Coronavirus : Update 9 from the Epicenter
I wanted to briefly shed a little more light on a question I asked a few days ago : “what is behind Italy’s strange graph of deaths vs recovered patients.
This is China’s graph. Green are recovered, orange are the deaths. It makes sense. Initially high mortality because death was the first indicator of the epidemic, and that rate falls as the country gets organized, testing increases and the true pattern emerges.
So how does one explain Italy’s curves? Forget the initial part of the curve – again, those numbers were due to the initial scramble. The bigger question is why have we settled at such a poor ratio?
Well, the mystery deepens as you look at how the numbers break down by region. As we know, it’s the north (and in particular Lombardy) that has taken the brunt of the epidemic. Towns like Bergamo have certainly generated the largest number of deaths, but remarkably they are not responsible for the poor survival rates.
Here is a breakdown of the total Deaths vs Recoveries by region as of today (28th March) :
As you can see there is a huge discrepancy in survival rates across the country. Discounting la Basilicata which only has one closed case, numbers range from 3% survival rate in le Marche to 73% in Fruili. The average numbers shown in the Italian graph hide what’s really going on.
So where does the discrepancy come from? I did some admittedly simple analysis to test the correlation of the survival rates to various factors. The closer to 1 the higher the correlation :
- Total number of cases in the region : 0.26
- Volume of testing in the region : 0.40
- Age index of the region : 0.04
So, there is little or no connection to age or testing – both of which are surprising. A score of 0.4 shows a moderate connection, in this case indicating some kind of saturation effect on intensive care capacity. But it’s not strong enough to tell the whole story, so I tried a couple of other tests, neither of which proved particularly illuminating :
- Population of the region : 0.37
- Population density : 0.39
- Intensive Care beds per person in a region : 0.19
Again, some indication that population density is a factor, however there is little correlation when you consider the number of IC beds available per person.
So, it remains a mystery for now. My top theories are :
- Despite the lack of correlation between median population age and mortality, the regions faring worse are the ones with oldest median infected population. Not sure how this might happen – perhaps it systematically took hold in church congregations or old people’s homes…
- I hate to say it – given how dedicated the front-line medical staff are, and what sacrifices they are making, but perhaps the equipment or procedures in some regions are simply better than others…
- I wrote a few days ago about the possible effect of general health on case outcomes around the world. Maybe there’s a link to smoking or air pollution, or dairy intake – who knows? Certainly the maps of air pollution and Covid19 cases in IC beds is startling :
Well, if I find out I’ll let you know. That’s all for now…
See here: https://www.google.com/url?sa=t&source=web&rct=j&url=https://www.actu-environnement.com/media/pdf/news-35178-covid-19.pdf&ved=2ahUKEwjAovP2q8XoAhVoTxUIHS0VBJMQFjAAegQIARAB&usg=AOvVaw3GLR8wiaA_Izq741q8zluc
[…] finally, at the end of Update 9 I threw out the idea that there may be a link between air pollution and Covid-19 transmission. I […]