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README

Netlify Status

The U.S. COVID-19 Atlas provided county-level visualizations and analytics to reveal a more detailed pandemic landscape with local hotspots of surging COVID cases that were missed by state-level data. The Atlas is live at: https://USCovidAtlas.org.

screenshot

DATA

For more information about additional datasets used in the Atlas, see our Data page. Detailed data documentations about different variables and data sources are available at the data-docs folder.

Current Release

Because there is no one single validated source for county-level COVID cases and deaths for real-time analysis, we incorporate multiple datasets from multiple projects to allow for comparisons.

  • USAFacts:this dataset is provided by a non-profit organization. The data are aggregated from CDC, state- and local-level public health agencies. County-level data is confirmed by referencing state and local agencies directly.
  • 1P3A: This was the initial, crowdsourced data project that served as a volunteer project led by Dr. Yu Gao, Head of Machine Learning Platform at Uber. We access this data stream using a token provided by the group.
  • CDC: the US Center for Disease Control and Prevention is the nation's health protection agency. The data provided includes vaccination and county level testing data.
  • New York Times: the New York Times newspaper releases time-series data compiled from state and local governments and health departments. The data is available via their github repository and was updated several times each day during the Pandemic.

We also include information from the following datasets:

Previously used Datasets:

  • Bin Yu Group: Predicted death counts and Severity index by County

Data Details

1P3A

To access raw 1P3A data, you must contact the 1P3A for a token directly.

Not all cases from 1P3A data can be assigned to a particular county, see following (the list is being updated as new data comes in everyday)

  • 1P3A does NOT assign cases in New York to specific counties, which includes New York City, Kings, Bronx, and Richmond.
  • Cases reported for US Virgin Islands, Guam are NOT included.
  • Cases in the following areas can NOT be assigned and hence are NOT included: Southwest Utah; Southeast Utah; Central Utah; Tri County, Utah; Kansas City, MO; Benton and Franklin, WA.
  • Other unassigned cases (or “cases to be assigned”) are NOT included.
  • Cases reported in the Military and some Correctional Centers are NOT included.

METHOD

For a complete breakdown about the methods used in the Atlas, see our Methods page.

The hotspot detection (a Local Indicator of Spatial Autocorrelation) is powered by Geoda. We also use many other features from GeoDa including natural breaks classification and cartogram techniques. See below for how one can apply these methods to reproduce the results using above datasets.

More information about the Geoda project can be found here.

COLLABORATORS

The US Covid Atlas open-science collaboration project was comprised of a coalition of research partners that were been integral to developing and expanding the Covid Atlas to meet the needs of health practitioners, planners, researchers, and the public.

Team

Check out the Team page for more information about the many contributors to the Atlas: https://uscovidatlas.org/about#team.

Advisory

The Advisory page details information about the Community Advisory Board: https://uscovidatlas.org/about#advisory.

Research Partners:

  • Healthy Regions & Policies Lab (HEROP) and the Center for Spatial Data Science (CSDS) at the University of Chicago, and later, the University of Ilinois. The Atlas was originally developed as a project co-led by Marynia Kolak, Xun Li, and Qinyun Lin at the Center for Spatial Data Science. The HEROP Lab at CSDS led the project until late 2022, when HEROP moved to the University of Illinois at Urbana-Champaign, where it remains as its home institution.
  • The Yu Group at UC Berkeley’s Department of Statistics is working with Response4Life to develop a severity index for each hospital to help distribute supplies when they become available. The Yu Group generates daily updates of COVID data and contributes both hospital and county-level severity index data for the Atlas.
  • County Health Rankings & Roadmaps (CHR&R) led by Lawrence Brown. CHR’s goal is to improve health outcomes for all and to close the health gaps between those with the most and least opportunities for good health. CHR leads efforts to connect socioeconomic and health vulnerability indicators to the Atlas to better contextualize and inform findings.
  • CSI Solutions led by Roger L. Chaufournier and Kathy Reims are critical to connecting the Atlas with rural health partners across the country to define and prioritize needs for care management during the pandemic. CSI leads efforts in developing and refining this “Communities of Practice” forum.
  • AFI DSI COVID-19 Research Group at UW-Madison. This group led by Brian Yandell was an early institutional partner to amplify regional efforts to respond to the pandemic. Kevin Little of Informing Ecological Design was critical in connecting the Atlas team with a nationwide network and leading user-group sessions to review the atlas, align priorities, and ensure it was effective for a wide audience. Steve Goldstein continued to work with the team in data validation efforts.

