Evidence map›Paper›PMID 35581274›Full record

ArticleScientific reports2022

Investigating the efficiency of dynamic vaccination by consolidating detecting errors and vaccine efficacy.

Yuichi Tatsukawa, Md Rajib Arefin, Shinobu Utsumi, Jun Tanimoto

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Yuichi TatsukawaInterdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga-koen, Kasuga, Fukuoka, 816-8580, Japan. tatsukawa.yuichi.534@s.kyushu-u.ac.jp.
Md Rajib ArefinInterdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga-koen, Kasuga, Fukuoka, 816-8580, Japan. arefin.math@du.ac.bd.
Shinobu UtsumiInterdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga-koen, Kasuga, Fukuoka, 816-8580, Japan.
Jun TanimotoInterdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga-koen, Kasuga, Fukuoka, 816-8580, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Vaccination, if available, is the best preventive measure against infectious diseases. It is, however, needed to prudently design vaccination strategies to successfully mitigate the disease spreading, especially in a time when vaccine scarcity is inevitable. Here we investigate a vaccination strategy on a scale-free network where susceptible individuals, who have social connections with infected people, are being detected and given vaccination before having any physical contact with the infected one. Nevertheless, detecting susceptible (also infected ones) may not be perfect due to the lack of information. Also, vaccines do not confer perfect immunity in reality. We incorporate these pragmatic hindrances in our analysis. We find that if vaccines are highly efficacious, and the detecting error is low, then it is possible to confine the disease spreading-by administering a less amount of vaccination-within a short period. In a situation where tracing susceptible seems difficult, then expanding the range for vaccination targets can be socially advantageous only if vaccines are effective enough. Our analysis further reveals that a more frequent screening for vaccination can reduce the effect of detecting errors. In the end, we present a link percolation-based analytic method to approximate the results of our simulation.

Indexed as

Vaccine EfficacyVaccinesComputer SimulationHumansVaccinationVaccines

Identifiers

PMID35581274
PMCPMC9114144

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.