COVID-19 Data Science and Behavior Links
by Tim Rogers you can also view this page on Tim’s github
The following are resources for non-medical, non-expert data-scientists interested in understanding the COVID-19 pandemic and human behavioral responses to same.
- Johns Hopkins Github repo for data and dashboard visualization
- Worldometers interactive data visualizations by country
- New York Times disease/death count data by state
- This Wikipedia article contains a table with number of tests conducted per country and results.
Human behavior datasets
- Dataset of major containment efforts by country and date
- Attitudes about COVID-19, Pew research center interactive
- International COVID-19 survey web page, with link to OSF
- Large dataset indicating tweet ID and sentiment score for COVID-19 tweets
- Unacast visualizations showing social distancing metrics including travel distance data.
- Google mobility reports showing plots of mobility data by country and subregion.
- This Kaggle COVID-19 page has links to datasets, code for models, and contests.
- Here is code to implement global forecast using SIR models
- R code and libraries for running/assessing SIR models.
- The UW Data-science coalition for COVID-19 has links to many resources to several resources including state and national data and visualizations. Here are some I thought particularly useful:
- Jerry Zhu’s “ten-hundred” plot to visualize exponential growth (Youtube explanation and Matlab code):
- Laurent Lessard’s JuPyTeR notebook for downloading/visualizing Johns Hopkins data
- Scott Sievert’s blog visualizing various aspects of disease spread
Visualizations and interactive demos
- Washington Post: How social distancing flattens the curve. This is a compelling visualization of SIR models, and why social distancing is the best intervention we currently have for limiting mass death in US.
- Interactive simulation modeling at Melting Asphalt. This fantastic interactive tools for SIR modeling gives a sense for how different assumptions about the disease do/do not impact total spread and fatalities, ending with an exercise in which you can try to figure out how best to “flatten the curve.”
- Video overview of SIR models and additional complexities from 3 brown 1 blue. This 20-minute video is worth your while if you want to understand some of the complexities that arise under SIR models with more realistic assumptions.
- Financial times COVID page with several graphical visualizations.
- An interactive visualization of the exponential spread of COVID-19 This visualization focuses on the growth curve (especially the exponential trend) of confirmed cases by country, US States.