RAPID: ENSURING INTEGRITY OF COVID-19 DATA AND NEWS ACROSS REGIONS

正在从各种来源生成和收集大量流行病学数据，以了解COVID-19的影响和传播。同样，编写和传播了大量关于大流行病的新闻文章，以使民众了解情况。个人、企业和政府所采取行动的适当性通常取决于数据和新闻的质量。因此，确保数据和新闻的质量非常重要。然而，恶意行为者可以改变数据记录的属性，插入虚假记录，或抑制记录，导致任何分析不充分，并传播错误信息。该项目解决了定义和识别有关COVID-19的虚假数据和新闻以及跟踪错误信息来源的关键问题。该项目的新颖之处在于开发了一种方法和相关工具集，该方法和工具集适应并结合了机器学习技术来检测虚假数据和错误信息，并以一种易于最终用户理解和解释的方式呈现结果。该方法检测COVID-19数据中的差异，并将标记的差异追溯到数据源。将从新闻来源获得的结果与从医疗数据分析获得的结果进行比较，以确定新闻质量与在任何地区进行的数据操纵的程度和类型之间的相关性。该项目的影响是显著提高对COVID-19进行准确科学分析以及发现和解释新闻操纵的能力。在项目中制定的科学原则预计将在医学领域之外有用。项目负责人和参与该项目的学生都是少数民族。该项目将在BRAID附属机构科罗拉多州立大学计算机科学系进行。COVID-19数据差异与(1)单个记录有关，其中一些字段被修改；(2)记录随时间的顺序形成时间维度，其中插入了虚假记录或记录被抑制；(3)跨区域的记录序列形成空间维度，其中存在跨区域操纵或信息披露的模式。该方法确定了自动编码器、长短期记忆（LSTM）、时间卷积网络（TCNs）和卷积神经网络（cnn）的适当组合，这些网络可以处理从医学来源和包含空间和时间维度的新闻中获得的数据。这些工具帮助科罗拉多大学安舒茨医学中心和疾病控制与预防中心的研究人员的合作者对医疗记录进行数据完整性检查，并提供违反完整性的解释。这些工具还可以处理与COVID-19有关的不同类型的数据和新闻更改。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Large amounts of epidemiological data are being generated and collected from a variety of sources to understand the impact and propagation of COVID-19. Similarly, huge amounts of news articles are generated and disseminated about the pandemic to keep the population informed. The appropriateness of the actions taken by individuals, corporations, and governments are often based on the quality of data and news. Thus, ensuring the quality of data and news is important. However, malicious actors can alter the attributes of data records, insert spurious records, or suppress records causing any analysis to be inadequate and misinformation to be propagated. This project addresses the critical problem of defining and identifying spurious data and news concerning COVID-19, and tracking the source of misinformation. The project novelty lies in the development of an approach and associated toolset that adapts and combines Machine Learning technologies to detect spurious data and misinformation and presents the results in a manner that is easy for end users to understand and interpret. The approach detects discrepancies in COVID-19 data and traces the flagged discrepancies back to the data sources. The results obtained from the news sources and those obtained from the medical data analysis are compared to determine correlations between the quality of news and the degree and type of data manipulation performed at any region. The project’s impacts are on significantly enhancing the ability to perform accurate scientific analysis, and detecting and explaining news manipulation with respect to COVID-19. The scientific principles developed in the project are expected to be useful outside the medical domain. The PI and the students identified for this project are minorities. The project will be carried out in the Computer Science Department at Colorado State University which is a BRAID affiliate.COVID-19 data discrepancies are related to (1) single records, where some field is modified, (2) sequence of records over time forming a temporal dimension, where spurious records have been inserted or records have been suppressed, and (3) sequences of records across regions forming a spatial dimension, where there is a pattern of manipulation or information disclosure across regions. The approach determines the appropriate combination of autoencoders, Long Short-Term Memory (LSTM), Temporal Convolution Network (TCNs), and Convolution Neural Networks (CNNs) that can work with data obtained from medical sources and news containing both spatial and temporal dimensions. The tools help the investigators’ collaborators at the University of Colorado Anschutz Medical Center and Center for Disease Control and Prevention to perform data integrity checking of medical records and to provide explanations of integrity violations. The tools also handle different types of data and news alterations pertaining to COVID-19.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.