Evidence map›Paper›PMID 34041970›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2021

Effectiveness of Face Masks in Reducing the Spread of COVID-19: A Model-Based Analysis.

Isabelle J Rao, Jacqueline J Vallon, Margaret L Brandeau

Abstract read
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Isabelle J RaoDepartment of Management Science and Engineering, Stanford University, Stanford, CA, USA.ORCID 0000-0002-6162-8235
Jacqueline J VallonDepartment of Management Science and Engineering, Stanford University, Stanford, CA, USA.ORCID 0000-0001-5756-8527
Margaret L BrandeauDepartment of Management Science and Engineering, Stanford University, Stanford, CA, USA.

Funding

Making Better Decisions: Policy Modeling for AIDS and Drug AbuseR01DA015612 · NIDA · STANFORD UNIVERSITY · PI OWENS, DOUGLAS K · 2002 to 2017
$9.4M
Making Better Decisions: Policy Modeling for AIDS and Drug AbuseR37DA015612 · NIDA · STANFORD UNIVERSITY · PI DOUGLAS K OWENS · 2019 to 2026
$6.3M
NIDA NIH HHS R01 DA015612NIDA NIH HHS R37 DA015612
6 · The paper itself

Abstract

backgroundThe World Health Organization and US Centers for Disease Control and Prevention recommend that both infected and susceptible people wear face masks to protect against COVID-19.

methodsWe develop a dynamic disease model to assess the effectiveness of face masks in reducing the spread of COVID-19, during an initial outbreak and a later resurgence, as a function of mask effectiveness, coverage, intervention timing, and time horizon. We instantiate the model for the COVID-19 outbreak in New York, with sensitivity analyses on key natural history parameters.

resultsDuring the initial epidemic outbreak, with no social distancing, only 100% coverage of masks with high effectiveness can reduce the effective reproductive number LIMITATIONS: Data regarding COVID-19 transmission are uncertain, and empirical evidence on mask effectiveness is limited. Our analyses assume homogeneous mixing, providing an upper bound on mask effectiveness.

conclusionsEven moderately effective face masks can play a role in reducing the spread of COVID-19, particularly with full coverage, but should be combined with social distancing measures to reduce

Indexed as

COVID-19EpidemicsHumansMasksPhysical DistancingSARS-CoV-2COVID-19dynamic disease modelepidemic controlhealth policymask effectiveness

Identifiers

PMID34041970
PMCPMC8484026

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.