@eladar said
With today being day 37, I think I found a decent logistics curve. Using my quadratic model, I estimated out to day 54, with day 48 as maximum death rate
Total deaths = c/(1+ae^(-bx)
a=1522.548562
b=.1606481609
c=41495
I looked at the table and found on day 73 deaths drop below 10 per day. So my Logistic model predicts May 7 as the day the pandemic is over for the ...[text shortened]... based on the data is 741, but since much data was estimated, not sure how valid any of this is lol.
Well, I can't get close to reproducing the US curve, but have a reasonable fit for the UK curve. I plotted your function against actual data and it isn't far off, but a note of caution. For the UK data I did linear regression on (daily deaths/cumulative deaths) vs cumulative deaths using various different start dates for the regression and got the following sets of outputs.
Date of initial point | total deaths | m | date of inflection point.
5th March | 7,490 | 0.2872 | 3rd April
14th March | 9,592 | 0.2595 | 5th April
26th March | 15,468 | 0.2255 | 8th April
28th March | 15,868 | 0.2235 | 8th April
All of these curves fit the UK data reasonably well and they differ in number of deaths by a factor of 2. It's possible to get reasonable looking answers and be way out.
Since the number of deaths in the UK looks like it's falling and the second entry in the list has the lowest root adjusted mean square error compared with the actual data - I expect deaths in the UK to continue to fall. So I think with some luck the UK might narrowly avoid 10,000 deaths. It remains to be seen.
With US data it might be better to analyse New York separately as it's dominating the figures, and so you're adding the logistic curve for NY to the logistic curve for the rest of the US and they probably have different parameters. Especially the exponent can be different.