Evidence map›Paper›PMID 32401210›Full record

ArticleJournal of medical Internet research2020

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

Chunmei Huang, Xinjie Xu, Yuyang Cai, Qinmin Ge, Guangwang Zeng, Xiaopan Li, Weide Zhang, Chen Ji, Ling Yang

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 1 pooled it
11.9field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

43 citing papers in PubMed, 1 synthesis or guideline pooled it, 88 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 2 countries.

Chunmei Huang *Department of Geriatrics, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.ORCID 0000-0002-9925-8073
Xinjie Xu *Department of Emergency, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.ORCID 0000-0003-0769-6234
Yuyang Cai *School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.ORCID 0000-0003-1500-8518
Qinmin Ge *Department of Emergency, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.ORCID 0000-0003-4378-9606
Guangwang Zeng *Department of Geriatrics, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.ORCID 0000-0001-5243-7341
Xiaopan LiCenter for Disease Control and Prevention, Pudong New Area, Shanghai, China.ORCID 0000-0002-9625-478X
Weide ZhangBig Data and Artificial Intelligence Center, Zhongshan Hospital, Fudan University, Shanghai, China.ORCID 0000-0001-7090-357X
Chen JiWarwick Clinical Trials Unit, Warwick Medical School, Coventry, United Kingdom.ORCID 0000-0003-4919-3299
Ling YangDepartment of Geriatrics, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.ORCID 0000-0001-6626-4541
XinHua Hospital · CNFudan University · CNShanghai Jiao Tong University · CNUniversity of Warwick · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn 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.

objectiveThis study aims to analyze the characteristics of suspected or laboratory-confirmed COVID-19 patients who asked for help on Sina Weibo.

methodsWe 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.

resultsAll 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.

conclusionsOur 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.

Indexed as

BetacoronavirusData MiningSocial MediaAdolescentAdultAgedAge FactorsChildChild, PreschoolChinaCoronavirus InfectionsCOVID-19FemaleFeverHumansInfantcoronavirus diseaseCOVID-19helpSARS-CoV-2Sina Weibosocial media

Identifiers

PMID32401210
PMCPMC7236610
OpenAlexW3025534520

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.