Accessible Unlicensed Requires Authentication Published by De Gruyter September 16, 2020

The Data Science of COVID-19 Spread: Some Troubling Current and Future Trends

Rex W. Douglass, Thomas Leo Scherer ORCID logo and Erik Gartzke

Abstract

One of the main ways we try to understand the COVID-19 pandemic is through time series cross section counts of cases and deaths. Observational studies based on these kinds of data have concrete and well known methodological issues that suggest significant caution for both consumers and produces of COVID-19 knowledge. We briefly enumerate some of these issues in the areas of measurement, inference, and interpretation.


Corresponding author: Rex W. Douglass, University of California San Diego, 92093 La Jolla, CA, USA, E-mail:

Funding source: Office of Naval Research

Award Identifier / Grant number: N00014-19-1-2491

Funding source: Charles Koch Foundation

Acknowledgments

Our thanks to the Center for Peace and Security Studies and its members, and to the Office of Naval Research [N00014-19-1-2491] and the Charles Koch Foundation for financial support. Thank you to all who provided feedback on the early draft, including two anonymous reviewers.

  1. Author contributions: Conceptualization, R.W.D., T.L.S., and E.G.; Investigation, R.W.D.; Writing–Original Draft, R.W.D.; Writing–Review & Editing, R.W.D. and T.L.S.; and Funding–E.G.

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Received: 2020-08-20
Accepted: 2020-08-28
Published Online: 2020-09-16

© 2020 Walter de Gruyter GmbH, Berlin/Boston