Comprehensive surveillance systems are the key to provide accurate data for effective modeling. Traditional symptom-based case surveillance has been joined with recent genomic, serologic, and environment surveillance to provide more integrated disease surveillance systems. A major gap in comprehensive disease surveillance is to accurately monitor potential population behavioral changes in real-time. Population-wide behaviors such as compliance with various interventions and vaccination acceptance significantly influence and drive the overall epidemic dynamics in the society. Original infoveillance utilizes online query data (e.g., Google and Wikipedia search of a specific content topic such as an epidemic) and later focuses on large volumes of online discourse data about the from social media platforms and further augments epidemic modeling. It mainly uses number of posts to approximate public awareness of the disease, and further compares with observed epidemic dynamics for better projection. The current COVID-19 pandemic shows that there is an urgency to further harness the rich, detailed content and sentiment information, which can provide more accurate and granular information on public awareness and perceptions toward multiple aspects of the disease, especially various interventions. In this perspective paper, we describe a novel conceptual analytical framework of content and sentiment infoveillance (CSI) and integration with epidemic modeling. This CSI framework includes data retrieval and pre-processing; information extraction via natural language processing to identify and quantify detailed time, location, content, and sentiment information; and integrating infoveillance with common epidemic modeling techniques of both mechanistic and data-driven methods. CSI complements and significantly enhances current epidemic models for more informed decision by integrating behavioral aspects from detailed, instantaneous infoveillance from massive social media data.

中文翻译：

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内容和情绪监控 (CSI)：现代流行病建模的重要组成部分

综合监测系统是为有效建模提供准确数据的关键。传统的基于症状的病例监测已与最近的基因组、血清学和环境监测相结合，以提供更综合的疾病监测系统。全面疾病监测的一个主要差距是实时准确监测潜在的人群行为变化。诸如遵守各种干预措施和接受疫苗接种等全民行为显着影响和推动社会的整体流行病动态。原始信息监控利用在线查询数据（例如，谷歌和维基百科搜索特定内容主题，如流行病），后来关注来自社交媒体平台的大量在线话语数据，并进一步增强流行病建模。它主要通过发帖数来估计公众对该疾病的认知程度，并进一步与观察到的流行动态进行比较以进行更好的预测。当前的 COVID-19 大流行表明，迫切需要进一步利用丰富、详细的内容和情绪信息，这些信息可以提供有关公众对该疾病的多个方面，尤其是各种干预措施的认识和看法的更准确、更详细的信息。在这篇前瞻性论文中，我们描述了一种新颖的内容和情感信息监控 (CSI) 概念分析框架，并与流行病建模相结合。这个CSI框架包括数据检索和预处理；信息提取 当前的 COVID-19 大流行表明，迫切需要进一步利用丰富、详细的内容和情绪信息，这些信息可以提供有关公众对该疾病的多个方面，尤其是各种干预措施的认识和看法的更准确、更详细的信息。在这篇前瞻性论文中，我们描述了一种新颖的内容和情感信息监控 (CSI) 概念分析框架，并与流行病建模相结合。这个CSI框架包括数据检索和预处理；信息提取 当前的 COVID-19 大流行表明，迫切需要进一步利用丰富、详细的内容和情绪信息，这些信息可以提供有关公众对该疾病的多个方面，尤其是各种干预措施的认识和看法的更准确、更详细的信息。在这篇前瞻性论文中，我们描述了一种新颖的内容和情感信息监控 (CSI) 概念分析框架，并与流行病建模相结合。这个CSI框架包括数据检索和预处理；信息提取 在这篇前瞻性论文中，我们描述了一种新颖的内容和情感信息监控 (CSI) 概念分析框架，并与流行病建模相结合。这个CSI框架包括数据检索和预处理；信息提取 在这篇前瞻性论文中，我们描述了一种新颖的内容和情感信息监控 (CSI) 概念分析框架，并与流行病建模相结合。这个CSI框架包括数据检索和预处理；信息提取通过自然语言处理识别和量化详细的时间、地点、内容和情感信息；将信息监控与机械和数据驱动方法的常见流行病建模技术相结合。CSI 通过整合来自大量社交媒体数据的详细、即时信息监控的行为方面，补充并显着增强了当前的流行病模型，以做出更明智的决策。