Evidence map›Paper›PMID 33330882›Full record

ArticlemedRxiv : the preprint server for health sciences2021

Model-informed COVID-19 vaccine prioritization strategies by age and serostatus.

Kate M Bubar, Kyle Reinholt, Stephen M Kissler, Marc Lipsitch, Sarah Cobey, Yonatan H Grad, Daniel B Larremore

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Kate M BubarDepartment of Applied Mathematics, University of Colorado Boulder, Boulder, CO, 80303, USA.
Kyle ReinholtDepartment of Computer Science, University of Colorado Boulder, Boulder, CO, 80309, USA.
Stephen M KisslerDepartment of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Marc LipsitchDepartment of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Sarah CobeyDepartment of Ecology and Evolution, University of Chicago, Chicago, IL, 60637, USA.
Yonatan H GradDepartment of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Daniel B LarremoreDepartment of Computer Science, University of Colorado Boulder, Boulder, CO, 80309, USA.

Funding

MIDAS Coordination CenterU24GM132013 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI HOCHHEISER, HARRY S · 2019 to 2023
$8.1M
Casual, Statistical and Mathematical Modeling with Serologic DataU01CA261277 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI HANAGE, WILLIAM, LIPSITCH, MARC · 2020 to 2023
$2.8M
NCI NIH HHS U01 CA261277NIGMS NIH HHS U24 GM132013Wellcome Trust
6 · The paper itself

Abstract

Limited initial supply of SARS-CoV-2 vaccine raises the question of how to prioritize available doses. Here, we used a mathematical model to compare five age-stratified prioritization strategies. A highly effective transmission-blocking vaccine prioritized to adults ages 20-49 years minimized cumulative incidence, but mortality and years of life lost were minimized in most scenarios when the vaccine was prioritized to adults over 60 years old. Use of individual-level serological tests to redirect doses to seronegative individuals improved the marginal impact of each dose while potentially reducing existing inequities in COVID-19 impact. While maximum impact prioritization strategies were broadly consistent across countries, transmission rates, vaccination rollout speeds, and estimates of naturally acquired immunity, this framework can be used to compare impacts of prioritization strategies across contexts.

Identifiers

PMID33330882
PMCPMC7743091

What OpenQuestion holds

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Registered trials

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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.