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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.
This study aims to analyze the characteristics of suspected or laboratory-confirmed COVID-19 patients who asked for help on Sina Weibo.
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.
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.
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.
In December 2019, pneumonia cases of unknown origin were reported in Wuhan City, Hubei Province, China. The illness was identified and officially named as coronavirus disease 2019 (COVID-19), which is caused by a novel viral strain called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [
Since the COVID-19 outbreak, social media, especially Sina Weibo, has become an important platform for the public to obtain epidemic-related information quickly and effectively. According to the official outbreak data released by Sina Weibo on February 26, 2020, 51.2 million users cumulatively posted 350 million pieces of epidemic-related content. Online readership of epidemic-related topics reached 754.5 billion. Sina Weibo established a communication channel that allowed the government to effectively listen and respond to public opinion quickly. Here, by collecting data from Sina Weibo from February 3 to 20, 2020, we aim to analyze the characteristics of suspected or laboratory-confirmed patients with the SARS-CoV-2 infection.
In this study, we describe the characteristics of suspected or laboratory-confirmed patients with the SARS-CoV-2 infection, the distribution of patients throughout Wuhan, and the relationship between helpers (eg, relative, friend, spouse, sibling) and patients. Social media was used to obtain timely access to public demand so that the government and the health department could identify high-risk patients and take measures to help these patients.
Sina Weibo launched a platform to provide online help channels for patients infected with SARS-CoV-2. From February 3 to 20, 2020, we obtained 9878 posts by using the keyword 肺炎患者求助 (COVID-19 pneumonia patients seeking help) from Sina Weibo through its application programming interface (API). Python (Python Software Foundation) was used to implement a rule-based screening and classification method on the PyCharm platform. We used the collected posts as a training set, including related posts and unrelated posts. Based on the post-for-help rules formulated by Sina Weibo, we considered the post text, as well as keywords pertaining to name, age, home address, time of illness, and description of illness as a related post; otherwise, it was deemed an irrelevant post. We excluded 6922 irrelevant posts that only described opinions and feelings about help seeking related to COVID-19 and initially collected 2956 related posts that contained mentions of clinical symptoms and/or imaging descriptions. Then, we manually screened out and excluded posts. We excluded 1679 reposted posts, 556 posts with a significant amount of missing valid clinical data, 195 nonpneumonia patient posts, and 41 patient posts with non-Wuhan home addresses. Finally, we selected 485 patient posts that presented clinical symptoms and imaging descriptions (
We collected clinical symptoms, chest computed tomography (CT) findings (the chest CT was only summarized for those who provided a clinical report), days from illness onset to online help, days from illness onset to RT-PCR testing, RT-PCR test results, the relationship between helpers and patients, and home address details from Sina Weibo’s records. We performed a study on the clinical characteristics of suspected or laboratory-confirmed patients with the SARS-CoV-2 infection seeking help on Sina Weibo. Suspected cases were identified as having fever or respiratory symptoms such as shortness of breath, cough, productive sputum, or chest pain. A laboratory-confirmed case with SARS-CoV-2 infection was defined as a positive result to high throughput sequencing or real-time reverse transcription-polymerase chain reaction (RT-PCR) assay of throat swabs and sputum [
We also used a descriptive research methodology to analyze the distribution of patients throughout Wuhan and the relationship between helpers and patients. The distance from patients' home to the nearest designated hospital was calculated using the geographic information system ArcGIS. The data used in the current study is publicly accessible on Sina Weibo and readers can obtain the raw data online [
An example of a patient with coronavirus disease (COVID-19) seeking help on Sina Weibo. RT-PCR: reverse transcription-polymerase chain reaction.
Study flow diagram. COVID-19: coronavirus disease.
Continuous variables were expressed as median (IQR) when appropriate. Categorical variables were summarized as counts and percentages in each category. Analysis was conducted using SPSS, version 19.0 (IBM). We used ArcGIS, version 10.2.2, to plot the numbers of patients seeking help on a map.
