Spatial Optimization to Improve COVID-19 Vaccine Allocation.

Stephen Scroggins, Justin Goodson, Tasnova Afroze, Enbal Shacham

Abstract read

In one paragraph

Article in Vaccines, 2022. 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

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

4 authors.

Stephen ScrogginsTaylor Geospatial Institute, College for Public Health and Social Justice, Saint Louis University, St. Louis, MO 63103, USA.ORCID 0000-0002-6095-3474

Justin GoodsonDepartment of Operations and IT Management, Chaifetz School of Business, Saint Louis University, St. Louis, MO 63103, USA.

Tasnova AfrozeDepartment of Operations and IT Management, Chaifetz School of Business, Saint Louis University, St. Louis, MO 63103, USA.

Enbal ShachamTaylor Geospatial Institute, College for Public Health and Social Justice, Saint Louis University, St. Louis, MO 63103, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early distribution of COVID-19 vaccines was largely driven by population size and did not account for COVID-19 prevalence nor location characteristics. In this study, we applied an optimization framework to identify distribution strategies that would have lowered COVID-19 related morbidity and mortality. During the first half of 2021 in the state of Missouri, optimized vaccine allocation would have decreased case incidence by 8% with 5926 fewer COVID-19 cases, 106 fewer deaths, and 4.5 million dollars in healthcare cost saved. As COVID-19 variants continue to be identified, and the likelihood of future pandemics remains high, application of resource optimization should be a priority for policy makers.

Indexed as

delivery of health careecological and environmental COVID-19vaccines

Identifiers

PMID36679909

DOI10.3390/vaccines11010064

PMCPMC9866695

What OpenQuestion holds

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LicenceCC BY

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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.