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Exploration of influenza incidence prediction model based on meteorological factors in Lanzhou, China, 2014–2017
Humans are susceptible to influenza. The influenza virus spreads quickly and behave seasonally. The seasonality and spread of influenza are often associated with meteorological factors and have spatio-temporal differences. Based on the influenza cases and daily average meteorological factors in Lanz...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9754291/ https://www.ncbi.nlm.nih.gov/pubmed/36520836 http://dx.doi.org/10.1371/journal.pone.0277045 |
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author | Du, Meixia Zhu, Hai Yin, Xiaochun Ke, Ting Gu, Yonge Li, Sheng Li, Yongjun Zheng, Guisen |
author_facet | Du, Meixia Zhu, Hai Yin, Xiaochun Ke, Ting Gu, Yonge Li, Sheng Li, Yongjun Zheng, Guisen |
author_sort | Du, Meixia |
collection | PubMed |
description | Humans are susceptible to influenza. The influenza virus spreads quickly and behave seasonally. The seasonality and spread of influenza are often associated with meteorological factors and have spatio-temporal differences. Based on the influenza cases and daily average meteorological factors in Lanzhou from 2014 to 2017, this study firstly aimed to analyze the characteristics of influenza incidence in Lanzhou and the impact of meteorological factors on influenza activities. Then, SARIMA(X) models for the prediction were established. The influenza cases in Lanzhou from 2014 to 2017 was more male than female, and the younger the age, the higher the susceptibility; the epidemic characteristics showed that there is a peak in winter, a secondary peak in spring, and a trough in summer and autumn. The influenza cases in Lanzhou increased with increasing daily pressure, decreasing precipitation, average relative humidity, hours of sunshine, average daily temperature and average daily wind speed. Low temperature was a significant driving factor for the increase of transmission intensity of seasonal influenza. The SARIMAX (1,0,0)(1,0,1)[12] multivariable model with average temperature has better prediction performance than the university model. This model is helpful to establish an early warning system, and provide important evidence for the development of influenza control policies and public health interventions. |
format | Online Article Text |
id | pubmed-9754291 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-97542912022-12-16 Exploration of influenza incidence prediction model based on meteorological factors in Lanzhou, China, 2014–2017 Du, Meixia Zhu, Hai Yin, Xiaochun Ke, Ting Gu, Yonge Li, Sheng Li, Yongjun Zheng, Guisen PLoS One Research Article Humans are susceptible to influenza. The influenza virus spreads quickly and behave seasonally. The seasonality and spread of influenza are often associated with meteorological factors and have spatio-temporal differences. Based on the influenza cases and daily average meteorological factors in Lanzhou from 2014 to 2017, this study firstly aimed to analyze the characteristics of influenza incidence in Lanzhou and the impact of meteorological factors on influenza activities. Then, SARIMA(X) models for the prediction were established. The influenza cases in Lanzhou from 2014 to 2017 was more male than female, and the younger the age, the higher the susceptibility; the epidemic characteristics showed that there is a peak in winter, a secondary peak in spring, and a trough in summer and autumn. The influenza cases in Lanzhou increased with increasing daily pressure, decreasing precipitation, average relative humidity, hours of sunshine, average daily temperature and average daily wind speed. Low temperature was a significant driving factor for the increase of transmission intensity of seasonal influenza. The SARIMAX (1,0,0)(1,0,1)[12] multivariable model with average temperature has better prediction performance than the university model. This model is helpful to establish an early warning system, and provide important evidence for the development of influenza control policies and public health interventions. Public Library of Science 2022-12-15 /pmc/articles/PMC9754291/ /pubmed/36520836 http://dx.doi.org/10.1371/journal.pone.0277045 Text en © 2022 Du et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Du, Meixia Zhu, Hai Yin, Xiaochun Ke, Ting Gu, Yonge Li, Sheng Li, Yongjun Zheng, Guisen Exploration of influenza incidence prediction model based on meteorological factors in Lanzhou, China, 2014–2017 |
title | Exploration of influenza incidence prediction model based on meteorological factors in Lanzhou, China, 2014–2017 |
title_full | Exploration of influenza incidence prediction model based on meteorological factors in Lanzhou, China, 2014–2017 |
title_fullStr | Exploration of influenza incidence prediction model based on meteorological factors in Lanzhou, China, 2014–2017 |
title_full_unstemmed | Exploration of influenza incidence prediction model based on meteorological factors in Lanzhou, China, 2014–2017 |
title_short | Exploration of influenza incidence prediction model based on meteorological factors in Lanzhou, China, 2014–2017 |
title_sort | exploration of influenza incidence prediction model based on meteorological factors in lanzhou, china, 2014–2017 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9754291/ https://www.ncbi.nlm.nih.gov/pubmed/36520836 http://dx.doi.org/10.1371/journal.pone.0277045 |
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