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

11 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

General Decision Sciences

Access to Document

10.1016/j.patter.2021.100310

Cite this

@article{a3af995832044c098af6c90c1025be17,

title = "Accounting for uncertainty during a pandemic",

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.",

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

author = "Jon Zelner and Julien Riou and Ruth Etzioni and Andrew Gelman",

note = "Publisher Copyright: {\textcopyright} 2021 The Authors",

year = "2021",

month = aug,

day = "13",

doi = "10.1016/j.patter.2021.100310",

language = "English (US)",

volume = "2",

journal = "Patterns",

issn = "2666-3899",

publisher = "Cell Press",

number = "8",

}

TY - JOUR

T1 - Accounting for uncertainty during a pandemic

AU - Zelner, Jon

AU - Riou, Julien

AU - Etzioni, Ruth

AU - Gelman, Andrew

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

UR - http://www.scopus.com/inward/record.url?scp=85112320566&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85112320566&partnerID=8YFLogxK

U2 - 10.1016/j.patter.2021.100310

DO - 10.1016/j.patter.2021.100310

M3 - Review article

AN - SCOPUS:85112320566

SN - 2666-3899

VL - 2

JO - Patterns

JF - Patterns

IS - 8

M1 - 100310

ER -

Accounting for uncertainty during a pandemic

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this