jeff-lewis / open-covid-19-analysis

Examples of how to load and analyze the data from the open-covid-19 dataset

Open COVID-19 Analysis

This repository contains Jupyter notebooks with examples of how to load and analyze the data from the open-covid-19 dataset. You can use Google Colab if you want to run your analysis without having to install anything in your computer, simply go to this URL: https://colab.research.google.com/github/open-covid-19/analysis.

See below for a list and description of each of the notebooks in this repo.

Exponential Modeling

This notebook explores modeling the spread of COVID-19 confirmed cases as an exponential function. While this is not a good model for long or even medium-term predictions, it is able to fit initial outbreaks quite well. For a more sophisticated and accurate model, see the logistic modeling notebook.

Logistic Modeling

This notebook explores modeling the spread of COVID-19 confirmed cases as a logistic function. It compares the accuracy of two sigmoid models: simple logistic function and Gompertz function, and finds the Gompertz function to be a fairly accurate short-term predictor of future confirmed cases.