Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sydney, NSW, Australia.
School of Public Health and Community Medicine, University of New South Wales (UNSW), Sydney, NSW, Australia.
PLoS One. 2020 Mar 17;15(3):e0230322. doi: 10.1371/journal.pone.0230322. eCollection 2020.
First reported in March 2014, an Ebola epidemic impacted West Africa, most notably Liberia, Guinea and Sierra Leone. We demonstrate the value of social media for automated surveillance of infectious diseases such as the West Africa Ebola epidemic. We experiment with two variations of an existing surveillance architecture: the first aggregates tweets related to different symptoms together, while the second considers tweets about each symptom separately and then aggregates the set of alerts generated by the architecture. Using a dataset of tweets posted from the affected region from 2011 to 2014, we obtain alerts in December 2013, which is three months prior to the official announcement of the epidemic. Among the two variations, the second, which produces a restricted but useful set of alerts, can potentially be applied to other infectious disease surveillance and alert systems.
2014 年 3 月首次报告了埃博拉疫情，疫情影响了西非，尤其是利比里亚、几内亚和塞拉利昂。我们展示了社交媒体在自动监测传染病（如西非埃博拉疫情）方面的价值。我们尝试了现有监测架构的两种变体：第一种变体将与不同症状相关的推文聚合在一起，而第二种变体则分别考虑关于每个症状的推文，然后聚合架构生成的警报集。使用 2011 年至 2014 年期间从受影响地区发布的推文数据集，我们在 2013 年 12 月获得了警报，这比疫情的官方宣布提前了三个月。在这两种变体中，第二种变体产生了一组受限但有用的警报，可能适用于其他传染病监测和警报系统。
