revoked, and possibly reintroduced over an extended period of time, more data will become available and practical identifiability of the separate effects of NPIs may be obtained. Until then, we suggest that the model1,3, and its conclusion that all NPIs apart from lockdown have been of low effectiveness, should be treated with caution with regard to policy-making decisions."
I could use a bit more of an explanation of how certain countries could have consistently higher excess deaths than others. Is the chart measuring against a European average, or do more and more people keep dying in Spain each year, or what?
Spain (and southern Europe in general) seems to have a particularly spikey flu season. Excess death expectations follow a smooth curve that peaks in the winter and comes to a nadir in the summer. Whenever the flu season hits the region, the excess deaths spike up above that curve.
Wouldn't that undermine the point you were making with those charts though? Meaning: that explanation makes it sound like the excess deaths in week 2 are just an artifact of the model--they're using a smooth curve to model something that is more chunky, and so when deaths spike up abruptly it looks like there are excess deaths. But this should be compensated for on the other end when deaths abruptly spike down. In any case, it actually would be fascinating to see the data on excess deaths relative to the European average. Or maybe even better: flu deaths per capita in the European countries. If flu deaths per capita patterned like covid deaths per capita, that would strongly suggest a cultural or biological explanation as opposed to a political one.
You’re forgetting one possible reason that Seatlle or SF did substantially better than New York. Perhaps the virus was circulating freely among the population In winter 2019.
It'd be interesting to use Lemoine's updated model to model U.S. states; might have to learn R.
This article in Nature also discussed some of the issues with Flaxman, et al: https://www.nature.com/articles/s41586-020-3025-y
"Although we fully support the ambition of Flaxman et al.1—to estimate the effectiveness of different NPIs from the available data—we find the underlying modelling approach problematic. Flexible parameterization leads to issues with identifiability, which are masked by model assumptions. In particular, we find it questionable to designate a country-specific effectiveness parameter to the last NPI that was introduced in each country. Besides the problems illustrated in Fig. 2, with large variations in the estimated effectiveness of NPIs, this prohibits prospective use of the model, as it is unknown at any given time whether the latest NPI will also be the last to be implemented in a particular country.
We conclude that the model1,3 is in effect too flexible, and therefore allows the data to be explained in various ways. This has led the authors to go beyond the data in reporting that particular interventions are especially effective. This kind of error—mistaking assumptions for conclusions—is easy to make, and not especially easy to catch, in Bayesian analysis. As NPIs are revoked, and possibly reintroduced over an extended period of time, more data will become available and practical identifiability of the separate effects of NPIs may be obtained. Until then, we suggest that the model1,3, and its conclusion that all NPIs apart from lockdown have been of low effectiveness, should be treated with caution with regard to policy-making decisions."