It still looks weird to me. For example, in Switzerland with no mitigation it estimates 1% of people infected now and 3% at the peak on Apr 14, which is 2.5 weeks from now. Since each infection lasts a couple weeks or more, and there have been few deaths and recoveries so far, that means <5% of the population will have been infected by that point. And then it says active infections will start falling. Why?

I think the model uses a much shorter time for active infections than 2.5 weeks. Not sure what it is, but I think it’s closer to 5 days or something like that, which seems to actually fit the behavior of the disease best, on a broad scale.

Agree that it looks weird. I’ve asked the authors of the project to add a cumulative graph, which makes these assumptions a lot clearer.

Wait, so your graph shows the number of people having their 2-day “infectious period” at any given time, which could be much lower than the number of people infected at a given time? That doesn’t seem to be explained on the page.

Anyway, I think the really important number is how many people are having their “required hospitalization period” at any given time (which is longer than 2 days). Maybe you could show that too, since you’re already showing the “care capacity” line?

That’s active infections. That number corresponds to something like 70% of the population having been infected at some point.

It still looks weird to me. For example, in Switzerland with no mitigation it estimates 1% of people infected now and 3% at the peak on Apr 14, which is 2.5 weeks from now. Since each infection lasts a couple weeks or more, and there have been few deaths and recoveries so far, that means <5% of the population will have been infected by that point. And then it says active infections will start falling. Why?

I think the model uses a much shorter time for active infections than 2.5 weeks. Not sure what it is, but I think it’s closer to 5 days or something like that, which seems to actually fit the behavior of the disease best, on a broad scale.

Agree that it looks weird. I’ve asked the authors of the project to add a cumulative graph, which makes these assumptions a lot clearer.

We used parameters based on a paper modelling Wuhan, that found that ~2 day infectious period predicted spread the best.

Adding cumulative statistics is in the pipeline; I or one of the devs might get around to it today.

Wait, so your graph shows the number of people having their 2-day “infectious period” at any given time, which could be much lower than the number of people infected at a given time? That doesn’t seem to be explained on the page.

Anyway, I think the really important number is how many people are having their “required hospitalization period” at any given time (which is longer than 2 days). Maybe you could show that too, since you’re already showing the “care capacity” line?