Accounting for uncertainty during a pandemic

Jon Zelner; Julien Riou; Ruth Etzioni; Andrew Gelman

doi:10.1016/j.patter.2021.100310

Accounting for uncertainty during a pandemic

Jon Zelner, Julien Riou, Ruth Etzioni, Andrew Gelman

Research output: Contribution to journal › Review article › peer-review

10 Scopus citations

Abstract

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.

Original language	English (US)
Article number	100310
Journal	Patterns
Volume	2
Issue number	8
DOIs	https://doi.org/10.1016/j.patter.2021.100310
State	Published - Aug 13 2021
Externally published	Yes

Keywords

DSML 2: Proof-of-Concept: Data science output has been formulated, implemented, and tested for one domain/problem

ASJC Scopus subject areas

Decision Sciences(all)

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10.1016/j.patter.2021.100310

Cite this

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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.

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Accounting for uncertainty during a pandemic

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

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