EPI-AI: Automated Understanding and Alerting of Disease Outbreaks from Global News Media

寨卡、埃博拉和SARS等疾病的爆发对国际社会和英国/加拿大政府至关重要。公共卫生组织需要在疫情爆发时尽早获得数据，以迅速做出反应并防止人类遭受痛苦。传统的生物监测依赖于人类实验室网络，但这些数据往往无法实时获得，地理覆盖范围不完整，并针对特定疾病。使用基于网络的新闻数据的数字疾病监测（DDS）克服了其中的一些限制，为传统网络提供了重要的补充。然而，目前的新闻发布系统在很大程度上依赖于人工筛选网络数据，寻找感兴趣的事件：鉴于新闻来源的数量、多种语文、速度和潜在的偏见，这是一个需要技能和劳力密集的过程。自21世纪初以来，自然语言处理（NLP）一直在使用，以有效地检测和跟踪疫情新闻报道中的健康威胁。例如，加拿大的GPHIN系统发现了SARS的第一个证据，它使用NLP和人类专家的组合，每天筛选超过20，000条9种语言的在线新闻报道。然而，传统的自动化方法对可以帮助专家解释风险因素的上下文不敏感，并且没有考虑可能的数据偏差。我们在EPI-AI项目中的目标是实现实时自动DDS的逐步变化。以前的工作往往采取孤立的方法，专注于自然语言处理方法或空间分析，很少考虑数据中的偏见所引起的平等考虑。我们将采用跨学科方法，结合计算机科学、流行病学和生物伦理学三个学科的专业知识，开发适应全球流行病监测复杂数据和目标的新型机器学习和统计模型。我们看到的好处包括：（一）提高地理精确度和覆盖面;（二）提高理解报告专题重点的能力;（三）将风险因素自动标准化为标准术语，以整合各系统的证据;（四）对报告进行自动时空分析，以更新全球风险图并触发警报;（五）提供关于潜在媒体偏见的背景信息，以支持对警报的解释。这项跨学科的研究将与加拿大，英国和全球公共卫生利益相关者密切合作。

Disease outbreaks, such as Zika, Ebola and SARS epidemics, are of the greatest importance to the international community and the UK/Canadian governments. Public health organisations need data as early as possible in an outbreak to respond rapidly and prevent human suffering. Traditional bio-surveillance relies on human laboratory networks, but these data are often unavailable in real-time, patchy in geographic coverage, and tuned to specific diseases. Digital disease surveillance (DDS) using Web-based news data overcomes some of these limitations, providing a critical supplement to traditional networks. However, current DDS systems rely to a large extent on manual screening of Web data for events of interest: a skilled and labour-intensive process given the volume, multilingualism, velocity and potential bias of news sources.Research has shown that there is significant potential to automate DDS. Natural Language Processing (NLP) has been in use since the early 2000s to efficiently detect and track health threats from outbreak news reports. For example, the Canadian GPHIN system, which detected the first evidence of SARS, uses a combination of NLP and human experts to sift through over 20K online news reports each day in nine languages. However, traditional automated approaches are insensitive to context that can help experts to interpret risk factors and fail to take account of possible data biases. Our goal in the EPI-AI project is to achieve a step-change in real-time automated DDS. Previous work has tended to take a siloed approach, focusing on Natural Language Processing methods or spatial analysis with little consideration of equality considerations that arise from biases in the data. We will use an interdisciplinary approach, combining expertise from three disciplines - computer science, epidemiology, and bioethics - to develop novel machine learning and statistical models adapted to the complex data and objectives of global epidemic surveillance. Benefits that we see include: (i) improved geographic precision and coverage; (ii) improved ability to understand the topical focus of a report; (iii) automated normalisation of risk factors to a standard terminology for integration of evidence across systems; (iv) automated spatio-temporal analysis of reports to update global risk maps and trigger alerts; and (v) provision of contextual information on potential media bias to support interpretation of alerts. This fundamentally interdisciplinary research will be closely aligned with key Canadian, UK and global public health stakeholders.