Evidence map›Paper›PMID 34414313›Full record

ArticleJMA journal2021

Epidemiology of Coronavirus Disease 2019 in Japan: Descriptive Findings and Lessons Learned through Surveillance during the First Three Waves.

Yuzo Arima, Kazuhiko Kanou, Takeshi Arashiro, Yura K Ko, Kanako Otani, Yuuki Tsuchihashi, Takuri Takahashi, Reiko Miyahara, Tomimasa Sunagawa, Motoi Suzuki

Abstract read
In one paragraph

Article in JMA journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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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

10 authors.

Yuzo ArimaInfectious Disease Surveillance Center, National Institute of Infectious Diseases, Tokyo, Japan.
Kazuhiko KanouInfectious Disease Surveillance Center, National Institute of Infectious Diseases, Tokyo, Japan.
Takeshi ArashiroInfectious Disease Surveillance Center, National Institute of Infectious Diseases, Tokyo, Japan.
Yura K KoInfectious Disease Surveillance Center, National Institute of Infectious Diseases, Tokyo, Japan.
Kanako OtaniInfectious Disease Surveillance Center, National Institute of Infectious Diseases, Tokyo, Japan.
Yuuki TsuchihashiInfectious Disease Surveillance Center, National Institute of Infectious Diseases, Tokyo, Japan.
Takuri TakahashiInfectious Disease Surveillance Center, National Institute of Infectious Diseases, Tokyo, Japan.
Reiko MiyaharaInfectious Disease Surveillance Center, National Institute of Infectious Diseases, Tokyo, Japan.
Tomimasa SunagawaInfectious Disease Surveillance Center, National Institute of Infectious Diseases, Tokyo, Japan.
Motoi SuzukiInfectious Disease Surveillance Center, National Institute of Infectious Diseases, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionCoronavirus disease 2019 (COVID-19) has caused unprecedented global morbidity and mortality. Japan has faced three epidemic "waves" of COVID-19 from early 2020 through early 2021. Here we narratively review the three waves in Japan, describe the key epidemiologic features of COVID-19, and discuss lessons learned.

methodsWe assessed publicly available surveillance data, routine surveillance reports, and other relevant sources-multiple indicators were monitored to improve interpretation of surveillance data. Weekly trends for each wave were described based on the number of case notifications; number of tests performed; proportion of those tests that were positive for the novel coronavirus; the prevalent number of COVID-19 hospitalizations (total hospitalizations and those categorized as severe); and number of COVID-19 deaths. For each indicator and wave, we recorded the first calendar week to show an increase over two consecutive previous weeks, along with the peak week.

resultsThe spring wave was characterized by detection of cases imported from China, followed by notifications of sporadic cases without travel history, clusters, and mild/asymptomatic cases. The summer wave saw a large increase in notifications and a younger age distribution, but in the context of increased testing with lower test positivity. The winter wave brought considerable morbidity and mortality, surpassing the cumulative case counts and fatalities from the earlier waves, with high peak values. Overall, relative to the first wave, the burden of severe outcomes was lower in the second and higher in the third wave, but varied by prefecture. In all three waves, severe outcomes peaked after notification counts and test positivity peaked; severe outcomes were also consistently skewed toward the elderly.

conclusionsImportant lessons were learned from each wave and across waves-some aspects remained constant, while others changed over time. In order to rapidly detect an increase in incidence, continuous, timely, and sensitive surveillance-using multiple information sources with careful interpretations-will be key in COVID-19 control.

Indexed as

biasdescriptive epidemiologySARS-CoV-2surveillance

Identifiers

PMID34414313
PMCPMC8355718

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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.