While this is a very nice simulation and explanation it has a serious flaw: It assumes a fixed CFR, IFR, and hospitalisation rate. This doesn't seem to be case as evidenced by the large differences between the countries with different response curves.
The inability to change the parameters is a major problem with the simulation and invalidates a number of conclusions at the end.
What we can observe so far is that CFR is heavily skewed towards the old and frail with significant co-morbidities. Most likely there is another, not yet fully identified, medical cofactor that makes this virus particularly difficult for a very small number of people of any age. Outside of those groups the virus doesn't seem to be very symptomatic for the majority of infected people. Note that symptomatic in the medical sense and common usage is not the same. The latter having a way higher subjective threshold.
The simulation should also account for the "weak tree" effect in that the majority of the susceptible will succumb to it on the first contact. In the following years the number of susceptible will be much lower and only go up with the remaining people going into ill health and becoming susceptible, if they haven't developed any immunity from the previous encounters.
A simulation to draw real conclusions from must have an adjustable IFR, CFR, the corresponding hospitalisation rates, and the age and health distribution of the population for a region to be modelled.
I'm taking data from the small company I used to be associated with. It covers a range of people who skew older, but are still very definitely working age.
No fatalities among the 200-odd employees. But around 2% became very ill indeed, and they still can't breathe properly weeks later.
Deaths are not the only problem. They're the most visible and the most shocking, but there will be 5-10X more serious illnesses, and it's not yet clear if these people will ever recover fully.
The article is about reducing R and maintaining it at or lower than 1. Yes, the article does not have sliders for age, CFR and IFR and what not, but the fact remains that people reading this must understand how to reduce R. I found it very informative and entertaining.
There's not necessarily a single value for R either, and if R is below 1 for society as a whole but above 1 in any particular community or location anywhere guess what happens next...
Yes, it also assumes no significant mutations/different strains. That's why they're saying actual epidemiological models used to support decision making are much more complicated. But it gives techy lay people (the worst kind of lay people) something to play with and understand a bit more
>NOTE: The simulations that inform policy are way, way more sophisticated than this! But the SIR Model can still explain the same general findings, even if missing the nuances.
This is a much simplified model for the purpose of education. So not including everything the real models include isn't really a flaw in my eyes.
Great article but I think this comment has a better point than this article. Especially the beginning of the article, is just unnecessary. I love the approach of Mr. Rosling which is basically go with the data and don't overreact.
If it's something bad,it's bad. You have to take precautions to it. But panic and fear is the things you have most likely not to on most of the situation. Because it'll lead you to the poor allocation of the resources. Although, these are just my opinions :)
Strains, as well as who gets the virus. C19 is equally contagious, but far from equal in who it kills. In NJ (where I live) 40% of deaths are to people in extended care. In Philadelphia, same people, but it's 50% of their deaths.
We need models that consider the details. We need models that consider that the virus preys on the weak (i.e., elderly and pre-existing conditions). Taking these profiles and applying them across the entire population is inaccurate ans misleading. It might even be dangerous.
Let me explain a bit better. Who is getting it now and requiring medical attention might not be the same going forward. There are a limited number of high risk people. High risk people is not everyone.
For example, I heard a new report that said the Bronx NYC has the highest per capita C19 infaction rate of any community nationally. Freightening? Maybe. How many other communities are similar to the Bronx? Similarly, how many living situations are similar to assisted living facilities. Taking edge and atypical cases and extrapolating that out over 330 million isn't a good model.
Yes. C19 is highly contagious. But we also know - from data - it is more likely to kill the weak than the strong.
The second biggest flaw: they don't account for the quality of the civil service.
Some countries can do it, some others cannot even at gunpoint because skills and discipline are not there, and you can not create them out of thin air.
> CFR is heavily skewed towards the old and frail with significant co-morbidities
Characterizing this demographic as "old and frail" is a bit much. And there's no science limiting this analysis to "significant" comorbidities. I hate to pick on one adjective, but it really seems like you believe that everyone who dies from this disease is at death's door already. There is absolutely no science to support that.
> the majority of the susceptible will succumb to it on the first contact.
And that's not correct at all. Even the most at-risk groups have a 90%+ survival rate.
The inability to change the parameters is a major problem with the simulation and invalidates a number of conclusions at the end.
What we can observe so far is that CFR is heavily skewed towards the old and frail with significant co-morbidities. Most likely there is another, not yet fully identified, medical cofactor that makes this virus particularly difficult for a very small number of people of any age. Outside of those groups the virus doesn't seem to be very symptomatic for the majority of infected people. Note that symptomatic in the medical sense and common usage is not the same. The latter having a way higher subjective threshold.
The simulation should also account for the "weak tree" effect in that the majority of the susceptible will succumb to it on the first contact. In the following years the number of susceptible will be much lower and only go up with the remaining people going into ill health and becoming susceptible, if they haven't developed any immunity from the previous encounters.
A simulation to draw real conclusions from must have an adjustable IFR, CFR, the corresponding hospitalisation rates, and the age and health distribution of the population for a region to be modelled.