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

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.

Huang C ，Xu X ，Cai Y ，Ge Q ，Zeng G ，Li X ，Zhang W ，Ji C ，Yang L ... -  《JOURNAL OF MEDICAL INTERNET RESEARCH》

Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study.

Li J ，Xu Q ，Cuomo R ，Purushothaman V ，Mackey T ... -  《JMIR Public Health and Surveillance》

Online Health Information Seeking Using "#COVID-19 Patient Seeking Help" on Weibo in Wuhan, China: Descriptive Study.

First detected in Wuhan, China in December 2019, the COVID-19 pandemic stretched the medical system in Wuhan and posed a challenge to the state's risk communication efforts. Timely access to quality health care information during outbreaks of infectious diseases can be effective to curtail the spread of disease and feelings of anxiety. Although existing studies have extended our knowledge about online health information-seeking behavior, processes, and motivations, rarely have the findings been applied to an outbreak. Moreover, there is relatively little recent research on how people in China are using the internet for seeking health information during a pandemic. The aim of this study is to explore how people in China are using the internet for seeking health information during a pandemic. Drawing on previous research of online health information seeking, this study asks the following research questions: how was the "#COVID-19 Patient Seeking Help" hashtag being used by patients in Wuhan seeking health information on Weibo at the peak of the outbreak? and what kinds of health information were patients in Wuhan seeking on Weibo at the peak of the outbreak? Using entity identification and textual analysis on 10,908 posts on Weibo, we identified 1496 patients with COVID-19 using "#COVID-19 Patient Seeking Help" and explored their online health information-seeking behavior. The curve of the hashtag posting provided a dynamic picture of public attention to the COVID-19 pandemic. Many patients faced difficulties accessing offline health care services. In general, our findings confirmed that the internet is used by the Chinese public as an important source of health information. The lockdown policy was found to cut off the patients' social support network, preventing them from seeking help from family members. The ability to seek information and help online, especially for those with young children or older adult members during the pandemic. A high proportion of female users were seeking health information and help for their parents or for older adults at home. The most searched information included accessing medical treatment, managing self-quarantine, and offline to online support. Overall, the findings contribute to our understanding of health information-seeking behaviors during an outbreak and highlight the importance of paying attention to the information needs of vulnerable groups and the role social media may play.

Zhao X ，Fan J ，Basnyat I ，Hu B ... -  《JOURNAL OF MEDICAL INTERNET RESEARCH》

Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2.

Xu X ，Yu C ，Qu J ，Zhang L ，Jiang S ，Huang D ，Chen B ，Zhang Z ，Guan W ，Ling Z ，Jiang R ，Hu T ，Ding Y ，Lin L ，Gan Q ，Luo L ，Tang X ，Liu J ... -  《-》

Using Reports of Symptoms and Diagnoses on Social Media to Predict COVID-19 Case Counts in Mainland China: Observational Infoveillance Study.

Coronavirus disease (COVID-19) has affected more than 200 countries and territories worldwide. This disease poses an extraordinary challenge for public health systems because screening and surveillance capacity is often severely limited, especially during the beginning of the outbreak; this can fuel the outbreak, as many patients can unknowingly infect other people. The aim of this study was to collect and analyze posts related to COVID-19 on Weibo, a popular Twitter-like social media site in China. To our knowledge, this infoveillance study employs the largest, most comprehensive, and most fine-grained social media data to date to predict COVID-19 case counts in mainland China. We built a Weibo user pool of 250 million people, approximately half the entire monthly active Weibo user population. Using a comprehensive list of 167 keywords, we retrieved and analyzed around 15 million COVID-19-related posts from our user pool from November 1, 2019 to March 31, 2020. We developed a machine learning classifier to identify "sick posts," in which users report their own or other people's symptoms and diagnoses related to COVID-19. Using officially reported case counts as the outcome, we then estimated the Granger causality of sick posts and other COVID-19 posts on daily case counts. For a subset of geotagged posts (3.10% of all retrieved posts), we also ran separate predictive models for Hubei province, the epicenter of the initial outbreak, and the rest of mainland China. We found that reports of symptoms and diagnosis of COVID-19 significantly predicted daily case counts up to 14 days ahead of official statistics, whereas other COVID-19 posts did not have similar predictive power. For the subset of geotagged posts, we found that the predictive pattern held true for both Hubei province and the rest of mainland China regardless of the unequal distribution of health care resources and the outbreak timeline. Public social media data can be usefully harnessed to predict infection cases and inform timely responses. Researchers and disease control agencies should pay close attention to the social media infosphere regarding COVID-19. In addition to monitoring overall search and posting activities, leveraging machine learning approaches and theoretical understanding of information sharing behaviors is a promising approach to identify true disease signals and improve the effectiveness of infoveillance.

Shen C ，Chen A ，Luo C ，Zhang J ，Feng B ，Liao W ... -  《JOURNAL OF MEDICAL INTERNET RESEARCH》