COVID-19 Surveiller: toward a robust and effective pandemic surveillance system basedon social media mining.
COVID-19 Surveiller: toward a robust and effective pandemic surveillance system basedon social media mining.
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COVID-19-19S监视者：朝着基于社交媒体挖掘的基于强大而有效的大流行监视系统。
DOI：
10.1098/rsta.2021.0125
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发表时间：
2022-01-10
期刊：
影响因子：
--
通讯作者：
Wang W
中科院分区：
文献类型：
--
作者：
Jiang JY;Zhou Y;Chen X;Jhou YR;Zhao L;Liu S;Yang PC;Ahmar J;Wang W
新型冠状病毒COVID-19的爆发已成为人类历史上最严重的流行病之一。在本文中，我们建议利用社交媒体用户作为社交传感器，同时预测流行趋势，并为公共卫生专家提供潜在的风险因素，以了解传播情况并建议适当的干预措施。更确切地说，我们开发了新的深度学习模型来识别重要的实体及其随时间的关系，从而建立动态异构图来描述社交媒体用户的观察结果。然后，动态图神经网络模型可以预测趋势（例如新诊断的病例和死亡率），并从社交媒体中识别高风险事件。基于所提出的计算方法，我们还开发了一个基于Web的系统，领域专家没有任何计算机科学背景，很容易进行交互。我们在Twitter提供的大规模COVID-19相关推文数据集上进行了广泛的实验，结果表明我们的方法可以精确地预测新发病例和死亡率。我们还展示了我们基于网络的流行病监测系统的稳健性及其检索基本知识和在各种情况下得出准确预测的能力。我们的系统也可以在。本文是主题问题“数据科学方法用于传染病监测”的一部分。
The outbreak of the novel coronavirus, COVID-19, has become one of the most severe pandemics in human history. In this paper, we propose to leverage social media users as social sensors to simultaneously predict the pandemic trends and suggest potential risk factors for public health experts to understand spread situations and recommend proper interventions. More precisely, we develop novel deep learning models to recognize important entities and their relations over time, thereby establishing dynamic heterogeneous graphs to describe the observations of social media users. A dynamic graph neural network model can then forecast the trends (e.g. newly diagnosed cases and death rates) and identify high-risk events from social media. Based on the proposed computational method, we also develop a web-based system for domain experts without any computer science background to easily interact with. We conduct extensive experiments on large-scale datasets of COVID-19 related tweets provided by Twitter, which show that our method can precisely predict the new cases and death rates. We also demonstrate the robustness of our web-based pandemic surveillance system and its ability to retrieve essential knowledge and derive accurate predictions across a variety of circumstances. Our system is also available at . This article is part of the theme issue ‘Data science approachs to infectious disease surveillance’.
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影响因子：
18.2
作者：
Gómez-Bombarelli R;Wei JN;Duvenaud D;Hernández-Lobato JM;Sánchez-Lengeling B;Sheberla D;Aguilera-Iparraguirre J;Hirzel TD;Adams RP;Aspuru-Guzik A
通讯作者：
Aspuru-Guzik A
影响因子：
2.6
作者：
Biswas, M. H. A.;Paiva, L. T.;de Pinho, MdR
通讯作者：
de Pinho, MdR
DOI：
10.1098/rspa.1927.0118
发表时间：
1927-08-01
期刊：
PROCEEDINGS OF THE ROYAL SOCIETY OF LONDON SERIES A-CONTAINING PAPERS OF A MATHEMATICAL AND PHYSICAL CHARACTER
影响因子：
--
作者：
Kermack, WO;McKendrick, AG
通讯作者：
McKendrick, AG
影响因子：
4
作者：
Harko, Tiberiu;Lobo, Francisco S. N.;Mak, M. K.
通讯作者：
Mak, M. K.
影响因子：
11.8
作者：
Fine, Paul;Eames, Ken;Heymann, David L.
通讯作者：
Heymann, David L.