Cargando…

A cross-sectional study of the epidemic situation on COVID-19 in Gansu Province, China – a big data analysis of the national health information platform

BACKGROUND: In December 2019, a pneumonia caused by SARS-CoV-2 emerged in Wuhan, China and has rapidly spread around the world since then. This study is to explore the patient characteristics and transmission chains of COVID-19 in the population of Gansu province, and support decision-making. METHOD...

Descripción completa

Detalles Bibliográficos
Autores principales: Yan, Xuanchen, Wang, Jianjian, Yao, Jingwen, Estill, Janne, Wu, Shouyuan, Lu, Jie, Liang, Baoping, Li, Hongmin, Tao, Shengxin, Bai, Huanli, Liu, Hongliang, Chen, Yaolong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7863032/
https://www.ncbi.nlm.nih.gov/pubmed/33546618
http://dx.doi.org/10.1186/s12879-020-05743-8
_version_ 1783647420159098880
author Yan, Xuanchen
Wang, Jianjian
Yao, Jingwen
Estill, Janne
Wu, Shouyuan
Lu, Jie
Liang, Baoping
Li, Hongmin
Tao, Shengxin
Bai, Huanli
Liu, Hongliang
Chen, Yaolong
author_facet Yan, Xuanchen
Wang, Jianjian
Yao, Jingwen
Estill, Janne
Wu, Shouyuan
Lu, Jie
Liang, Baoping
Li, Hongmin
Tao, Shengxin
Bai, Huanli
Liu, Hongliang
Chen, Yaolong
author_sort Yan, Xuanchen
collection PubMed
description BACKGROUND: In December 2019, a pneumonia caused by SARS-CoV-2 emerged in Wuhan, China and has rapidly spread around the world since then. This study is to explore the patient characteristics and transmission chains of COVID-19 in the population of Gansu province, and support decision-making. METHODS: We collected data from Gansu Province National Health Information Platform. A cross-sectional study was conducted, including patients with COVID-19 confirmed between January 23 and February 6, 2020, and analyzed the gender and age of the patients. We also described the incubation period, consultation time and sources of infection in the cases, and calculated the secondary cases that occurred within Gansu for each imported case. RESULTS: We found thirty-six (53.7%) of the patients were women and thirty-one (46.3%) men, and the median ages were 40 (IQR 31–53) years. Twenty-eight (41.8%) of the 67 cases had a history of direct exposure in Wuhan. Twenty-five (52.2%) cases came from ten families, and we found no clear reports of modes of transmission other than family clusters. The largest number of secondary cases linked to a single source was nine. CONCLUSION: More women than men were diagnosed with COVID-19 in Gansu Province. Although the age range of confirmed cases of COVID-19 in Gansu Province covered almost all age groups, most patients with confirmed COVID-19 tend to be middle aged persons. The most common suspected mode of transmission was through family cluster. Gansu and other settings worldwide should continue to strengthen the utilization of big data in epidemic control.
format Online
Article
Text
id pubmed-7863032
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-78630322021-02-05 A cross-sectional study of the epidemic situation on COVID-19 in Gansu Province, China – a big data analysis of the national health information platform Yan, Xuanchen Wang, Jianjian Yao, Jingwen Estill, Janne Wu, Shouyuan Lu, Jie Liang, Baoping Li, Hongmin Tao, Shengxin Bai, Huanli Liu, Hongliang Chen, Yaolong BMC Infect Dis Research Article BACKGROUND: In December 2019, a pneumonia caused by SARS-CoV-2 emerged in Wuhan, China and has rapidly spread around the world since then. This study is to explore the patient characteristics and transmission chains of COVID-19 in the population of Gansu province, and support decision-making. METHODS: We collected data from Gansu Province National Health Information Platform. A cross-sectional study was conducted, including patients with COVID-19 confirmed between January 23 and February 6, 2020, and analyzed the gender and age of the patients. We also described the incubation period, consultation time and sources of infection in the cases, and calculated the secondary cases that occurred within Gansu for each imported case. RESULTS: We found thirty-six (53.7%) of the patients were women and thirty-one (46.3%) men, and the median ages were 40 (IQR 31–53) years. Twenty-eight (41.8%) of the 67 cases had a history of direct exposure in Wuhan. Twenty-five (52.2%) cases came from ten families, and we found no clear reports of modes of transmission other than family clusters. The largest number of secondary cases linked to a single source was nine. CONCLUSION: More women than men were diagnosed with COVID-19 in Gansu Province. Although the age range of confirmed cases of COVID-19 in Gansu Province covered almost all age groups, most patients with confirmed COVID-19 tend to be middle aged persons. The most common suspected mode of transmission was through family cluster. Gansu and other settings worldwide should continue to strengthen the utilization of big data in epidemic control. BioMed Central 2021-02-05 /pmc/articles/PMC7863032/ /pubmed/33546618 http://dx.doi.org/10.1186/s12879-020-05743-8 Text en © The Author(s) 2021 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/. The Creative Commons Public Domain Dedication waiver (http://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 Article
Yan, Xuanchen
Wang, Jianjian
Yao, Jingwen
Estill, Janne
Wu, Shouyuan
Lu, Jie
Liang, Baoping
Li, Hongmin
Tao, Shengxin
Bai, Huanli
Liu, Hongliang
Chen, Yaolong
A cross-sectional study of the epidemic situation on COVID-19 in Gansu Province, China – a big data analysis of the national health information platform
title A cross-sectional study of the epidemic situation on COVID-19 in Gansu Province, China – a big data analysis of the national health information platform
title_full A cross-sectional study of the epidemic situation on COVID-19 in Gansu Province, China – a big data analysis of the national health information platform
title_fullStr A cross-sectional study of the epidemic situation on COVID-19 in Gansu Province, China – a big data analysis of the national health information platform
title_full_unstemmed A cross-sectional study of the epidemic situation on COVID-19 in Gansu Province, China – a big data analysis of the national health information platform
title_short A cross-sectional study of the epidemic situation on COVID-19 in Gansu Province, China – a big data analysis of the national health information platform
title_sort cross-sectional study of the epidemic situation on covid-19 in gansu province, china – a big data analysis of the national health information platform
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7863032/
https://www.ncbi.nlm.nih.gov/pubmed/33546618
http://dx.doi.org/10.1186/s12879-020-05743-8
work_keys_str_mv AT yanxuanchen acrosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT wangjianjian acrosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT yaojingwen acrosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT estilljanne acrosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT wushouyuan acrosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT lujie acrosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT liangbaoping acrosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT lihongmin acrosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT taoshengxin acrosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT baihuanli acrosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT liuhongliang acrosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT chenyaolong acrosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT acrosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT yanxuanchen crosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT wangjianjian crosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT yaojingwen crosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT estilljanne crosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT wushouyuan crosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT lujie crosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT liangbaoping crosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT lihongmin crosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT taoshengxin crosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT baihuanli crosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT liuhongliang crosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT chenyaolong crosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform
AT crosssectionalstudyoftheepidemicsituationoncovid19ingansuprovincechinaabigdataanalysisofthenationalhealthinformationplatform