About: Mining the Characteristics of COVID-19 Patients in China: Analysis of Social Media Posts

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Mining the Characteristics of COVID-19 Patients in China: Analysis of Social Media Posts
Creator	Huang, Chunmei Yang, Ling Eysenbach, Gunther Benis, Arriel Cai, Yuyang Ji, Chen Li, Xiaopan Zeng, Guangwang Ge, Qinmin Mueller, Julia Surian, Didi Xu, Xinjie Zhang, Weide
source	Medline; PMC
abstract	BACKGROUND: In December 2019, pneumonia cases of unknown origin were reported in Wuhan City, Hubei Province, China. Identified as the coronavirus disease (COVID-19), the number of cases grew rapidly by human-to-human transmission in Wuhan. Social media, especially Sina Weibo (a major Chinese microblogging social media site), has become an important platform for the public to obtain information and seek help. OBJECTIVE: This study aims to analyze the characteristics of suspected or laboratory-confirmed COVID-19 patients who asked for help on Sina Weibo. METHODS: We conducted data mining on Sina Weibo and extracted the data of 485 patients who presented with clinical symptoms and imaging descriptions of suspected or laboratory-confirmed cases of COVID-19. In total, 9878 posts seeking help on Sina Weibo from February 3 to 20, 2020 were analyzed. We used a descriptive research methodology to describe the distribution and other epidemiological characteristics of patients with suspected or laboratory-confirmed SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection. The distance between patients’ home and the nearest designated hospital was calculated using the geographic information system ArcGIS. RESULTS: All patients included in this study who sought help on Sina Weibo lived in Wuhan, with a median age of 63.0 years (IQR 55.0-71.0). Fever (408/485, 84.12%) was the most common symptom. Ground-glass opacity (237/314, 75.48%) was the most common pattern on chest computed tomography; 39.67% (167/421) of families had suspected and/or laboratory-confirmed family members; 36.58% (154/421) of families had 1 or 2 suspected and/or laboratory-confirmed members; and 70.52% (232/329) of patients needed to rely on their relatives for help. The median time from illness onset to real-time reverse transcription-polymerase chain reaction (RT-PCR) testing was 8 days (IQR 5.0-10.0), and the median time from illness onset to online help was 10 days (IQR 6.0-12.0). Of 481 patients, 32.22% (n=155) lived more than 3 kilometers away from the nearest designated hospital. CONCLUSIONS: Our findings show that patients seeking help on Sina Weibo lived in Wuhan and most were elderly. Most patients had fever symptoms, and ground-glass opacities were noted in chest computed tomography. The onset of the disease was characterized by family clustering and most families lived far from the designated hospital. Therefore, we recommend the following: (1) the most stringent centralized medical observation measures should be taken to avoid transmission in family clusters; and (2) social media can help these patients get early attention during Wuhan’s lockdown. These findings can help the government and the health department identify high-risk patients and accelerate emergency responses following public demands for help.
has issue date	2020-05-17(xsd:dateTime)
bibo:doi	10.2196/19087
bibo:pmid	32401210
has license	cc-by
schema:url	https://doi.org/10.2196/19087
resource representing a document's title	Mining the Characteristics of COVID-19 Patients in China: Analysis of Social Media Posts
has PubMed Central identifier	PMC7236610
has PubMed identifier	32401210
schema:publication	J Med Internet Res
resource representing a document's body	covid:PMC7236610#body_text
is schema:about of	named entity 'COVID-19' named entity 'Social Media' named entity 'China' named entity 'Social Media' named entity 'Wuhan' named entity 'SARS-CoV-2' named entity 'chest CT' named entity 'health care' named entity 'Sina Weibo' named entity 'fever' named entity 'Declaration of Helsinki' named entity 'epidemic' named entity 'People's Daily' named entity 'Twitter' named entity 'clinical symptoms' named entity 'cough' named entity 'Sina Weibo' named entity 'Sina Weibo' named entity 'MERS-CoV' named entity 'health department' named entity 'China' named entity 'developed economy' named entity 'lockdown' named entity 'research methodology' named entity 'IBM' named entity 'share information' named entity 'reverse transcription-polymerase chain reaction' named entity 'Wuhan' named entity 'SARS-CoV-2' named entity 'Sina Weibo' named entity 'demographic' named entity 'chest CT' named entity 'Sina Weibo' named entity 'public transportation' named entity 'infection' named entity 'ArcGIS' named entity 'Python Software Foundation' named entity 'Wuhan' named entity 'China' named entity 'chronic obstructive pulmonary disease' named entity 'China' named entity 'home isolation' named entity 'RT-PCR' named entity 'sputum' named entity 'public transportation' named entity 'high throughput sequencing' named entity 'SARS-CoV-2' named entity 'high-risk' named entity 'COVID' named entity 'clinical data' named entity 'ground-glass opacity' named entity 'clinical symptoms' named entity 'RT-PCR' named entity 'COVID' named entity 'application programming interface' named entity 'diarrhea' named entity 'COVID-19' named entity 'ground-glass opacity' named entity 'January 1' named entity 'public health emergencies' named entity 'SARS-CoV-2' named entity 'Hanyang' named entity 'treat patients' named entity 'nausea' named entity 'RT-PCR' named entity 'RT-PCR' named entity 'Dengue' named entity 'Wuchang' named entity 'severe acute respiratory syndrome coronavirus 2' named entity 'Wuhan lockdown'

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