This folder contains Jupyter notebooks and HTML files with examples of how to load, analyze and visualize the Open COVID-19 dataset dataset. You can use Google Colab if you want to run your analysis using notebooks without having to install anything in your computer, simply go to this URL: https://colab.research.google.com/github/open-covid-19/data.
See below for a list and description of some of the files in this folder.
This notebook contains very basic examples of how to load and filter data
from the Open COVID-19 dataset using pandas.
This HTML file contains the bare minimum needed to load the data from the
Open COVID-19 dataset and display it in a table using jquery.
This notebook showcases a methodology for estimating current mild, severe and critical patients by applying empirical data recorded in literature and validating the results against reported.
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.
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.