Evidence map›Paper›PMID 37234593›Full record

ArticleVaccine: X2023

Vaccine distribution with drones for less developed countries: A case study in Vanuatu.

Shakiba Enayati, James F Campbell, Haitao Li

Abstract read
In one paragraph

Article in Vaccine: X, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
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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.

Shakiba EnayatiSupply Chain and Analytics Department, College of Business Administration, University of Missouri, St. Louis, United States.
James F CampbellSupply Chain and Analytics Department, College of Business Administration, University of Missouri, St. Louis, United States.
Haitao LiSupply Chain and Analytics Department, College of Business Administration, University of Missouri, St. Louis, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Drones (uncrewed aerial vehicles or UAVs) introduce new opportunities to improve vaccine distribution systems, particularly in regions with limited transportation infrastructure where maintaining the cold chain is challenging. This paper addresses the use of drones to deliver vaccines to hard-to-reach populations using a novel optimization model to strategically design a multimodal vaccine distribution network. The model is illustrated in a case study for distributing routine childhood vaccines in Vanuatu, a South Pacific island nation with limited transportation infrastructure. Our research incorporates multiple drone types, recharging of drones, a cold chain travel time limit, transshipment delays for switching transport modes, and practical limits on the vaccine paths and drone trips. The goal is to locate facilities (distribution centers, drone bases, and relay stations) and design vaccine paths to minimize transportation costs, including the fixed costs for facilities and transportation links and variable costs for transportation through the network. Results show large potential cost savings and improved service quality provided by incorporating drones in a multimodal vaccine distribution system. Results also show the impact of introducing drones on the usage of other more expensive or slower transport modes.

Indexed as

Alternate vaccine delivery systemDronesLess-developed countriesNetwork designOptimization modelVaccine distributionVaccine supply chain

Identifiers

PMID37234593
PMCPMC10205531

What OpenQuestion holds

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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.