An open repository of real-time COVID-19 indicators

  1. Alex Reinhart
  2. Logan Brooks
  3. Maria Jahja
  4. Aaron Rumack
  5. Jingjing Tang
  6. Sumit Agrawal
  7. Wael Al Saeed
  8. Taylor Arnold
  9. Amartya Basu
  10. Jacob Bien
  11. Ángel A. Cabrera
  12. Andrew Chin
  13. Eu Jing Chua
  14. Brian Clark
  15. Sarah Colquhoun
  16. Nat DeFries
  17. David C. Farrow
  18. Jodi Forlizzi
  19. Jed Grabman
  20. Samuel Gratzl
  21. Alden Green
  22. George Haff
  23. Robin Han
  24. Kate Harwood
  25. Addison J. Hu
  26. Raphael Hyde
  27. Sangwon Hyun
  28. Ananya Joshi
  29. Jimi Kim
  30. Andrew Kuznetsov
  31. Wichada La Motte-Kerr
  32. Yeon Jin Lee
  33. Kenneth Lee
  34. Zachary C. Lipton
  35. Michael X. Liu
  36. Lester Mackey
  37. Kathryn Mazaitis
  38. Daniel J. McDonald
  39. Phillip McGuinness
  40. Balasubramanian Narasimhan
  41. Michael P. O’Brien
  42. Natalia L. Oliveira
  43. Pratik Patil
  44. Adam Perer
  45. Collin A. Politsch
  46. Samyak Rajanala
  47. Dawn Rucker
  48. Chris Scott
  49. Nigam H. Shah
  50. Vishnu Shankar
  51. James Sharpnack
  52. Dmitry Shemetov
  53. Noah Simon
  54. Benjamin Y. Smith
  55. Vishakha Srivastava
  56. Shuyi Tan
  57. Robert Tibshirani
  58. Elena Tuzhilina
  59. Ana Karina Van Nortwick
  60. Valérie Ventura
  61. Larry Wasserman
  62. Benjamin Weaver
  63. Jeremy C. Weiss
  64. Spencer Whitman
  65. Kristin Williams
  66. Roni Rosenfeld
  67. Ryan J. Tibshirani

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Abstract

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

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  1. Version published to 10.1073/pnas.2111452118
  2. ScreenIT

    SciScore for 10.1101/2021.07.12.21259660: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    These data sources provide information not available from standard public health reporting or other common sources, such as: Health Insurance Claims: Based on de-identified medical insurance claims from Change Healthcare and other health system partners, we release indicators on the estimated per-centage of covered outpatient visits and hospitalizations that involved COVID diagnoses or symptoms.
    Change Healthcare
    suggested: None
    The R and Python package software is public and open-source, at https://github.com/cmu-delphi/covidcast/.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Lastly, most data sources are provided under the Creative Commons Attribution license, and a small number have additional restrictions imposed by the data source; see https://cmu-delphi.github.io/delphi-epidata/api/covidcast_licensing.html. 1.5 Interactive Visualization: Since April 2020, Delphi has been maintaining and continually improving various online visualization tools for the COVIDcast indicators [22].
    Delphi
    suggested: (DelPhi, RRID:SCR_008669)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  3. Version published to 10.1101/2021.07.12.21259660 on medRxiv