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感染起源和在推特上传播的新研究
Disease Propagation in Social Networks: A Novel Study of Infection Genesis and Spread on Twitter |
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| 课程网址: |
https://videolectures.net/videos/kdd2016_shah_infection_genesis
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| 主讲教师: |
Manan Shah
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| 开课单位: |
KDD 2016研讨会
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| 开课时间: |
2016-10-12
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| 课程语种: |
英语
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| 中文简介: |
美国疾病控制与预防中心每年诊断数百万例传染病，观察到的疾病曲线在12月中旬左右达到峰值，在8月和9月趋于平稳。虽然这提供了对疾病传播的准确描述，但其汇编时间太长，无法进行最新监测。生成实时疾病分布的能力对于识别疫情和促进当局与医疗保健提供者之间的即时沟通非常重要。我们试图使用推特来描述疾病传播的特征，扩展了谷歌2008年流感趋势项目。我们的新颖贡献是开发了一种基于管道的模型，该模型结合了自然语言处理和机器学习。通过我们的方法获得的推特疾病分布与美国疾病控制与预防中心数据之间的相关系数为0.98。我们的模型进一步确定了在疾病预防控制中心分布中不流行的疾病爆发，例如2014年底的腮腺炎爆发，大型医院样本未能识别。我们还开发了一种基于微分方程的疾病模拟（称为SEIR），以进一步验证我们的推特疾病分布模型。我们的模型有可能通过使用不断增长的社交媒体领域实时识别疾病爆发，为创建早期感染系统提供极大的帮助，这对社会来说是一种独特而强大的利益。
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| 课程简介: |
The CDC diagnoses millions of cases of infectious diseases annually with observed disease curves peaking around mid-December and lulling in August and September. While this provides an accurate depiction of disease spread, its compilation takes too long for up-to-date monitoring. The ability to generate real-time disease distributions is important in identifying outbreaks and facilitating instant communication between authorities and health-care providers. We have attempted to characterize disease propagation using Twitter, expanding upon Google’s 2008 Flu Trends project. Our novel contribution is the development of a pipeline based model incorporating natural language processing and machine learning. The correlation coefficient between the Twitter disease distribution obtained via our approach and CDC data was 0.98. Our model further identified disease outbreaks that were not prevalent in the CDC distribution such as the parotitis outbreak in late 2014 that large hospital samples failed to identify. We additionally develop a differential equation based disease simulation (known as SEIR) in order to further validate our Twitter disease distribution model. Our model has the potential to greatly assist in the creation of an early-warning infection system by identifying disease outbreaks in real-time using the ever-growing social media sphere, representing a unique and powerful benefit to society.
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| 关 键 词: |
社交网络; 疾病传播; 感染起源
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| 课程来源: |
视频讲座网
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| 数据采集: |
2024-12-30：liyq
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| 最后编审: |
2024-12-30：liyq
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| 阅读次数: |
8
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