背景:冠状病毒病(COVID-19)已影响全球200多个国家和地区。这种疾病对公共卫生系统构成了极大的挑战,因为筛查和监测能力往往受到严重限制,特别是在爆发开始的时候;这可能会助长爆发,因为许多患者会在不知不觉中感染其他人。
目的:本研究的目的是收集和分析微博上与COVID-19相关的帖子,在中国流行的类似Twitter的社交媒体网站。据我们所知,这项信息监控研究采用了最大的,最全面的,以及迄今为止最精细的社交媒体数据来预测中国大陆的COVID-19病例数。
方法:我们建立了一个拥有2.5亿人口的微博用户池,大约是整个每月活跃微博用户人口的一半。使用167个关键字的综合列表,从2019年11月1日至2020年3月31日,我们从用户池中检索并分析了约1500万条与COVID-19相关的帖子。我们开发了一个机器学习分类器来识别“生病的帖子,其中用户报告自己或其他人与COVID-19相关的症状和诊断。使用官方报告的病例数作为结果,然后,我们根据每日病例数估计了病员和其他COVID-19病员的Granger因果关系。对于带有地理标记的帖子的子集(占所有检索帖子的3.10%),我们还运行了湖北省的独立预测模型,最初爆发的震中,以及中国大陆其他地区。
结果:我们发现,关于COVID-19症状和诊断的报告显着预测了每日病例数,比官方统计数据提前了14天,而其他COVID-19帖子没有相似的预测能力。对于带有地理标记的帖子的子集,我们发现,无论医疗资源的不平等分配和爆发时间,湖北省和中国大陆其他地区的预测模式都成立。
结论:可以有效地利用公共社交媒体数据来预测感染病例并及时做出反应。研究人员和疾病控制机构应密切关注有关COVID-19的社交媒体信息领域。除了监控整体搜索和发布活动之外,利用机器学习方法和对信息共享行为的理论理解是识别真实疾病信号并提高信息监测有效性的一种有前途的方法。
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.