Kaggle has recently launched a handful of COVID-19 competitions summarized with links below. This is a great opportunity for all ML practitioners to directly contribute!
While these challenges involve forecasting confirmed cases and fatalities by region, the primary goal isn’t to produce accurate forecasts, but rather to identify factors that appear to impact the transmission rate of COVID-19. Leaderboard will be updated with live results based on data made available from the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE).
COVID-19 Data Exploration & Research
The Roche Data Science Coalition (RDSC) is requesting the collaborative effort of the AI community to fight COVID-19. This challenge presents a curated collection of datasets from 20 global sources and asks you to model solutions to key questions that were developed and evaluated by a global frontline of healthcare providers, hospitals, suppliers, and policy makers.
This dataset is composed of a curated collection of over 200 publicly available COVID-19 related datasets from sources like Johns Hopkins, the WHO, the World Bank, the New York Times, and many others. It includes data on a wide variety of potentially powerful statistics and indicators, like local and national infection rates, global social distancing policies, geospatial data on movement of people, and more.