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Association Between Seasonal Influenza and Absolute Humidity: Time-Series Analysis with Daily Surveillance Data in Japan
Seasonal influenza epidemics are associated with various meteorological factors. Recently absolute humidity (AH) has garnered attention, and some epidemiological studies show an association between AH and human influenza infection. However, they mainly analyzed weekly surveillance data, and daily da...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7211015/ https://www.ncbi.nlm.nih.gov/pubmed/32385282 http://dx.doi.org/10.1038/s41598-020-63712-2 |
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author | Shimmei, Keita Nakamura, Takahiro Ng, Chris Fook Sheng Hashizume, Masahiro Murakami, Yoshitaka Maruyama, Aya Misaki, Takako Okabe, Nobuhiko Nishiwaki, Yuji |
author_facet | Shimmei, Keita Nakamura, Takahiro Ng, Chris Fook Sheng Hashizume, Masahiro Murakami, Yoshitaka Maruyama, Aya Misaki, Takako Okabe, Nobuhiko Nishiwaki, Yuji |
author_sort | Shimmei, Keita |
collection | PubMed |
description | Seasonal influenza epidemics are associated with various meteorological factors. Recently absolute humidity (AH) has garnered attention, and some epidemiological studies show an association between AH and human influenza infection. However, they mainly analyzed weekly surveillance data, and daily data remains largely unexplored despite its potential benefits. In this study, we analyze daily influenza surveillance data using a distributed lag non-linear model to examine the association of AH with the number of influenza cases and the magnitude of the association. Additionally, we investigate how adjustment for seasonality and autocorrelation in the model affect results. All models used in the study showed a significant increase in the number of influenza cases as AH decreased, although the magnitude of the association differed substantially by model. Furthermore, we found that relative risk reached a peak at lag 10–14 with extremely low AH. To verify these findings, further analysis should be conducted using data from other locations. |
format | Online Article Text |
id | pubmed-7211015 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-72110152020-05-19 Association Between Seasonal Influenza and Absolute Humidity: Time-Series Analysis with Daily Surveillance Data in Japan Shimmei, Keita Nakamura, Takahiro Ng, Chris Fook Sheng Hashizume, Masahiro Murakami, Yoshitaka Maruyama, Aya Misaki, Takako Okabe, Nobuhiko Nishiwaki, Yuji Sci Rep Article Seasonal influenza epidemics are associated with various meteorological factors. Recently absolute humidity (AH) has garnered attention, and some epidemiological studies show an association between AH and human influenza infection. However, they mainly analyzed weekly surveillance data, and daily data remains largely unexplored despite its potential benefits. In this study, we analyze daily influenza surveillance data using a distributed lag non-linear model to examine the association of AH with the number of influenza cases and the magnitude of the association. Additionally, we investigate how adjustment for seasonality and autocorrelation in the model affect results. All models used in the study showed a significant increase in the number of influenza cases as AH decreased, although the magnitude of the association differed substantially by model. Furthermore, we found that relative risk reached a peak at lag 10–14 with extremely low AH. To verify these findings, further analysis should be conducted using data from other locations. Nature Publishing Group UK 2020-05-08 /pmc/articles/PMC7211015/ /pubmed/32385282 http://dx.doi.org/10.1038/s41598-020-63712-2 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Shimmei, Keita Nakamura, Takahiro Ng, Chris Fook Sheng Hashizume, Masahiro Murakami, Yoshitaka Maruyama, Aya Misaki, Takako Okabe, Nobuhiko Nishiwaki, Yuji Association Between Seasonal Influenza and Absolute Humidity: Time-Series Analysis with Daily Surveillance Data in Japan |
title | Association Between Seasonal Influenza and Absolute Humidity: Time-Series Analysis with Daily Surveillance Data in Japan |
title_full | Association Between Seasonal Influenza and Absolute Humidity: Time-Series Analysis with Daily Surveillance Data in Japan |
title_fullStr | Association Between Seasonal Influenza and Absolute Humidity: Time-Series Analysis with Daily Surveillance Data in Japan |
title_full_unstemmed | Association Between Seasonal Influenza and Absolute Humidity: Time-Series Analysis with Daily Surveillance Data in Japan |
title_short | Association Between Seasonal Influenza and Absolute Humidity: Time-Series Analysis with Daily Surveillance Data in Japan |
title_sort | association between seasonal influenza and absolute humidity: time-series analysis with daily surveillance data in japan |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7211015/ https://www.ncbi.nlm.nih.gov/pubmed/32385282 http://dx.doi.org/10.1038/s41598-020-63712-2 |
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