Evidence map›Paper›PMID 34853681›Full record

ArticleJournal of global health2021

Simulation of future COVID-19 epidemic by vaccination coverage scenarios in Japan.

Yuki Furuse

Abstract read
In one paragraph

Article in Journal of global health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

1 author.

Yuki FuruseInstitute for Frontier Life and Medical Sciences, Kyoto University, Kyoto, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCOVID-19 continues to impose significant morbidity and mortality in Japan even after implementing the vaccination program. It would remain elusive if restrictions for its mitigation were to be lifted or relaxed in the future.

methodsA simulation study that explored possible vaccination coverage scenarios and changes in the intensity of nonpharmaceutical intervention restrictions was performed to assess the impact of COVID-19 based on death count.

resultsAssuming the basic reproduction number of circulating viruses was 5.0, vaccines could prevent 90% of infections and 95% of deaths, and the vaccination coverage rate was high (75%, 80%, and 90% in people aged 12-39 years, 40-59 years, ≥60 years, respectively), approximately 50 000 deaths would occur over 150 days in Japan if all restrictions were lifted. Most deaths would occur among older adults, even if their vaccination coverage was assumed to be especially high. A low vaccination coverage scenario (45%, 60%, and 80% in people aged 12-39 years, 40-59 years, ≥60 years, respectively) would require periodic implementation of strict measures even if the modified lifestyle observed in 2020 was sustained and vaccines were very effective. Some restrictions could be relaxed under high vaccination coverage. However, in the worst-case scenario where vaccines had decreased efficacy, as we have observed for the Delta variant, and people lived a relaxed lifestyle, our simulation suggests that even high vaccination coverage would occasionally require strict measures.

conclusionsWe should carefully explore a manageable degree of restrictions and their relaxation. We will have to keep bracing for occasional surges of COVID-19 infection, which could lead to strict measures, such as those under a state of emergency. Such strategies are essential even after a wide rollout of vaccination.

Indexed as

COVID-19Vaccination CoverageAgedCOVID-19 VaccinesHumansJapanSARS-CoV-2COVID-19 Vaccines

Identifiers

PMID34853681
PMCPMC8576362

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.