We selected 485 patients with suspected or laboratory-confirmed SARS-CoV-2 infection with at least clinical symptoms and imaging descriptions from Sina Weibo. The demographic and clinical characteristics were shown in
The 485 patients came from 421 families, and 39.67% (167/421) of these families had at least one family member with a laboratory-confirmed and/or suspected diagnosis of SARS-CoV-2; 11.40% (48/421) of families had one laboratory-confirmed family member only. Families with one confirmed case accounted for 9.50% (40/421); two, three, and four confirmed members accounted for 1.19% (5/421), 0.48% (2/421), and 0.24% (1/421), respectively. A suspected diagnosis occurred in 30.64% (129/421) of families; a family with one suspected member accounted for 21.14% (89/421), two suspected members accounted for 7.60% (32/421), three suspected members accounted for 1.43% (6/421), and four suspected members accounted for 0.48% (2/421) (
Clinical characteristics of suspected or laboratory-confirmed patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (N=485).
Characteristic | Value | |
Age (years), median (IQR) | 63.0 (55.0-71.0) | |
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|
|
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0-14 years | 1 (0.21) |
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15-49 years | 74 (15.74) |
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50-64 years | 178 (37.87) |
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≥65 years | 217 (46.17) |
Sex (female; n=485), n (%) | 243 (50.10) | |
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|
|
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Fever (temperature ≥37.3℃) | 408 (84.12) |
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Cough | 190 (39.18) |
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Fatigue | 224 (46.19) |
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Shortness of breath | 261 (53.81) |
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Nausea or vomiting | 81 (16.70) |
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Diarrhea | 61 (12.58) |
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Any | 112 (23.09) |
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Chronic obstructive pulmonary disease | 10 (2.06) |
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Diabetes | 43 (8.87) |
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Hypertension | 55 (11.34) |
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Coronary heart disease | 38 (7.84) |
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Cerebrovascular diseases | 12 (2.47) |
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Cancera | 7 (1.44) |
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Chronic renal diseases | 7 (1.44) |
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Immunodeficiency | 2 (0.41) |
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Hepatitis B infectionb | 3 (0.62) |
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||
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Ground-glass opacity | 237 (75.48) |
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Local patchy shadowing | 20 (6.37) |
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Bilateral patchy shadowing | 191 (60.83) |
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Interstitial abnormalities | 6 (1.91) |
Days from illness onset to online help, median (range) | 10 (6-12) | |
Days from illness onset to RT-PCRd testing, median (range) | 8 (5-10) | |
|
253 (52.16) | |
|
Positive | 173 (68.38) |
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No result | 48 (18.97) |
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Suspect | 5 (1.98) |
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Negative | 2 (10.67) |
aCancer referred to any malignancy.
bHepatitis B infection denotes a positive test for hepatitis B surface antigen, with or without elevated alanine or aspartate aminotransferase levels.
cCT: computed tomography.
dRT-PCR: reverse transcription-polymerase chain reaction.
The distribution of family clusters.
All patients were located in Wuhan, but more patients lived in the central districts (Hongshan, Jiang'an, Wuchang, Hanyang, and Qiaokou) compared to outskirt districts (
The distribution of patients throughout Wuhan.
The distance between patients and the nearest designated hospital, as well as the relationship between helpers and patients.
Variable | Count, n (%) | |
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≤1km | 123 (25.57) |
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1-2 km | 119 (24.74) |
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2-3 km | 84 (17.46) |
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≥3km | 155 (32.22) |
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Relative | 232 (70.52) |
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Friend | 38 (11.55) |
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Patient themselves | 34 (10.33) |
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Spouse | 14 (4.26) |
|
Sibling | 11 (3.34) |
The distance between patients and their nearest designated hospital.
We explored the relationship between helpers and patients. During data collection, 156 helpers stated that they were family members of patients, but they did not specify their relationship. The remaining 70.52% (232/329) of helpers were the patients' relatives; 11.55% (38/329) were friends, 4.26% (14/329) were their spouses, 3.34% (11/329) were siblings, and 10.33% (34/329) were the patients themselves (
This study has shown that patients seeking help on Sina Weibo lived in Wuhan and most of them were elderly. Our statistical analysis of the age of patients seeking help on Sina Weibo demonstrated that patients on Sina Weibo were older—the proportion of patients who were ≥65 years was as high as 46.17%. Zhong et al [
Additionally, 23.09% of patients had at least one underlying disorder. Fever was the dominant symptom whereas gastrointestinal symptoms were rare. Ground-glass opacity was the most common pattern on chest CT. Among all laboratory-confirmed COVID-19 patients, the most common pattern on chest CT were ground-glass opacity (56.4%) [
Person-to-person transmission of COVID-19 in hospital and family settings has been increasing [
Our research also found that the number of patients in the Wuchang, Jiang'an, Qiaokou, Hongshan, and Hanyang districts was greater than in other districts.
