Hi there!
A few days ago I launched the COVID-19 timeline application: <a href="https://covid-gauss.site" rel="nofollow">https://covid-gauss.site</a>. I was curious about the actual timeline structure and a little annoyed by people in the media saying that the whole thing would last for months without any grounds for this kind of thesis.<p>I am not a scientist. This is a simple approximation to the Gaussian curve (so one of the possible mathematical models) and for now - nothing more. But for many countries such as Italy, Spain, Germany, the US, etc, the curve seems to be more accurate as the days go by.<p>I know the app is not perfect, updates will come. I surely need to make the model more stable.<p>Two days ago I heard in the media that in Germany couple hundred people died and more than 4000 got infected. That's bad. But if you look at the curve, you will see that for the day that was the exact estimate, 20000 more infections will come (resulting in 140000 total cases) and the whole party will end in 4 weeks. And that's a slightly different perspective.<p>Estimates are made for each country separately.<p>What do you think?<p><a href="https://covid-gauss.site/" rel="nofollow">https://covid-gauss.site/</a><p>~ etp.<p>PS. The code is open sourced and can be found here: <a href="https://github.com/eatthatpie/covid-gauss" rel="nofollow">https://github.com/eatthatpie/covid-gauss</a>. Please keep in mind that some parts of the code are still a bit messy. ;)
People complain, but a Gaussian is good enough for a rough approximation. The real curve is something like the derivative of a logistic, convoluted with something like a 15 day interval[1], and some noise, a lot of noise. Take a look at the SIR models and similar <a href="https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology" rel="nofollow">https://en.wikipedia.org/wiki/Compartmental_models_in_epidem...</a> , that are betters models. They are close enough to a Gaussian, but the Gaussian has a faster decay, so a Gaussian approximation underestimate the length of the tail.<p>I don't want to be mean, but the curve for Argentina is fucking inaccurate. [Hi from Argentina!] <a href="https://covid-gauss.site/country/argentina" rel="nofollow">https://covid-gauss.site/country/argentina</a> The expected peak is in the middle of May, the epidemy is not reducing here :(. Moreover, we made a huge mistake that effectively broke the quarantine last week, so I expect the peak to be earlier and higher.<p>Perhaps your fit is good enough for post-peak countries, but not for pre-peak countries.<p>[1] The important part is not how many people is diagnosed, but how many people is in the hospital. So it is necessary to model that the bad cases will need something like 2 weeks / 1 month of hospitalization.
This is really cool! I wonder how it fares with semi-reliable data, like where I live, in Belarus:<p><a href="https://covid-gauss.site/country/belarus" rel="nofollow">https://covid-gauss.site/country/belarus</a><p>Last I heard we were only testing people who had traveled and first-order contacts of those people, so the true numbers (and conversely the asymptomatic cases) could be much higher. That was over a month ago and perhaps we're testing more people now, given community spread.<p>Still, can we assume the Gaussian curve is even in the right ball park? It'd be great to visualize an end date for all this.