Evidence map›Paper›PMID 42168342›Full record

ArticleScientific reports2026

Longitudinal characterization of COVID-19 across a surveillance transition in Japan: thirteen epidemic waves, 2020-2025.

Chihiro Wakabayashi, Masayoshi Matsumoto, Shiho Sato, Yui Nakaizumi, Yuri Matsubara, Daisuke Matsubara, Yoshihisa Aiba, Izumi Oki, Yosikazu Nakamura, Hiroshi Yanagawa

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Chihiro WakabayashiDepartment of Health Sciences, Saitama Prefectural University, 820 sannomiya, Koshigaya, Saitama, Japan. wakabayashi@spu.ac.jp.ORCID http://orcid.org/0000-0001-5914-7961
Masayoshi MatsumotoDepartment of Health Sciences, Saitama Prefectural University, 820 sannomiya, Koshigaya, Saitama, Japan.
Shiho SatoDepartment of Health Sciences, Saitama Prefectural University, 820 sannomiya, Koshigaya, Saitama, Japan.
Yui NakaizumiDepartment of Health Sciences, Saitama Prefectural University, 820 sannomiya, Koshigaya, Saitama, Japan.
Yuri MatsubaraDepartment of Public Health, Dokkyo Medical University School of Medicine, Mibu, Tochigi, Japan.
Daisuke MatsubaraDivision of Community and Family Medicine, Center for Community Medicine, Jichi Medical University, Shimotsuke, Tochigi, Japan.
Yoshihisa AibaDepartment of Health Sciences, Saitama Prefectural University, 820 sannomiya, Koshigaya, Saitama, Japan.
Izumi OkiDepartment of Health Sciences, Saitama Prefectural University, 820 sannomiya, Koshigaya, Saitama, Japan.
Yosikazu NakamuraUtsunomiya Public Health Center, Utsunomiya, Tochigi, Japan.
Hiroshi YanagawaJapan Kawasaki Disease Research Center, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long-term comparison of COVID-19 epidemic waves is challenging because surveillance systems and reporting practices change over time. In Japan, official reporting shifted repeatedly between January 2020 and December 2025, from comprehensive nationwide notification to restricted reporting and sentinel-based surveillance. Using publicly available national data on reported cases and deaths, we characterized 13 epidemic waves while explicitly accounting for reporting transitions. We described weekly incidence and mortality rates per 100,000 population using population-based indicators to enable comparison across heterogeneous surveillance frameworks. Early waves showed very high observed case fatality ratios. Observed case fatality ratios declined markedly in subsequent waves, although estimates varied with reporting criteria and adjustments for reporting limitations, including periods when mild cases were excluded. Death counts were available only through week 18 of 2023, precluding evaluation of population-based mortality and observed case fatality ratios after the transition to sentinel-based surveillance. This transparent bridging framework enables longitudinal, comparability-oriented description of 13 epidemic waves in Japan across the 2023 surveillance transition. These findings should be interpreted as descriptive patterns observed under evolving surveillance contexts, and causal attribution is beyond the scope of this study.

Indexed as

COVID-19HumansIncidenceJapanLongitudinal StudiesPopulation SurveillanceSARS-CoV-2

Identifiers

PMID42168342
PMCPMC13396733

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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.