Mono 2-Deg Poly Fit + Doubling (d1)

Next Doubling

# Cases	Date	Days since previous doubling
1000	2020-03-11	-
2000	2020-03-13	2
4000	2020-03-16	3
8000	2020-03-18	2
16000	2020-03-20	2
32000	2020-03-22	2
64000	2020-03-25	3
128000	2020-03-29	4
256000	2020-04-02	4
512000	2020-04-10	8
1.024 million	2020-04-28	18
2.048 million	2020-06-13	46
4.096 million	2020-07-24	41
8.192 million	2020-10-19	87
16.384 million	2020-12-13	55
32.768 million	2021-05-07	145
65.536 million	2021-12-11	218

About This Model

The chart above attempts to predict the future number of COVID-19 cases for the given region. The Y-axis shows the number of people who have tested positive and the X-axis is time. The vertical red line (labeled "Hypothetical Data") is the present day (on our latest graph). Everything to the right of the red line is the future, and its lines are calculated from the extrapolation model.

In this model, there's three distinct predictions:

1. Last 30 days The purple line does a second-degree polynomial curve fit against the most-recent 30-days of data in the dataset
2. Last 7 days The green line does a curve fit on the most-recent 7 days of data
3. Last 3 days The red line does a curve fit on the most-recent 3 days' data

You'll find that, depending on recent events in the past 3-days, the red line will flap around more wildly day-to-day than the others. This can be a useful coorelation indicator for the consequences of recent events, such as prematurely ending lockdowns.

The "d1" model shown here builds on the e2b model, which does a simple monotonic second-degree polynomial curve fit using numpy's poly1d() function.

Unlike e2b, this model tracks the "doubling" time, which is the amount of time it takes for the number of COVID-19 cases in the given region to multiply by two. Doubling tracking begins at 1,000 cases. Therefore, the second doubling is 2,000. Then 4,000, 8,000, 16,000, etc

At every doubling, a black tick is drawn on the graph indicating the height of the doubling in terms of number of cases. A vertical transparent "band" is also drawn behind each of these ticks. The distance between these bands over time gives a visual to the amount of time it takes for COVID-19 cases to double over time.

To the right of the red "hypothetical data" line, predictions for the next date of doubling are marked with a dashed vertical black line originating from the x-axis and terminating at its cooresponding trace

Below the chart is a data table that lists every doubling that occured in the historical dataset for the given region, starting with 1,000 cases. The last row in this table is a projection using the curve fitting algorithm to predict when the next doubling will occur in the future.

This model is an improvement over Coviz's first model (e2a), yet it's still very simple and has many shortcomings.

• Model Short Name: d1
• Model Full Name: Mono 2-Deg Poly Fit + Doubling
• Submitted By: Michael Altfield

Pros

1. Easy to write in python
2. Easy to comprehend how it works

Cons

1. Expects exponential growth
2. Doesn't take into account herd immunity
3. Doesn't take into account history of previous pandemics
4. Assumes y is infinite, yet there's a finite max human population
5. The monotonic implementation causes some nearly-negligable precision to be lost in the polynomial curve fit plot

If you'd like to submit your own Extrapolation Model that fixes some of the issues above, see our guide to developing and submitting models.

Country-Specific Charts

You can view graphs generated from data specific to individual countries below:

Previous Graphs

You can view graphs generated from data on previous days below: