tetano
Editor, Senior Moderator
BMC Res Notes
. 2025 Oct 27;18(1):451.
doi: 10.1186/s13104-025-07522-7. Time-series datasets of meteorological variables and influenza incidence in Kawasaki City, Japan
Keita Wagatsuma[SUP] 1 2 [/SUP]
Affiliations
Objectives: Although 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).
Keywords: Climate change; Distributed lag non-linear model; Environmental epidemiology; Environmental health; Influenza; Meteorological variables; Short-term effect; Time-series regression.
. 2025 Oct 27;18(1):451.
doi: 10.1186/s13104-025-07522-7. Time-series datasets of meteorological variables and influenza incidence in Kawasaki City, Japan
Keita Wagatsuma[SUP] 1 2 [/SUP]
Affiliations
- PMID: 41146340
- DOI: 10.1186/s13104-025-07522-7
Objectives: Although 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).
Keywords: Climate change; Distributed lag non-linear model; Environmental epidemiology; Environmental health; Influenza; Meteorological variables; Short-term effect; Time-series regression.