Evidence map›Paper›PMID 39464731›Full record

ArticleVirusdisease2024

Sewage surveillance of SARS-CoV-2 in Tokyo (May 2020-June 2022): method using high-throughput, fully-automated platform.

Ryota Kumagai, Mamiyo Kawakami, Fumi Kasuya, Kinji Yamada, Akihiro Takahashi, Kouki Murayama, Kenshi Morita, Tomoya Kaneyasu, Naoko Tamai, Michiya Hasegawa and 6 more

Abstract read
In one paragraph

Article in Virusdisease, 2024. 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

16 authors.

Ryota KumagaiDepartment of Microbiology, Tokyo Metropolitan Institute of Public Health, 3-24-1 Hyakunin-Cho, Shinjuku-Ku, Tokyo, 169-0073 Japan.ORCID 0009-0003-2367-0672
Mamiyo KawakamiDepartment of Microbiology, Tokyo Metropolitan Institute of Public Health, 3-24-1 Hyakunin-Cho, Shinjuku-Ku, Tokyo, 169-0073 Japan.
Fumi KasuyaDepartment of Microbiology, Tokyo Metropolitan Institute of Public Health, 3-24-1 Hyakunin-Cho, Shinjuku-Ku, Tokyo, 169-0073 Japan.
Kinji YamadaBureau of Sewerage, Tokyo Metropolitan Government, 2-8-1, Nishi-Shinjuku, Shinjuku-Ku, Tokyo, 163-8001 Japan.
Akihiro TakahashiBureau of Sewerage, Tokyo Metropolitan Government, 2-8-1, Nishi-Shinjuku, Shinjuku-Ku, Tokyo, 163-8001 Japan.
Kouki MurayamaBureau of Sewerage, Tokyo Metropolitan Government, 2-8-1, Nishi-Shinjuku, Shinjuku-Ku, Tokyo, 163-8001 Japan.
Kenshi MoritaBureau of Sewerage, Tokyo Metropolitan Government, 2-8-1, Nishi-Shinjuku, Shinjuku-Ku, Tokyo, 163-8001 Japan.
Tomoya KaneyasuBureau of Waterworks, Tokyo Metropolitan Government, 2-8-1, Nishi-Shinjuku, Shinjuku-Ku, Tokyo, 163-8001 Japan.
Naoko TamaiBureau of Waterworks, Tokyo Metropolitan Government, 2-8-1, Nishi-Shinjuku, Shinjuku-Ku, Tokyo, 163-8001 Japan.
Michiya HasegawaDepartment of Microbiology, Tokyo Metropolitan Institute of Public Health, 3-24-1 Hyakunin-Cho, Shinjuku-Ku, Tokyo, 169-0073 Japan.
Sachiko HaradaDepartment of Microbiology, Tokyo Metropolitan Institute of Public Health, 3-24-1 Hyakunin-Cho, Shinjuku-Ku, Tokyo, 169-0073 Japan.
Arisa AmanoDepartment of Microbiology, Tokyo Metropolitan Institute of Public Health, 3-24-1 Hyakunin-Cho, Shinjuku-Ku, Tokyo, 169-0073 Japan.
Takushi FujiwaraDepartment of Microbiology, Tokyo Metropolitan Institute of Public Health, 3-24-1 Hyakunin-Cho, Shinjuku-Ku, Tokyo, 169-0073 Japan.
Hirofumi MiyakeDepartment of Microbiology, Tokyo Metropolitan Institute of Public Health, 3-24-1 Hyakunin-Cho, Shinjuku-Ku, Tokyo, 169-0073 Japan.
Mami NagashimaDepartment of Microbiology, Tokyo Metropolitan Institute of Public Health, 3-24-1 Hyakunin-Cho, Shinjuku-Ku, Tokyo, 169-0073 Japan.
Kenji SadamasuDepartment of Microbiology, Tokyo Metropolitan Institute of Public Health, 3-24-1 Hyakunin-Cho, Shinjuku-Ku, Tokyo, 169-0073 Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is shed in saliva, sputum, and feces of the novel coronavirus disease 2019 (COVID-19) patients, and SARS-CoV-2 RNA has been detected in sewage. Thus, many regions worldwide have implemented the measurement of SARS-CoV-2 RNA in sewage as a monitoring tool for COVID-19 prevalence. Here, we report the monitoring of two wastewater treatment plants in Tokyo from May 2020 to June 2022 using a fully automated genetic testing method for the solid fraction of sewage. We found that SARS-CoV-2 sewage monitoring using the Panther System with the Aptima SARS-CoV-2 Assay Kit is an effective method of qualitative monitoring. Furthermore, spike gene mutations (N501Y, L452R, E484A) by real-time PCR using test residues were detected in some samples, which reflected the epidemic status. Nevertheless, our findings demonstrate that the use of a fully automated testing platform is effective for SARS-CoV-2 RNA detection in the solid fraction of sewage.

Indexed as

COVID-19SARS-CoV-2Sewage surveillanceTokyo

Identifiers

PMID39464731
PMCPMC11502634

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.