Evidence map›Paper›PMID 32817963›Full record

ArticlemedRxiv : the preprint server for health sciences2020

Vaccine optimization for COVID-19: who to vaccinate first?

Laura Matrajt, Julia Eaton, Tiffany Leung, Elizabeth R Brown

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2020. 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

4 authors.

Laura MatrajtVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Julia EatonUniversity of Washington, Tacoma, WA, USA.
Tiffany LeungVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Elizabeth R BrownVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

Funding

Leadership Group for the Infectious Diseases Clinical Research Consortium (IDCRCLG) - Momi-Vax DMID #21-0004 {Supplement #6}UM1AI148684 · NIAID · EMORY UNIVERSITY · PI DAVID S STEPHENS · 2020 to 2026
$77.2M
High-Performance Compute Cluster for Comprehensive Cancer and Infectious Diseases ResearchS10OD028685 · OD · FRED HUTCHINSON CANCER RESEARCH CENTER · PI BRADLEY, PHILIP · 2020 to 2020
$2.0M
NIAID NIH HHS UM1 AI148684NIH HHS S10 OD028685
6 · The paper itself

Abstract

A vaccine, when available, will likely become our best tool to control the current COVID-19 pandemic. Even in the most optimistic scenarios, vaccine shortages will likely occur. Using an age-stratified mathematical model paired with optimization algorithms, we determined optimal vaccine allocation for four different metrics (deaths, symptomatic infections, and maximum non-ICU and ICU hospitalizations) under many scenarios. We find that a vaccine with effectiveness ≥50% would be enough to substantially mitigate the ongoing pandemic provided that a high percentage of the population is optimally vaccinated. When minimizing deaths, we find that for low vaccine effectiveness, irrespective of vaccination coverage, it is optimal to allocate vaccine to high-risk (older) age-groups first. In contrast, for higher vaccine effectiveness, there is a switch to allocate vaccine to high-transmission (younger) age-groups first for high vaccination coverage. While there are other societal and ethical considerations, this work can provide an evidence-based rationale for vaccine prioritization.

Identifiers

PMID32817963
PMCPMC7430607

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.