Evidence map›Paper›PMID 35162361›Full record

ArticleInternational journal of environmental research and public health2022

Common Demand vs. Limited Supply-How to Serve the Global Fight against COVID-19 through Proper Supply of COVID-19 Vaccines.

Yiqing Su, Yanyan Li, Yanggui Liu

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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.

Yiqing SuRegional Social Governance Innovation Research Center, Guangxi University, Nanning 530004, China.
Yanyan LiSchool of Public Policy and Management, Guangxi University, Nanning 530004, China.
Yanggui LiuSchool of Public Policy and Management, Guangxi University, Nanning 530004, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Vaccination plays an essential role in the fight against Coronavirus Disease 2019 (COVID-19). The currently insufficient vaccine production capacity makes it difficult to balance supply with demand, which has led to a contradiction between command demand and limited supply. According to analysis based on game theory, the attributes of COVID-19 vaccines vary with supply strategies formulated by vaccine-producing countries. This means that vaccine-receiving countries can only be motivated to prepare operable vaccine distribution plans through the supply of COVID-19 vaccines as global public goods. The rational distribution of global public goods must be guaranteed by a global supply institution system. To that end, Elinor Ostrom's eight design principles provide a basis for designing such a global supply system. This paper proposes a nested institution solution for guaranteeing the global supply of COVID-19 vaccines based on the design principles, which include clearly defined boundaries, proportional equivalence between benefits and costs, collective-choice arrangements, monitoring, graduated sanctions, conflict-resolution mechanisms, minimal recognition of rights to organize, and nested enterprises. To win this global fight against COVID-19, COVID-19 vaccines must not only be treated as global public goods, but countries must also be urged to coordinate cooperation in global institutional design, thus ensuring that COVID-19 vaccines can truly benefit all mankind.

Indexed as

COVID-19VaccinesCOVID-19 VaccinesHumansSARS-CoV-2VaccinationCOVID-19 VaccinesVaccinesCOVID-19 vaccinesglobal public goodsinstitutional designnested institution

Identifiers

PMID35162361
PMCPMC8834692

What OpenQuestion holds

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LicenceCC BY
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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.