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

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.