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(1/12) Big thread with 16 diagrams to investigate the “dry tinder” hypothesis (mild preceding seasons affecting future mortality). We begin with a deep dive into the Nordics – in short: the data does not seem to refute the hypothesis (preview in pictures).pic.twitter.com/bRBhhY0Ibj
(2/12) I provide comments in the pictures to keep the thread manageable. The diagrams are organized by country. Although I generally advise against inter-country comparison, in this case I would suggest looking at the same diagrams for different countries side by side too.
(3/12) Sweden. The outlier in the group in terms of response policy. A record-low mortality in 18/19 and 19/20 flu seasons could have spared a large population vulnerable to respiratory illness.pic.twitter.com/VQCjaQGNF1
(4/12) Finland. Relatively high mortality with an upward trend over the past three seasons with a lack of deep valleys between seasons. The population vulnerable to respiratory illness have could possibly have succumbed prior to the outbreak of Covid19.pic.twitter.com/JRbMEHobJK
(5/12) Denmark. High mortality has been persistent between the last two flu seasons. Notably there was an outsized spike in mortality in 17/18, followed by an off-season peak due to heat and relatively high mortality 18/19. 19/20 the flu caught on early and was fairly severe.pic.twitter.com/z9nIA3Osyi
(6/12) Norway. Both 18/19 and 19/20 mild with moderate peaks and deep valleys. Did record two severe flu seasons 16/17 and 17/18 that reversed an otherwise downward trend in mortality. Notably greater impact on mortality from flu than Covid19.pic.twitter.com/Cc92hmdUVq
(7/12) Personal take: no virus exists in a vacuum. It is uncontroversial to assume there are frail individuals in all populations. Their proportion of the population will vary over time depending on a myriad of factors. It seems intuitive that preceding mortality would be one.
(8/12) Even hot summers cause spikes in mortality among these groups. It would seem reasonable to expect that many viruses can too. From the data I have observed so far, I cannot refute “dry tinder” as a possibly important factor determining how severe this spike becomes.
(9/12) I should refrain from jumping to conclusions, but after working with this data I am tempted to propose that the number of susceptible individuals at the start of the outbreak could be a greater determinant of outcome than any active response measures imposed by government.
(10/12) I imagine that this is a staple of any introductory course to epidemiology. But I have yet to see the theory being either proposed or debunked by insiders. Therefore, I truly hope to get some feedback from people in the field. Please help the diagrams to reach them.
(11/12) That said, I welcome everyone’s opinion regardless of background. A lot of the most informative reports rely on objective data that does not require a background in epidemiology. This data has piqued my interest so I will shamelessly ping en masse at the end of the thread
(12/12) Lastly, I have it set up so that I can reproduce these diagrams for any country that is tracked by the Max Planck Institute. If you would like to have a diagram on any specific country, let me know (probably won’t come with my commentary though).
It would also be very interesting if someone could do a similar analysis of countries outside the EU and of US states. Any takers?
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Apologies for the pings. I would greatly appreciate your opinion and even more so your ability to point out any potential error(s) I have made in this thread.
Updated version with new Swedish data as well as diagrams subdivided per age bracket available below.https://twitter.com/EffectsFacts/status/1300152192964452354?s=20 …
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