*This is week 5 of Kaggle's COVID-19 forecasting series, following the [Week 4 competition](https://www.kaggle.com/c/covid19-global-forecasting-week-4). This competition has some changes from prior weeks - be sure to check the [Evaluation](https://www.kaggle.com/c/covid19-global-forecasting-week-5/overview/evaluation) and [Data](https://www.kaggle.com/c/covid19-global-forecasting-week-5/data) pages for more details. All of the prior discussion forums have been migrated to this competition for continuity.*

### Background

The White House Office of Science and Technology Policy (OSTP) pulled together a coalition research groups and companies (including Kaggle)  to prepare the [COVID-19 Open Research Dataset (CORD-19)](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge) to attempt to address [key open scientific questions on COVID-19](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge/tasks). Those questions are drawn from [National Academies of Sciences, Engineering, and Medicine’s (NASEM)](https://www.nationalacademies.org/event/03-11-2020/standing-committee-on-emerging-infectious-diseases-and-21st-century-health-threats-virtual-meeting-1) and the [World Health Organization (WHO)](https://www.who.int/blueprint/priority-diseases/key-action/Global_Research_Forum_FINAL_VERSION_for_web_14_feb_2020.pdf?ua=1).

### The Challenge

Kaggle is launching a companion COVID-19 forecasting challenges to help answer a [subset of the NASEM/WHO questions](https://www.kaggle.com/c/covid19-global-forecasting-week-4/overview/open-scientific-questions). While the challenge involves developing quantile estimates intervals for confirmed cases and fatalities between May 12 and June 7 by region, the primary goal **isn't only to produce accurate forecasts**. It’s also to identify factors that appear to impact the transmission rate of COVID-19.

You are encouraged to pull in, curate and share data sources that might be helpful. If you find variables that look like they impact the transmission rate, please share your finding in a notebook. 

As the data becomes available, we will update the leaderboard with live results based on [data made available](https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series) from the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE).

We have received support and guidance from health and policy organizations in launching these challenges. We're hopeful the Kaggle community can make valuable contributions to developing a better understanding of factors that impact the transmission of COVID-19. 

### Companies and Organizations

**There is also a call to action for companies and other organizations:** If you have datasets that might be useful, please upload them to [Kaggle’s dataset platform](https://www.kaggle.com/datasets) and reference them in [this forum thread](https://www.kaggle.com/c/covid19-global-forecasting-week-4/discussion/137078). That will make them accessible to those participating in this challenge and a resource to the wider scientific community.

### Acknowledgements

JHU CSSE for making the data available to the public. The White House OSTP for pulling together the key open questions. The image comes from the Center for Disease Control.

> **This is a Code Competition. Refer to [Code Requirements](https://www.kaggle.com/c/covid19-global-forecasting-week-4/overview) for details.**