In total, 32.22% (155/481) of patients lived more than 3 kilometers away from their nearest designated hospital. According to Baidu maps, adults can walk 4 kilometers in 1 hour. Considering that the patients in this study were older and their health condition may have slowed them down even more, we estimate that patients could walk 3 kilometers in a 1-hour period. Hence, this indicates that a patient would need to walk more than 1 hour to see a doctor since public transportation was suspended at the time. This may be one of the reasons why patients wanted to be admitted to a hospital. In addition, on February 5th, the Wuhan municipal health commission designated 28 hospitals for the treatment of laboratory-confirmed patients with the SARS-CoV-2 infection. The empty bed rate of hospitals within the city was only 3.6%. Thus, patients could not be hospitalized for the various reasons above. This also reflected an insufficiency of medical resources during the initial outbreak [
We also explored the relationship between helpers and patients. Judging from the content of Sina Weibo posts asking for help, “Mom” and “Dad” were high-frequency words; 70.52% (232/329) of helpers were the patients' relatives, indicating that the publishers of the help information were mostly the children of the elderly. Unfamiliarity with new technology may have hindered elderly people from seeking assistance from the outside world.
With the rapid and effective dissemination of help information, since February 5th, the People's Daily has launched an all-media operation to provide online help channels for patients with the SARS-CoV-2 infection. The government implemented a policy to maximize hospital admissions, which led to a rapid decrease in the number of people seeking help on Sina Weibo on February 6th and remained at low levels since February 8th, indicating that the needs of the public had been met. This also means that it is important to establish new and effective communication mechanisms for the dissemination of important factual information in a timely manner. Through this epidemic, we can see that medical resources are insufficiently allocated. There are substantial regional disparities in health care resource availability and accessibility in China [
Our study has some limitations. First, given that our data was collected from a social media platform, the description of patients’ symptoms and laboratory information were likely to be incomplete. Second, the urgent timeline for data extraction and the subjective judgment of the collectors might undermine the data quality to a certain extent. Finally, we learned that most of these patients have been admitted to the hospital with government help and many patients remain in the hospital, so we did not compare the 28-day rate for the composite endpoint.
In summary, our study found that the distance between patients and hospitals and the closure of public transportation further increased the difficulty of hospitalization for the elderly. We recommend the application of centralized medical observations to avoid the spread of COVID-19 through family clusters. In a public health emergency, making full use of available social media platforms can establish effective, factual communication channels and shorten admission times, helping patients get early attention during the Wuhan lockdown. These findings can help the government and health departments pay attention to the elderly population during the outbreak and accelerate emergency responses following public demands for help.
application programming interface
coronavirus disease
computed tomography
Middle East respiratory syndrome
reverse transcription-polymerase chain reaction
severe acute respiratory syndrome coronavirus
severe acute respiratory syndrome coronavirus 2
This study was supported by the Zhejiang University Special Scientific Research Fund for COVID-19 prevention and control (2020XGZX065); the National Natural Science Foundation of China (71432006); the National Social Science Fund of China (grant number 17BSH056); and the Shanghai Jiao Tong University think tank research project (ZKYJ-20200114). The funders had no role in the study design, data collection, data analysis and interpretation, writing of the report, or the decision to submit the paper for publication. The Authors were also supported by the Shanghai Jiaotong University special scientific research fund for "Technology Promotion Project" in 2019 (ZT201903) and the Shanghai science and Technology Fund (18441905200).
CH, XX, YC, QG, and GZ are joint first authors. LY, QG, and YC are joint co-correspondence authors. LY and YC obtained funding. CH, XX, PL, YC, QG, and LY participated in study design; CH, XX, WZ, and GZ collected the data; CH, XX, GZ, WZ, and PL performed data analysis; CH and JC drafted the manuscript; CH, XX, PL, YC, QG, and LY were responsible for study conception. All authors provided critical review of the manuscript and approved the final draft for publication. QG, YC, and LY contributed to the interpretation of the results and critical revision of the manuscript for important intellectual content and approved the final version of the manuscript. All authors have read and approved the final manuscript.
None declared.