The adjustments I made to the model curves yesterday seem to be consistent with the data that came in yesterday. Today, I made an additional very small adjustment that shifted things inconsequentially. The curve fit is now very good. Excellent, actually. In the figure below, I have plotted the Predicted number of cases from the Gompertz curve vs. the actual number observed. It's a beautiful straight line with a slope of 1.01, which is to say that the predictions are off, on average, by about 1%. A perfect model would have a slope of 1.0. The correlation is actually much higher: 99.99%.
The rest of the standard plots are shown below with a mix of linear and log axes. I will call your attention to the daily case numbers and daily death numbers. The weekly oscillations have grown rather larger with a minimum around the weekend and a maximum around mid-week. This makes curve fitting a challenge because rather than trying to get the model to reproduce the data, the goal is shifted to trying to match the average of the data. As you can see, the model more or less goes right down the middle. The weekend data is too low, but yesterday's data pops up above the model. There's little doubt that yesterday's data includes backlogged data from over the weekend. I expect today's data to be a bit above the model, and then I expect things will start to fall again over the weekend.
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