利用社交媒体预测COVID-19病例

C. Comito
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引用次数: 1

摘要

社交媒体已经成为传播新闻、讨论对世界事件的看法和评论的关键工具,在公共卫生管理方面也发挥着相关作用,特别是在季节性流感等流行病监测方面。在线社交媒体实际上可以在监测疾病传播方面提供重要帮助,因为用户会自我报告他们的健康问题。自2019冠状病毒病爆发的第一天起,人们就交换了有关大流行的新闻、最新情况、情绪和意见。这篇论文描述了一项旨在评估推文与官方COVID-19数据相关性的研究。基于相关研究结果,本文提出了预测日新增病例数的预测模型。该方法是将推文和官方新冠肺炎数据相结合的自回归模型。使用真实tweets数据集进行相关性研究,并评估预测模型的性能。结果显示了该方法的可行性,突出了将推文整合到预测模型中所获得的改进,可以提前预测新的COVID-19病例,平均在确诊前4-6天。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sensing Social Media to Forecast COVID-19 Cases
Social media has become a key tool for spreading the news, discussing ideas and comments on world events, playing a relevant role also in public health management, especially in epidemics surveillance like seasonal flu. Online social media actually can provide an important help in monitoring disease spreading as users self-report their health-related issues. Since the very first days of COVID-19 outbreak, people exchanged news, updates, sentiment and opinion about the pandemics. The paper describes a study aiming at evaluating the correlation of tweets with official COVID-19 data. Based on the outcomes of the correlation study, the paper proposes a forecasting model to predict the number of new daily COVID-19 cases. The approach is formulated as an autoregressive model that combines tweets and official COVID-19 data. A real-word dataset of tweets is used for the correlation study and to evaluate the performance of the forecasting model. Results shown the feasibility of the approach, highlighting the improvement obtained when tweets are integrated in the forecasting model, allowing to predict new COVID-19 cases in advance, on average 4–6 days before they were confirmed.
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