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Spatio-temporal distribution and influencing factors of norovirus outbreaks in Beijing, China from 2016 to 2020
BACKGROUND: Noroviruses are a leading cause of acute gastroenteritis (AGE) worldwide. The geographical characteristics of norovirus outbreaks in Beijing and their influencing factors remain unknown. This study aimed to explore the spatial distributions, geographical characteristics, and influencing...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10152695/ https://www.ncbi.nlm.nih.gov/pubmed/37131193 http://dx.doi.org/10.1186/s12879-023-08243-7 |
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author | Chen, Yanwei Liu, Baiwei Wang, Yu Zhang, Yewu Yan, Hanqiu Li, Weihong Shen, Lingyu Tian, Yi Jia, Lei Zhang, Daitao Yang, Peng Gao, Zhiyong Wang, Quanyi |
author_facet | Chen, Yanwei Liu, Baiwei Wang, Yu Zhang, Yewu Yan, Hanqiu Li, Weihong Shen, Lingyu Tian, Yi Jia, Lei Zhang, Daitao Yang, Peng Gao, Zhiyong Wang, Quanyi |
author_sort | Chen, Yanwei |
collection | PubMed |
description | BACKGROUND: Noroviruses are a leading cause of acute gastroenteritis (AGE) worldwide. The geographical characteristics of norovirus outbreaks in Beijing and their influencing factors remain unknown. This study aimed to explore the spatial distributions, geographical characteristics, and influencing factors of norovirus outbreaks in Beijing, China. METHODS: Epidemiological data and specimens were collected through the AGE outbreak surveillance system in all 16 districts of Beijing. Data on spatial distribution, geographical characteristics, and influencing factors of norovirus outbreaks were analyzed using descriptive statistics methods. We measured spatial, geographical clustering of high- or low-value deviance from random distribution using Z-scores and P-values as statistical significance measures with Global Moran’s I statistics and Getis-Ord Gi in ArcGIS. Linear regression and correlation methods were used to explore influencing factors. RESULTS: Between September 2016 and August 2020, 1,193 norovirus outbreaks were laboratory-confirmed. The number of outbreaks varied seasonally, typically peaking in spring (March to May) or winter (October to December). Outbreaks primarily occurred around central districts at the town level, and spatial autocorrelation was evident in both the entire study period and in individual years. Hotspots of norovirus outbreaks in Beijing were primarily found in contiguous areas between three central districts (Chaoyang, Haidian, Fengtai) and four suburban districts (Changping, Daxing, Fangshan, Tongzhou). The average population numbers, mean number of all schools, and mean number of kindergartens and primary schools for towns in central districts and hotspot areas were higher than those in suburban districts and non-hotspot areas respectively. Additionally, population numbers and densities of kindergartens and primary schools were influencing factors at the town level. CONCLUSIONS: Hotspots of norovirus outbreaks in Beijing were in contiguous areas between central and suburban districts with high populations, and high kindergarten and primary school densities were the likely driving forces. Outbreak surveillance needs to focus on contiguous areas between central and suburban districts with increased monitoring, medical resources, and health education. |
format | Online Article Text |
id | pubmed-10152695 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-101526952023-05-03 Spatio-temporal distribution and influencing factors of norovirus outbreaks in Beijing, China from 2016 to 2020 Chen, Yanwei Liu, Baiwei Wang, Yu Zhang, Yewu Yan, Hanqiu Li, Weihong Shen, Lingyu Tian, Yi Jia, Lei Zhang, Daitao Yang, Peng Gao, Zhiyong Wang, Quanyi BMC Infect Dis Research BACKGROUND: Noroviruses are a leading cause of acute gastroenteritis (AGE) worldwide. The geographical characteristics of norovirus outbreaks in Beijing and their influencing factors remain unknown. This study aimed to explore the spatial distributions, geographical characteristics, and influencing factors of norovirus outbreaks in Beijing, China. METHODS: Epidemiological data and specimens were collected through the AGE outbreak surveillance system in all 16 districts of Beijing. Data on spatial distribution, geographical characteristics, and influencing factors of norovirus outbreaks were analyzed using descriptive statistics methods. We measured spatial, geographical clustering of high- or low-value deviance from random distribution using Z-scores and P-values as statistical significance measures with Global Moran’s I statistics and Getis-Ord Gi in ArcGIS. Linear regression and correlation methods were used to explore influencing factors. RESULTS: Between September 2016 and August 2020, 1,193 norovirus outbreaks were laboratory-confirmed. The number of outbreaks varied seasonally, typically peaking in spring (March to May) or winter (October to December). Outbreaks primarily occurred around central districts at the town level, and spatial autocorrelation was evident in both the entire study period and in individual years. Hotspots of norovirus outbreaks in Beijing were primarily found in contiguous areas between three central districts (Chaoyang, Haidian, Fengtai) and four suburban districts (Changping, Daxing, Fangshan, Tongzhou). The average population numbers, mean number of all schools, and mean number of kindergartens and primary schools for towns in central districts and hotspot areas were higher than those in suburban districts and non-hotspot areas respectively. Additionally, population numbers and densities of kindergartens and primary schools were influencing factors at the town level. CONCLUSIONS: Hotspots of norovirus outbreaks in Beijing were in contiguous areas between central and suburban districts with high populations, and high kindergarten and primary school densities were the likely driving forces. Outbreak surveillance needs to focus on contiguous areas between central and suburban districts with increased monitoring, medical resources, and health education. BioMed Central 2023-05-02 /pmc/articles/PMC10152695/ /pubmed/37131193 http://dx.doi.org/10.1186/s12879-023-08243-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Chen, Yanwei Liu, Baiwei Wang, Yu Zhang, Yewu Yan, Hanqiu Li, Weihong Shen, Lingyu Tian, Yi Jia, Lei Zhang, Daitao Yang, Peng Gao, Zhiyong Wang, Quanyi Spatio-temporal distribution and influencing factors of norovirus outbreaks in Beijing, China from 2016 to 2020 |
title | Spatio-temporal distribution and influencing factors of norovirus outbreaks in Beijing, China from 2016 to 2020 |
title_full | Spatio-temporal distribution and influencing factors of norovirus outbreaks in Beijing, China from 2016 to 2020 |
title_fullStr | Spatio-temporal distribution and influencing factors of norovirus outbreaks in Beijing, China from 2016 to 2020 |
title_full_unstemmed | Spatio-temporal distribution and influencing factors of norovirus outbreaks in Beijing, China from 2016 to 2020 |
title_short | Spatio-temporal distribution and influencing factors of norovirus outbreaks in Beijing, China from 2016 to 2020 |
title_sort | spatio-temporal distribution and influencing factors of norovirus outbreaks in beijing, china from 2016 to 2020 |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10152695/ https://www.ncbi.nlm.nih.gov/pubmed/37131193 http://dx.doi.org/10.1186/s12879-023-08243-7 |
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