Evidence map›Paper›PMID 41146340›Full record

ArticleBMC research notes2025

Time-series datasets of meteorological variables and influenza incidence in Kawasaki City, Japan.

Keita Wagatsuma

Abstract read
In one paragraph

Article in BMC research notes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

1 author.

Keita WagatsumaDivision of International Health (Public Health), Graduate School of Medical and Dental Sciences, Niigata University, 1-757 Asahimachi dori, Chuo-ku, Niigata, 951-8510, Niigata, Japan. waga@med.niigata-u.ac.jp.

Funding

Japan Society for the Promotion of Science 24K23680
6 · The paper itself

Abstract

objectivesAlthough a wealth of studies has explored the impact of individual meteorological variables on influenza transmission, investigations of these effects within key subgroups-such as viral type, sex, and age-remain scarce. Moreover, long-term daily time-series influenza surveillance data are exceptionally limited. To address these shortcomings, we herein present a meticulously curated daily time-series dataset spanning approximately 11 years from Kawasaki City, Japan, to facilitate comprehensive analyses of the short-term association between meteorological variables and influenza incidence. This resource also aims to aid researchers, educators, and students by fostering the application of time-series regression modelling in both research and educational contexts. DATA DESCRIPTION: Daily total number of influenza cases and ambient meteorological data-mean, minimum, and maximum temperature (°C); relative humidity (%); wind speed (m s⁻¹); rainfall (mm); sunshine duration (hours); and vapour pressure (hPa)-were collated for Kawasaki City, Japan (March 2014-April 2025). Influenza cases were stratified by virus type (A/B), sex (male/female), and age (0-5, 6-11 months; 1-9 years in single-year bands; 10-14, 15-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, ≥ 80 years).

Indexed as

Influenza, HumanMeteorological ConceptsWeatherAdolescentAdultAgedChildChild, PreschoolCitiesFemaleHumansIncidenceInfantInfant, NewbornJapanMaleClimate changeDistributed lag non-linear modelEnvironmental epidemiologyEnvironmental healthInfluenzaMeteorological variablesShort-term effectTime-series regression

Identifiers

PMID41146340
PMCPMC12560287

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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.