Article Text

Challenges in reported COVID-19 data: best practices and recommendations for future epidemics
  1. Rinette Badker1,
  2. Kierste Miller2,
  3. Chris Pardee1,
  4. Ben Oppenheim1,
  5. Nicole Stephenson1,
  6. Benjamin Ash1,
  7. Tanya Philippsen1,3,
  8. Christopher Ngoon1,
  9. Partrick Savage1,
  10. Cathine Lam1,
  11. Nita Madhav1
  1. 1Metabiota Inc, San Francisco, California, USA
  2. 2Stockguard Inc, San Francisco, California, USA
  3. 3University of Victoria, Victoria, British Columbia, Canada
  1. Correspondence to Rinette Badker; rbadker{at}


The proliferation of composite data sources tracking the COVID-19 pandemic emphasises the need for such databases during large-scale infectious disease events as well as the potential pitfalls due to the challenges of combining disparate data sources. Multiple organisations have attempted to standardise the compilation of disparate data from multiple sources during the COVID-19 pandemic. However, each composite data source can use a different approach to compile data and address data issues with varying results.

We discuss some best practices for researchers endeavouring to create such compilations while discussing three key categories of challenges: (1) data dissemination, which includes discrepant estimates and varying data structures due to multiple agencies and reporting sources generating public health statistics on the same event; (2) data elements, such as date formats and location names, lack standardisation, and differing spatial and temporal resolutions often create challenges when combining sources; and (3) epidemiological factors, including missing data, reporting lags, retrospective data corrections and changes to case definitions that cannot easily be addressed by the data compiler but must be kept in mind when reviewing the data.

Efforts to reform the global health data ecosystem should bear such challenges in mind. Standards and best practices should be developed and incorporated to yield more robust, transparent and interoperable data. Since no standards exist yet, we have highlighted key challenges in creating a comprehensive spatiotemporal view of outbreaks from multiple, often discrepant, reporting sources and provided guidelines to address them. In general, we caution against an over-reliance on fully automated systems for integrating surveillance data and strongly advise that epidemiological experts remain engaged in the process of data assessment, integration, validation and interpretation to identify, diagnose and resolve data challenges.

  • COVID-19
  • epidemiology
  • public health

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  • Handling editor Seye Abimbola

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  • Contributors RB, CP, BA, TP, CN and PS acquired the data and contributed to the methodology. RB, KM and CL provided analysis. RB drafted the manuscript. KM, BO, NS, CP and NM provided critical revision of the article. All authors provided feedback on the manuscript. All authors approved the final version.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests All authors have completed the International Committee of Medical Journal Editors uniform disclosure form at and declare that they have been employed by Metabiota Inc; no other relationships or activities that could appear to have influenced the submitted work.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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