Evidence map›Paper›PMID 34113457›Full record

ArticleRoyal Society open science2021

Optimal governance and implementation of vaccination programmes to contain the COVID-19 pandemic.

Mahendra Piraveenan, Shailendra Sawleshwarkar, Michael Walsh, Iryna Zablotska, Samit Bhattacharyya, Habib Hassan Farooqui, Tarun Bhatnagar, Anup Karan, Manoj Murhekar, Sanjay Zodpey and 4 more

Open access · goldAbstract read
In one paragraph

Article in Royal Society open science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 1 pooled it
7.2field-weighted citation impact, top 2% of its field
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

36 citing papers in PubMed, 1 synthesis or guideline pooled it, 94 citations in OpenAlex.

  1. Pooled it
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  5. A novel mathematical model for prioritization of individuals to receive vaccine considering governmental health protocols.The European journal of health economics : HEPAC : health economics in prevention and care · 2023
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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

14 authors at 6 institutions in 5 countries.

Mahendra PiraveenanComplex Systems Research Group, Faculty of Engineering, University of Sydney, New South Wales 2006, Australia.ORCID 0000-0001-7073-1421
Shailendra SawleshwarkarWestmead Clinical School, Faculty of Medicine and Health, University of Sydney, New South Wales 2006, Australia.ORCID 0000-0002-9399-0490
Michael WalshSchool of Public Health, Faculty of Medicine and Health, University of Sydney, New South Wales 2006, Australia.
Iryna ZablotskaWestmead Clinical School, Faculty of Medicine and Health, University of Sydney, New South Wales 2006, Australia.
Samit BhattacharyyaDepartment of Mathematics, School of Natural Sciences, Shiv Nadar University, Uttar Pradesh, India.ORCID 0000-0002-7357-7949
Habib Hassan FarooquiPublic Health Foundation of India, Delhi, India.
Tarun BhatnagarICMR-National Institute of Epidemiology, Chennai, India.
Anup KaranPublic Health Foundation of India, Delhi, India.
Manoj MurhekarICMR-National Institute of Epidemiology, Chennai, India.
Sanjay ZodpeyPublic Health Foundation of India, Delhi, India.
K S Mallikarjuna RaoIndustrial Engineering and Operations Research, Indian Institute of Technology Bombay, Mumbai, India.
Philippa PattisonOffice of the Deputy Vice-Chancellor, University of Sydney, New South Wales 2006, Australia.
Albert ZomayaSchool of Computer Science, Faculty of Engineering, University of Sydney, New South Wales 2006, Australia.
Matjaz PercFaculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia.ORCID 0000-0002-3087-541X
The University of Sydney · AUPublic Health Foundation of India · INNational Institute of Epidemiology · INChina Medical University · TWIndian Institute of Technology Bombay · INShiv Nadar University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since the recent introduction of several viable vaccines for SARS-CoV-2, vaccination uptake has become the key factor that will determine our success in containing the COVID-19 pandemic. We argue that game theory and social network models should be used to guide decisions pertaining to vaccination programmes for the best possible results. In the months following the introduction of vaccines, their availability and the human resources needed to run the vaccination programmes have been scarce in many countries. Vaccine hesitancy is also being encountered from some sections of the general public. We emphasize that decision-making under uncertainty and imperfect information, and with only conditionally optimal outcomes, is a unique forte of established game-theoretic modelling. Therefore, we can use this approach to obtain the best framework for modelling and simulating vaccination prioritization and uptake that will be readily available to inform important policy decisions for the optimal control of the COVID-19 pandemic.

Indexed as

cooperationevolutionary game theorysocial dilemmavaccination

Identifiers

PMID34113457
PMCPMC8188005
OpenAlexW3102760423

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.