TY - JOUR
T1 - Accounting for uncertainty during a pandemic
AU - Zelner, Jon
AU - Riou, Julien
AU - Etzioni, Ruth
AU - Gelman, Andrew
N1 - Funding Information:
We thank Nina Masters and three anonymous reviewers for helpful comments and the U.S. National Science Foundation for grant 2055251 . J.Z. was supported by awards from the U.S. Centers for Disease Control and Prevention (no. U01IP001138-01 ) and the Simons Foundation . We thank Rob Trangucci for preliminary analysis of death certificate data.
Publisher Copyright:
© 2021 The Authors
PY - 2021/8/13
Y1 - 2021/8/13
N2 - We discuss several issues of statistical design, data collection, analysis, communication, and decision-making that have arisen in recent and ongoing coronavirus studies, focusing on tools for assessment and propagation of uncertainty. This paper does not purport to be a comprehensive survey of the research literature; rather, we use examples to illustrate statistical points that we think are important.
AB - We discuss several issues of statistical design, data collection, analysis, communication, and decision-making that have arisen in recent and ongoing coronavirus studies, focusing on tools for assessment and propagation of uncertainty. This paper does not purport to be a comprehensive survey of the research literature; rather, we use examples to illustrate statistical points that we think are important.
KW - DSML 2: Proof-of-Concept: Data science output has been formulated, implemented, and tested for one domain/problem
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U2 - 10.1016/j.patter.2021.100310
DO - 10.1016/j.patter.2021.100310
M3 - Review article
AN - SCOPUS:85112320566
VL - 2
JO - Patterns
JF - Patterns
SN - 2666-3899
IS - 8
M1 - 100310
ER -