Second-Degree Polynomial Curve Fit #1
Disclaimer: This page shows a hypothetical model attempting to predict the future spread of coronavirus. It was automatically generated using an open-source algorithm. Such a model is inexact and may be wildly innaccurate. See below for more details.
For a list of other Extrapolation Models against the COVID-19 data, see our Models Page
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:
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 "e2a" model shown here attempts to fit a curve using a second-degree polynomial using numpy's
poly1d() function. This is Coviz's first model, and it is very simple. e2a is good for demonstrative purposes, yet it has many shortcomings.
- Model Short Name: e2a
- Model Full Name: Second-Degree Polynomial Curve Fit #1
- Submitted By: Michael Goldenberg
- Easy to write in python
- Easy to comprehend how it works
- Expects exponential growth
- Doesn't take into account herd immunity
- Doesn't take into account history of previous pandemics
- Assumes y is infinite, yet there's a finite max human population
- May arch down parabolicly, yet the number of cases can never decrease
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.
You can view graphs generated from data specific to individual countries below:
You can view graphs generated from data on previous days below: