Evidence map›Paper›PMID 36992217›Full record

ArticleVaccines2023

Projection of COVID-19 Positive Cases Considering Hybrid Immunity: Case Study in Tokyo.

Sachiko Kodera, Akito Takada, Essam A Rashed, Akimasa Hirata

Abstract read
In one paragraph

Article in Vaccines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Vaccination versus natural infection: A review of antibody differentiation techniques.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2025
    Review
  2. 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.

Sachiko KoderaDepartment of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan.ORCID 0000-0001-6595-0742
Akito TakadaDepartment of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan.
Essam A RashedGraduate School of Information Science, University of Hyogo, Kobe 650-0047, Japan.ORCID 0000-0001-6571-9807
Akimasa HirataDepartment of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan.ORCID 0000-0001-8336-1140

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since the emergence of COVID-19, the forecasting of new daily positive cases and deaths has been one of the essential elements in policy setting and medical resource management worldwide. An essential factor in forecasting is the modeling of susceptible populations and vaccination effectiveness (VE) at the population level. Owing to the widespread viral transmission and wide vaccination campaign coverage, it becomes challenging to model the VE in an efficient and realistic manner, while also including hybrid immunity which is acquired through full vaccination combined with infection. Here, the VE model of hybrid immunity was developed based on an in vitro study and publicly available data. Computational replication of daily positive cases demonstrates a high consistency between the replicated and observed values when considering the effect of hybrid immunity. The estimated positive cases were relatively larger than the observed value without considering hybrid immunity. Replication of the daily positive cases and its comparison would provide useful information of immunity at the population level and thus serve as useful guidance for nationwide policy setting and vaccination strategies.

Indexed as

COVID-19deep learningforecastingherd immunityhybrid immunityvaccination effectiveness

Identifiers

PMID36992217
PMCPMC10057330

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.