Skip to content

lcbeas/covid_track1

Repository files navigation

covid_track1

Luke Beasley, Shahen Mirzoyan, Maxime Tchibozo, Akshay Pakhle

Guidelines : https://datascience.columbia.edu/dsis-center-health-analytics-launches-covid-19-data-challenge

Data Sources: 1. 2. https://ourworldindata.org/covid-testing

Predict values of the cumulative number of confirmed cases, recovered cases and deaths for May 1, 2020.

Submission format for Track 1:

Upload three .csv files (for the three target numbers, same format as in, e.g., https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv, with a column labeled 5/1/20 with positive integer predicted values. Teams will be given a folder to upload files.

Link to the team’s repo with code generating aforementioned files

Evaluation for Track 1:

Sum, over all entries that are ≥100 on Friday, April 24, 2020, 11:59 p.m. EDT, of the squared difference between log(prediction) and log(truth).

Allowed data and methods for Track 1:

Any legal data and methods are allowed, as long as the code is provided.

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Contributors 4

  •  
  •  
  •  
  •