From 5Vs to 6Cs: Operationalizing Epidemic Data Management with COVID-19 Surveillance

  1. Akhil Sai Peddireddy
  2. Dawen Xie
  3. Pramod Patil
  4. Mandy L. Wilson
  5. Dustin Machi
  6. Srinivasan Venkatramanan
  7. Brian Klahn
  8. Przemyslaw Porebski
  9. Parantapa Bhattacharya
  10. Shirish Dumbre
  11. Erin Raymond
  12. Madhav Marathe

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Abstract

The COVID-19 pandemic brought to the forefront an unprecedented need for experts, as well as citizens, to visualize spatio-temporal disease surveillance data. Web application dashboards were quickly developed to fill this gap, including those built by JHU, WHO, and CDC, but all of these dashboards supported a particular niche view of the pandemic (ie, current status or specific regions). In this paper 1 , we describe our work developing our own COVID-19 Surveillance Dashboard, available at https://nssac.bii.virginia.edu/covid-19/dashboard/ , which offers a universal view of the pandemic while also allowing users to focus on the details that interest them. From the beginning, our goal was to provide a simple visual way to compare, organize, and track near-real-time surveillance data as the pandemic progresses. Our dashboard includes a number of advanced features for zooming, filtering, categorizing and visualizing multiple time series on a single canvas. In developing this dashboard, we have also identified 6 key metrics we call the 6Cs standard which we propose as a standard for the design and evaluation of real-time epidemic science dashboards. Our dashboard was one of the first released to the public, and remains one of the most visited and highly used. Our group uses it to support federal, state and local public health authorities, and it is used by people worldwide to track the pandemic evolution, build their own dashboards, and support their organizations as they plan their responses to the pandemic. We illustrate the utility of our dashboard by describing how it can be used to support data story-telling – an important emerging area in data science.

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    SciScore for 10.1101/2020.10.27.20220830: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: Due to the challenges in the collection and maintenance of the state/province-level data, with the inconsistent formats at upstream sources, our dashboard currently only supports 20 countries at this level of granularity. Also, while a large number of countries do not have this data reported, others provide it in the form of text-based reports or press releases, hence making it difficult to extract on a continual basis; this leaves room for improvement for Curation. D. Conclusion: This paper presents our epidemic surveillance dashboard in support of the COVID-19 pandemic planning and response. We propose the 6Cs metrics as a possible standard for epidemic data management. Our application has been used extensively by users globally. The need for such systems is clear, and we hope it spurs further discussion on how data can be collected and shared during such pandemics.

    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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.10.27.20220830v1 on medRxiv