The is – and has been – a lot of heated debate about “The best Strategy” for Covid mitigation. That is, the focus is – and has been – basically looking at the following:
- Number of cases of Covid
- Number of ICU’s for Covid-positive
- Number of deaths where Covid is present
And based on these variables, the debate has been around which countries have done well, which have not. And the apparent explanation for why some countries have performed better or worse than some other, can be illustrated by the below DAG:
That is, the “Goverment Response”, a.k.a “Strategy” or “Lockdown Level”, is the single factor that determines the outcome. However, a simple occular inspection of the trendlines for deaths and stringency index (here) does not reveal any strong relationship, in fact it reveals no relationship at all…
I spent a few minutes thinking about other potential factors, that could plausibly impact the outcome. Below a list of just a few of them (feel free to modify the list to your liking, e.g face masks…):
Population Size
Age Demographics
Overall Health Level
Overall Medical Care Quality & Availability (to all, not just those who can pay)
Quality of Elderly Care
Overall Economy
Level of “Industrialization”
Birth Rate
Quality, Level and Availability of Education
Proportion migrant population
Population Density
Cultural / Socioeconomic Interaction Patterns
Behavioral Patterns
Family Structure
Previous Flu Season
Season/Latitude
…
These are just a few off top of my head, there are many other factors plausibly have an impact on the Covid outcome, measured e.g. by the number of deaths.
Let’s look at one possible dependency graph (no longer a DAG!) that can be generated from these factors:
Now, I count to some 12-13 factors with potentially direct impact on the number of deaths, and I will not even try to count all the indirect factors.
That’s a lot of confounding variables….!
So, IMO, the laser focus on more or less exclusively on “Government Strategy”, is a “bit” _simplistic_ a model to explain the huge variability in the number of Covid-associated deaths, that is, I believe the problem is way more complex than how most of us, media, politicians as well as large part of the various populations have come to believe.
[Graphs in higher resolution can be found here]
systems perestroika - éminence grise 
Det går inte att läsa texten på grafen ens om jag laddar ner bilden.
// Mikael
On Thu, Aug 6, 2020 at 4:06 PM systems perestroika – éminence grise wrote:
> swdevperestroika posted: “The is – and has been – a lot of heated debate > about “The best Strategy” for Covid mitigation. That is, the focus is – and > has been – basically looking at the following: Number of cases of Covid > Number of ICU’s for Covid-positive Number of deaths wher” >
skaffa nya glasögon…! 🙂 Ok, lägger upp den på Github…
Eminence Grise, since you posted the guest-blog of @HaraldofW I’m following your blog. I liked the graph of OxCGRT x death/mio a lot. I totally agree that the ‘supposed’ direct correlation between Measures and Death is ridiculous. I would like to add some extra – probably pretty important – factors to your list:
‘Conditions’
– Excess Mortality Previous Year
– Number of Flu Vaccination p/capita
– Type of Flu Vaccines used
– Latitude (temp/moist condition due to aerosol spread)
– Number of G6PD p/capita
– Residential/Housing Situation
But also important factors to take into account are:
‘measures’ possibly causing extra mortality through:
– postponed/canceled regular hospital treatments
– postponed/canceled regular hospital diagnostics
– loneliness
– stress/anxiety/fear
– exposed ‘vulnerable’ more (due to lockdown in care-facilities)
– lowered immune system (due to lockdown)
– deadly treatments, ICU/intubation (due to panic and tunnel vision)
– allowed/banned treatments HCQ+ and alike
Besides that, defining the ‘outcome’ of government response only in terms of ‘deaths’ is very very limited and does not even begin to grasp the reality of the impact of ‘the measurements’ to society. I’d say the science tends to undeniably show a lot of evidence for concluding that the damage caused by the measurements is orders of magnitudes larger than what the measurements hoped to ‘gain’.
Thnx for your nice work!
Stefan
Hi Stefan, the OxCGRT x deaths_M graph (or one of them at least) has been shared on Twitter without context, and generally misunderstood by many – the one I’m referring to, is the one with the ~100 countries regression, and in isolation, without the additional graphs, the only thing one should infer from that graph is what I stated, that is, it’s only to be used for prediction, not for any causal analysis. In combination with the other charts, however, particularily the trend lines, it’s possible to draw the conclusion that there is no clear causal effect obtained from Lockdowns. Wrt. today’s post, you have good suggestions for factors, and sound conclusions, let’s see if I will incorporate some of them – today’s post was a quick hack done during a coffee-break, I just wanted to illustrate the contrast between the simplistic manner that many people think about covid, and it’s true complexity… so there’s not been any thorough dependency analysis done… Anyways, thanks for your constructive comment! –T
Stefan, it’s seems that you have a huge social network… the stat’s on the blog post you are referring to have been booming the past few days, most of the hits from Netherlands… 🙂
One significant thing which seems always to be overlooked is the effect of vacation times on disease spread.
So when comparing Sweden with Norway and Denmark this is good to know:
Copenhagen is the capital of Denmark and the greater Copenhagen area has a population of ~2 million — Denmark as a whole has a population of ~5.8 million.
Oslo is the capital of Norway. The greater Oslo area has a population of ~1 million — Norway as a whole has a population ~5.5 million.
Malmö (in Skåne) is the third largest city in Sweden and the greater area has a population of ~700,000. Göteborg (in Västra Götaland) is the second largest city in Sweden, and the greater area has a population of ~1 million. Stockholm is Sweden’s largest city and the greater Stockholm region has a population of ~2.4 million. Sweden as a whole has a population of ~10.3 million. (source wi
kipedia).
Winter break Copenhagen and Göteborg: 10.2.2020 – 14.2.2020
Winter break Oslo and Malmö : 17.2.2202 – 21.2.2020
Winter break Stockholm: 24.2.2020 – 28.2.2020
(source: https://publicholidays.dk https://publicholidays.no and https://publicholidays.se)
When the people of Copenhagen, Göteborg, Oslo and Malmö came back from their winter vacation, they did not arrive home sick, or at any rate they did not immediately head for the hospitals in large numbers . It was travellers in the last week of February and the first week of March who did so, in all three countries. (source https://en.wikipedia.org/wiki/COVID-19_pandemic_in_Denmark , https://en.
wikipedia.org/wiki/COVID-19_pandemic_in_Norway , https://en.wikipedia.org
/wiki/COVID-19_pandemic_in_Sweden )
I have written the health agencies in Norway and in Denmark asking ‘how many people were out of the country in the last week of February and the first week of March, but so far I have not received a response. The agency for tourism, which mostly is interested in travellers into the country said ‘probably around 1%’ for both countries when I asked them. For Sweden the number is an order of magnitude more, about 1 million people, i.e. more than 10% of the whole population.
source https://www.thelocal.se/20200611/public-health-agency-head-coronavirus-came-to-sweden-from-countries-that-were-under-our-radar
All the Skandinavian countries, Sweden included, did a very good job of containing covid from travellers from the Italian and Austrian alps. Unfortunately, this just meant that the next great group of cases seen in hospitals in Sweden were a strain of covid-from-someplace-else — the UK, the USA, France, Iran and the Netherlands, as we could see now that we can analyse covid strains for transmission paths. This seems highly significant to me.
Laura, I fully agree. The winter break, “sportlov”, occurs during 4 different weeks in Sweden, and for Stockholm, it’s week 9, plausibly the ‘best’ (worst ?) week in hindsight to have a huge part of the population to do massive international travel…. I was myself in Northern Italy that week…