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

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About: Using Reports of Symptoms and Diagnoses on Social Media to Predict COVID-19 Case Counts in Mainland China: Observational Infoveillance Study Goto Sponge NotDistinct Permalink

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Using Reports of Symptoms and Diagnoses on Social Media to Predict COVID-19 Case Counts in Mainland China: Observational Infoveillance Study
Creator	Feng, Bo Liang, Hai Zhang, Jingwen Eysenbach, Gunther Lau, Eric Luo, Chen Liao, Wang Basch, Corey Chen, Anfan Fu, King-wa Shen, Cuihua
source	ArXiv; Medline; PMC; WHO
abstract	BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
has issue date	2020-05-28(xsd:dateTime)
bibo:doi	10.2196/19421
bibo:pmid	32452804
has license	cc-by
schema:url	https://doi.org/10.2196/19421
resource representing a document's title	Using Reports of Symptoms and Diagnoses on Social Media to Predict COVID-19 Case Counts in Mainland China: Observational Infoveillance Study
has PubMed Central identifier	PMC7257484
has PubMed identifier	32452804
schema:publication	J Med Internet Res
resource representing a document's body	covid:PMC7257484#body_text
is schema:about of	named entity 'REPORTS' named entity 'STUDY' named entity 'Study' named entity 'Infoveillance' named entity 'Mainland China' named entity 'Hubei' named entity 'Hubei' named entity 'COVID-19' named entity 'medical care' named entity 'social media surveillance' named entity 'disease surveillance' named entity 'COVID-19' named entity 'social media surveillance' named entity 'COVID-19' named entity 'standard deviation' named entity 'Weibo' named entity 'emerging disease' named entity 'influenza' named entity 'China CDC' named entity 'COVID-19' named entity 'linear model' named entity 'diagnostic criteria' named entity 'ingroup' named entity 'National Health Commission' named entity 'social media' named entity 'symptom' named entity 'Hubei' named entity 'China' named entity 'Wuhan' named entity 'COVID-19' named entity 'mainland China' named entity 'Ordinary least squares' named entity 'digital surveillance' named entity 'social media surveillance' named entity 'COVID-19' named entity 'machine learning algorithms' named entity 'mainland China' named entity 'COVID-19' named entity 'supervised machine learning' named entity 'Hubei province' named entity 'ingroup' named entity 'Weibo' named entity 'geotagged' named entity 'Google Flu Trends' named entity '1 million' named entity 'COVID-19' named entity 'COVID-19' named entity 'infoveillance' named entity 'social media' named entity 'COVID-19' named entity 'social media' named entity 'Weibo' named entity 'COVID-19' named entity 'infoveillance' named entity 'infoveillance' named entity 'data based' named entity 'COVID-19' named entity 'Weibo' named entity 'disease surveillance' named entity 'COVID-19' named entity 'COVID-19' named entity 'Weibo' named entity 'remdesivir' named entity 'medical conditions' named entity 'COVID-19' named entity 'COVID-19' named entity 'China CDC' named entity 'social media' named entity 'social media' named entity 'confidence level' named entity 'social media' named entity 'disease surveillance'

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