Evidence map›Paper›PMID 33846672›Full record

ArticleTransport policy2021

Containing the COVID-19 pandemic with drones - Feasibility of a drone enabled back-up transport system.

Maximilian Kunovjanek, Christian Wankmüller

Abstract read
In one paragraph

Article in Transport policy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
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  4. Review
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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

2 authors.

Maximilian KunovjanekDepartment of Operations, Energy, and Environmental Management, Universitaet Klagenfurt, Universitätsstraße 65 - 67, 9020, Klagenfurt, Austria.
Christian WankmüllerDepartment of Operations, Energy, and Environmental Management, Universitaet Klagenfurt, Universitätsstraße 65 - 67, 9020, Klagenfurt, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic has profoundly altered common social and economic patterns as governments all over the world have been forced to take drastic measures to counter the spread of the disease. Among them, quarantine, the closure of borders, and social distancing are the ones that have affected transportation systems most severely. With the clear need to avoid all unnecessary direct human contact, an increased interest in contactless transportation and delivery modes emerged. Drones are a promising alternative in this regard, especially for the delivery of essential goods, such as COVID-19 viral tests. In this study, we therefore investigate how drones can be used to distribute viral tests to potentially infected patients. The novel approach that we propose is to use existing drone infrastructure to perform this task, where drones owned and operated by different public and private entities are retrofitted for the distribution of essential goods in the case of emergency. In a wider sense, we hence suggest the establishment of a drone enabled back-up transport system. Potential performance gains are analyzed through a mathematical time and cost model that was developed in close cooperation with the state Red Cross Organization and a utility drone manufacturer. Process design as well as parameter estimation are based on empirical investigation including, but not limited to, accompanying a COVID-19 mobile testing team in the field. The practical feasibility was verified by retrofitting drones initially assigned to other purposes. Additionally, policy recommendations, such as the establishment of public-public and public-private partnerships, were identified.

Indexed as

Corona virusCOVID-19DronesPolicyTransportViral testing

Identifiers

PMID33846672
PMCPMC8019130

What OpenQuestion holds

Textmetadata
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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.