Hacker News Reader

Comment by MatteoFrigo | Hacker News Reader

In case you haven't seen it before, https://euromomo.eu/graphs-and-maps has graphs of excess data for most of Europe (plus Israel) by age groups.

I find it a great resource to cross-check the misinformation of the mainstream media. For example, I challenge anybody to look at the 0-14 and 14-44 graphs and tell me when covid actually occurred.

Note that excess is not defined as a deviation from a typical year, but as a deviation from a typical year when no diseases are circulating. The goal of the euromomo website is to detect things like flu outbreaks, so the number of excess deaths is usually greater than 0, capturing the flu, heat waves, and presumably other disasters.

View on HN

I am talking about the raw "deaths in 2020 - average deaths in 2015-2019" number. It is much higher than just the COVID deaths.

> Note that excess is not defined as a deviation from a typical year, but as a deviation from a typical year when no diseases are circulating.

Data is deseasonalized, which does not mean what you wrote.

> Data is deseasonalized, which does not mean what you wrote.

You are correct that I oversimplified a complex situation. https://euromomo.eu/how-it-works/methods/ describes their method, especially section "Hypothesis".

Their model includes a sinusoidal seasonal component, as you noted. However, what I said is also true. From the Hypothesis section, third bullet:

"Parts of Spring and Autumn are less likely to be influenced by additional processes leading to excess deaths and the main pattern of mortality can therefore be modelling using only those periods, resulting in a baseline being the number of deaths expected when no particular process increases mortality."

As is clear from the bullet that precedes the text that I quoted, "processes leading to excess deaths" means winter infections and summer heat waves. So my description, while incomplete, is hopefully not too misleading. For completeness, I should mention that they also adjust for population growth. (You can't just do 2020-avg(2015-2019) because the total population is different.)

But winter excess deaths will not be zero _in the absence_ of winter infections. Because they add that sinusoidal component, they will be zero in an average-strong flu year for example, and less than zero in weaker flu years.

Your claim is empirically false. The total number of excess deaths was 81K in 2017, 158K in 2018, 101K in 2019. (You need to click a few buttons to enable data from past years.) The reason is that the sinusoidal amplitude is fairly small and is explicitly calibrated not to capture the flu. Look for example at the 2019-2020 winter graph: the empirical data matches the model until covid kicks in. For some reason, people die more in winter irrespective of the flu, and the sinusoidal component attempts to capture this effect.

Stepping back from the details for a minute: this is a 15-year long joint effort of public health authorities in Europe, with the explicit goal "to design a routine public health mortality monitoring system aimed at detecting and measuring, on a real-time basis, excess number of deaths related to influenza and other possible public health threats across participating European Countries." In terms of data analysis, their model is very simple, comprising a constant, a linear trend term for population growth, and a sine-wave term for non-flu seasonality. Do you really think that they would be so incompetent as to use a model that explicitly removes the flu, which is the very effect that they are trying to capture? Give them some credit, they know what they are doing.

Reply on news.ycombinator.com