RESOURCES

Learn

There are multiple resources the learn more about the data, methods, technical infrastructure, and more at the main Covid Atlas site:

Questions

If you have a question regarding a specific dataset, please contact the dataset author(s) directly. If you have any questions regarding the Atlas, contact us by via: https://uscovidatlas.org/contact

Citations

Please cite us according to how you used the US Covid Atlas:

Website: Marynia Kolak, Qinyun Lin, Dylan Halpern, Susan Paykin, Aresha Martinez-Cardoso, and Xun Li. The US Covid Atlas, 2022. Center for Spatial Data Science at University of Chicago. https://www.uscovidatlas.org

Published Work of beta Version: Kolak, Marynia, Xun Li, Qinyun Lin, Ryan Wang, Moksha Menghaney, Stephanie Yang, and Vidal Anguiano Jr. "The US COVID Atlas: A dynamic cyberinfrastructure surveillance system for interactive exploration of the pandemic." Transactions in GIS 25, no. 4 (2021): 1741-1765.

Codebase of beta Version: Xun Li, Qinyun Lin, Marynia Kolak, Robert Martin, Stephanie Yang, Moksha Menghaney, Ari Israel, Ryan Wang, Vidal Anguiano Jr., Erin Abbott, Dylan Halpern, Sihan-Mao. (2020, October 12). GeoDaCenter/covid: beta (Version beta). Zenodo. http://doi.org/10.5281/zenodo.4081869

INFRASTRUCTURE DETAILS

Current Repos, Subdomains and branches of the Atlas

Repositories

URLs

There are various other branch deploys on the US Covid Atlas web hosting (netlify) that are not publicly listed.

Running the React App

This project was bootstrapped with Create React App.

Required Environment Variables

REACT_APP_MAPBOX_ACCESS_TOKEN=<token> Enter your mapbox token (must have access to the resources that are hard-coded into the style.json and style_light.json files).

REACT_APP_ALERT_POPUP_FLAG=false Just leave this "false".

Variables to connect with the Covid Stories content:

REACT_APP_EMAIL_FORM_URL=
REACT_APP_STORIES_PUBLIC_URL=

The following are all related to Google BigQuery credentials:

BIGQUERY_PROJECT_ID=
BIGQUERY_CLIENT_ID=
BIGQUERY_CLIENT_EMAIL=
BIGQUERY_CLIENT_X509_CERT_URL=
BIGQUERY_SECRET_KEY=
BIGQUERY_SECRET_KEY_ID=

Env in production and workflows

All of the above variables (and perhaps a couple of others must also exist in the Netlify environment. If the data-pull-1.yml workflow is enabled, then the BigQuery variables must also be added to this repositories list of secrets.

Quickstart

  1. Clone this repository
  2. Use cp .env.example .env to create a local .env file and update values as needed.
  3. Install node / npm, and install yarn with npm i -g yarn
  4. From the repository root, run yarn to install dependencies
  5. From the repository root, run yarn fetch-data to fetch the latest data
  6. From the repository root, run yarn start to start the app

Available Scripts

In the project directory, you can run:

yarn fetch-data

Updates the data in the public data directory as required by the frontend application.

yarn start

Runs the app in the development mode.
Open http://localhost:3000 to view it in the browser.

The page will reload if you make edits.
You will also see any lint errors in the console.

yarn docs

Generates JSDoc site, output to the folder jsdocs. See jsdoc folder for configuration.

yarn test

Launches the test runner in the interactive watch mode.
See the section about running tests for more information.

yarn build

Builds the app for production to the build folder.
It correctly bundles React in production mode and optimizes the build for the best performance.

The build is minified and the filenames include the hashes.
Your app is ready to be deployed!

See the section about deployment for more information.

yarn eject

Note: this is a one-way operation. Once you eject, you can’t go back!

If you aren’t satisfied with the build tool and configuration choices, you can eject at any time. This command will remove the single build dependency from your project.

Instead, it will copy all the configuration files and the transitive dependencies (webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except eject will still work, but they will point to the copied scripts so you can tweak them. At this point you’re on your own.

You don’t have to ever use eject. The curated feature set is suitable for small and middle deployments, and you shouldn’t feel obligated to use this feature. However we understand that this tool wouldn’t be useful if you couldn’t customize it when you are ready for it.